{"id":"4175fb16-2452-4502-befa-07c068f50ee6","entityType":"agent","slug":"clawhub-dgr-ai-labs-dgr","name":"Decision-Grade Reasoning (DGR)","canonicalUrl":"https://www.xpersona.co/agent/clawhub-dgr-ai-labs-dgr","canonicalPath":"/agent/clawhub-dgr-ai-labs-dgr","generatedAt":"2026-10-09T19:17:41.120Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T05:19:17.742Z","emptyReason":null},"description":"Audit-ready decision artifacts for LLM outputs - assumptions, risks, recommendation, and review gating (schema-valid JSON). Replaced with https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate) Skill: Decision-Grade Reasoning (DGR) Owner: dgr-ai-labs Summary: Audit-ready decision artifacts for LLM outputs - assumptions, risks, recommendation, and review gating (schema-valid JSON). 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Replaced with https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate)\n\nTags: latest:1.1.1\n\nVersion history:\n\nv1.1.1 | 2026-09-24T19:04:55.087Z | user\n\nNo changes in this version.\n\n- Version 1.1.1: No code or documentation updates detected.\n\nv1.1.0 | 2026-09-24T18:59:44.774Z | auto\n\n**1.1.0 — This version marks DGR as a superseded reference skill; enforcement now handled by DGR Gate.**\n\n- Clearly states the skill is kept for provenance and no longer enforces governance; enforcement has moved to DGR Gate.\n- Updates description and guidance to clarify the difference between prompt requests and tool-enforceable gates.\n- Removes usage instructions, retaining only reference documentation and rationale for the change.\n- Redefines role as a source of structured reasoning records; existing installs continue to function but no further development is planned.\n- Removes redundant file (skill-card.md).\n\nv1.0.4 | 2026-02-04T02:00:39.426Z | user\n\n- Removed redundant CLAWHUB_SUMMARY.md; summary is now provided in the SKILL.md front-matter.\n\nv1.0.3 | 2026-02-04T01:53:37.685Z | user\n\n**1.0.3**\n\n- Tightened the front-matter description for clarity and improved user conversion.\n- Added a \"reasoning\" category to metadata.\n- Compressed and clarified the identity/version/modes block for faster scanning.\n\nv1.0.2 | 2026-02-04T01:43:12.558Z | user\n\n**1.0.2 — Add ClawHub front-matter metadata for improved discovery and presentation.**\n- Added ClawHub metadata fields: emoji (\"🧭\") and homepage link.\n- No changes to skill logic or usage.\n\nv1.0.1 | 2026-02-04T00:02:06.821Z | user\n\n- Added CLAWHUB_SUMMARY.md as a new file.\n- Revised the \"What this skill does\" and \"How to use\" sections for clarity and brevity in SKILL.md.\n- Consolidated and simplified usage instructions for easier onboarding.\n\nv1.0.0 | 2026-02-03T23:17:12.784Z | auto\n\nInitial public release of Decision‑Grade Reasoning (DGR) skill:\n\n- Introduces a machine‑validated, auditable protocol for reasoning and governance.\n- Provides structured output including decision context, explicit assumptions, risks, rationale, and consistency checks.\n- Supports three operating modes for varying speed, detail, and scrutiny.\n- Enables traceability and structured review for high-stakes or review‑required decisions.\n- Includes safety boundaries to improve process quality and reduce risk.\n\nArchive index:\n\nArchive v1.1.1: 9 files, 11539 bytes\n\nFiles: examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), skill-card.md (2042b), SKILL.md (4015b), _meta.json (122b)\n\nFile v1.1.1:SKILL.md\n\n---\nname: dgr\nversion: \"1.1.0\"\ndescription: Superseded reference skill: structured reasoning records, kept for provenance. Enforcement moved to DGR Gate, a pre-execution gate for OpenClaw tool calls: clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  openclaw:\n    emoji: \"🧭\"\n  category: \"reasoning\"\n---\n\n> **Superseded — kept as a reference.** This skill formats reasoning into a structured record. It cannot stop an action; the model decides whether to follow it.\n>\n> **A rule in the prompt is a request. A rule in the tool is a gate.** The enforcing successor is [DGR Gate](https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate), a pre-execution gate that checks a call against operator-configured policy before it runs. It currently gates two demo tools, not your real ones.\n>\n> This page remains published as the record of the earlier approach and why it was replaced. Existing installs keep working; no further development.\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr · **Version:** 1.1.0 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON\n\n## What this skill does\nThis skill produced a structured decision record: decision context, explicit assumptions, identified risks, a recommendation with rationale, and a consistency check. The output was documentation of reasoning, formatted as schema-valid JSON for easier review and storage.\n\nThat was insufficient. A model's own justification is not evidence — a better-formatted rationale from the same model does not change that. A prompt-level instruction can be lost during context compaction or ignored after the model reads untrusted text. This approach documented reasoning but could not enforce constraints on tool execution.\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Changelog\n**1.1.0** — Repositioned as a superseded reference. Terminology aligned with DGR Gate (pre-execution gate). Removed usage guidance; this skill formats reasoning and does not enforce. Existing installs unaffected.\n\n**1.0.4** — Remove redundant CLAWHUB_SUMMARY.md; summary now sourced from SKILL.md front-matter.\n\n**1.0.3** — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.\n\n**1.0.2** — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.\n\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.1.1:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.1.1\",\n  \"publishedAt\": 1790276695087\n}\n\nFile v1.1.1:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.1.1:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.1.1:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.1.1:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.1.1:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.1.1:skill-card.md\n\n## Description:\n\nThis superseded reference skill produces structured JSON decision records with assumptions, risks, recommendations, and consistency checks for human review.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dgr-ai-labs](https://clawhub.ai/user/dgr-ai-labs)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and decision reviewers use this reference skill to document decision context, assumptions, risks, and recommendations in a consistent format for human review. It does not enforce actions or establish that a recommendation is correct.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: A structured rationale can be mistaken for verified evidence or a correct decision.\n\nMitigation: Validate important assumptions and sources independently, and require qualified human review before acting on high-stakes recommendations.\n\nRisk: The skill documents decisions but cannot prevent an agent from taking an unsafe action.\n\nMitigation: Do not treat the record as an enforcement mechanism; apply separate controls before consequential actions.\n\n## Reference(s):\n\n- [DGR skill release](https://clawhub.ai/dgr-ai-labs/skills/dgr)\n- [DGR Gate successor](https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate)\n- [Decision record schema](artifact/schema.json)\n- [Decision record field guide](artifact/field_guide.md)\n\n## Skill Output:\n\n**Output Type(s):** [Structured decision records]\n\n**Output Format:** [JSON]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes assumptions, risks, a recommendation, and a consistency check; intended for human review, not enforcement.]\n\n## Skill Version(s):\n\n1.1.1 (source: ClawHub release evidence; bundled SKILL.md states 1.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.1.1:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n          \"minLength\": 8\n        },\n        \"query_summary\": {\n          \"type\": \"string\",\n          \"maxLength\": 500\n        }\n      }\n    },\n    \"clarifications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"question\",\n          \"why_needed\",\n          \"blocking\"\n        ],\n        \"properties\": {\n          \"question\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"why_needed\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"blocking\": {\n            \"type\": \"boolean\"\n          }\n        }\n      },\n      \"default\": []\n    },\n    \"decomposition\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      },\n      \"default\": []\n    },\n    \"assumptions\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"statement\",\n          \"impact_if_wrong\"\n        ],\n        \"properties\": {\n          \"statement\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"impact_if_wrong\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"risks\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"risk\",\n          \"severity\",\n          \"mitigation\"\n        ],\n        \"properties\": {\n          \"risk\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"severity\": {\n            \"type\": \"string\",\n            \"enum\": [\n              \"low\",\n              \"medium\",\n              \"high\"\n            ]\n          },\n          \"mitigation\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"recommendation\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"action\",\n        \"rationale\",\n        \"confidence\",\n        \"review_required\",\n        \"next_steps\"\n      ],\n      \"properties\": {\n        \"action\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"rationale\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"confidence\": {\n          \"type\": \"number\",\n          \"minimum\": 0,\n          \"maximum\": 1\n        },\n        \"review_required\": {\n          \"type\": \"boolean\"\n        },\n        \"next_steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"minItems\": 0\n        }\n      }\n    },\n    \"consistency_check\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"checks\",\n        \"passed\",\n        \"notes\"\n      ],\n      \"properties\": {\n        \"checks\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          }\n        },\n        \"passed\": {\n          \"type\": \"boolean\"\n        },\n        \"notes\": {\n          \"type\": \"string\"\n        }\n      }\n    }\n  }\n}\n\nArchive v1.1.0: 9 files, 11553 bytes\n\nFiles: examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), skill-card.md (2067b), SKILL.md (4015b), _meta.json (122b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: dgr\nversion: \"1.1.0\"\ndescription: Superseded reference skill: structured reasoning records, kept for provenance. Enforcement moved to DGR Gate, a pre-execution gate for OpenClaw tool calls: clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  openclaw:\n    emoji: \"🧭\"\n  category: \"reasoning\"\n---\n\n> **Superseded — kept as a reference.** This skill formats reasoning into a structured record. It cannot stop an action; the model decides whether to follow it.\n>\n> **A rule in the prompt is a request. A rule in the tool is a gate.** The enforcing successor is [DGR Gate](https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate), a pre-execution gate that checks a call against operator-configured policy before it runs. It currently gates two demo tools, not your real ones.\n>\n> This page remains published as the record of the earlier approach and why it was replaced. Existing installs keep working; no further development.\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr · **Version:** 1.1.0 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON\n\n## What this skill does\nThis skill produced a structured decision record: decision context, explicit assumptions, identified risks, a recommendation with rationale, and a consistency check. The output was documentation of reasoning, formatted as schema-valid JSON for easier review and storage.\n\nThat was insufficient. A model's own justification is not evidence — a better-formatted rationale from the same model does not change that. A prompt-level instruction can be lost during context compaction or ignored after the model reads untrusted text. This approach documented reasoning but could not enforce constraints on tool execution.\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Changelog\n**1.1.0** — Repositioned as a superseded reference. Terminology aligned with DGR Gate (pre-execution gate). Removed usage guidance; this skill formats reasoning and does not enforce. Existing installs unaffected.\n\n**1.0.4** — Remove redundant CLAWHUB_SUMMARY.md; summary now sourced from SKILL.md front-matter.\n\n**1.0.3** — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.\n\n**1.0.2** — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.\n\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1790276384774\n}\n\nFile v1.1.0:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.1.0:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.1.0:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.1.0:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.1.0:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.1.0:skill-card.md\n\n## Description:\n\nFormats decision reasoning as structured JSON records with assumptions, risks, recommendations, and consistency checks for human review.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dgr-ai-labs](https://clawhub.ai/user/dgr-ai-labs)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDecision makers, teams, and auditors use this reference skill to document and review assumptions, risks, recommendations, and open questions in a consistent decision record. It does not enforce approval or block tool actions.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Structured reasoning can be mistaken for an enforced approval or evidence that a recommendation is correct.\n\nMitigation: Treat the record as review material, not an automated gate; verify assumptions and apply independent policy checks before acting.\n\nRisk: Illustrative lending, access-control, or incident-response examples may be applied to high-impact decisions without sufficient review.\n\nMitigation: Require qualified human review and applicable policy or compliance checks before using recommendations in high-impact decisions.\n\n## Reference(s):\n\n- [DGR skill release](https://clawhub.ai/dgr-ai-labs/skills/dgr)\n- [DGR Gate (enforcing successor)](https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate)\n- [DGR artifact schema](artifact/schema.json)\n- [DGR field guide](artifact/field_guide.md)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Decision guidance]\n\n**Output Format:** [JSON decision record]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes assumptions, risks, a recommendation, and a consistency check; may flag required human review.]\n\n## Skill Version(s):\n\n1.1.0 (source: release metadata and skill frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.1.0:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n          \"minLength\": 8\n        },\n        \"query_summary\": {\n          \"type\": \"string\",\n          \"maxLength\": 500\n        }\n      }\n    },\n    \"clarifications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"question\",\n          \"why_needed\",\n          \"blocking\"\n        ],\n        \"properties\": {\n          \"question\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"why_needed\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"blocking\": {\n            \"type\": \"boolean\"\n          }\n        }\n      },\n      \"default\": []\n    },\n    \"decomposition\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      },\n      \"default\": []\n    },\n    \"assumptions\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"statement\",\n          \"impact_if_wrong\"\n        ],\n        \"properties\": {\n          \"statement\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"impact_if_wrong\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"risks\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"risk\",\n          \"severity\",\n          \"mitigation\"\n        ],\n        \"properties\": {\n          \"risk\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"severity\": {\n            \"type\": \"string\",\n            \"enum\": [\n              \"low\",\n              \"medium\",\n              \"high\"\n            ]\n          },\n          \"mitigation\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"recommendation\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"action\",\n        \"rationale\",\n        \"confidence\",\n        \"review_required\",\n        \"next_steps\"\n      ],\n      \"properties\": {\n        \"action\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"rationale\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"confidence\": {\n          \"type\": \"number\",\n          \"minimum\": 0,\n          \"maximum\": 1\n        },\n        \"review_required\": {\n          \"type\": \"boolean\"\n        },\n        \"next_steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"minItems\": 0\n        }\n      }\n    },\n    \"consistency_check\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"checks\",\n        \"passed\",\n        \"notes\"\n      ],\n      \"properties\": {\n        \"checks\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          }\n        },\n        \"passed\": {\n          \"type\": \"boolean\"\n        },\n        \"notes\": {\n          \"type\": \"string\"\n        }\n      }\n    }\n  }\n}\n\nArchive v1.0.4: 9 files, 11481 bytes\n\nFiles: examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), skill-card.md (1952b), SKILL.md (3955b), _meta.json (122b)\n\nFile v1.0.4:SKILL.md\n\n---\nname: dgr\ndescription: Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  clawdbot:\n    emoji: \"🧭\"\n  category: \"reasoning\"\n---\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr · **Version:** 1.0.4 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON\n\n## What this skill does\nDGR is a **reasoning governance protocol** that produces a **machine‑validated, auditable artifact** describing:\n- the decision context,\n- explicit assumptions and risks,\n- a recommendation with rationale,\n- and a consistency check.\n\nThis skill is designed for **high‑stakes** or **review‑required** decisions where you want traceability and structured review.\n\n## How to use\n1. **Ask your question** — Provide a decision request or problem context\n2. **Pick mode:** `dgr_min` | `dgr_full` | `dgr_strict`\n3. **Store JSON artifact** in ticket / incident / audit log\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Mode Behavior\n\n| Mode | Speed | Detail Level | Clarifications | Review Required | Use Case |\n|------|-------|--------------|---------------|----------------|----------|\n| `dgr_min` | Fastest | Minimal compliant output | Only critical gaps | Risk-based | Quick decisions, low stakes |\n| `dgr_full` | Moderate | Fuller decomposition + alternatives | More proactive | Balanced | Standard decision support |\n| `dgr_strict` | Slower | Conservative analysis | More questioning | Default on ambiguity | High-stakes, uncertain contexts |\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Files in this skill\n- `prompt.md` — operational instructions\n- `schema.json` — output schema (stub aligned to DGR spec)\n- `examples/*.md` — example inputs and outputs\n- `field_guide.md` — how to interpret DGR artifact fields\n\n## Quick start\n1) Provide a decision request.\n2) Choose a mode (`dgr_min` default).\n3) The skill returns a JSON artifact suitable for review and storage.\n\n## Changelog\n**1.0.4** — Remove redundant CLAWHUB_SUMMARY.md; summary now sourced from SKILL.md front-matter.\n\n**1.0.3** — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.\n\n**1.0.2** — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.\n\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.0.4:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.0.4\",\n  \"publishedAt\": 1770170439426\n}\n\nFile v1.0.4:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.0.4:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.0.4:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.0.4:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.0.4:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.0.4:skill-card.md\n\n## Description:\n\nAudit-ready decision artifacts for LLM outputs with assumptions, risks, recommendations, and review gating as schema-valid JSON.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[sapenov](https://clawhub.ai/user/sapenov)\n\n### License/Terms of Use:\n\n\n## Use Case:\n\nDecision makers, auditors, and teams use DGR to turn decision requests into auditable JSON records that surface assumptions, risks, recommendations, review gates, and consistency checks for ticket, incident, or audit workflows.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: DGR outputs may be mistaken for authorization to act in production, financial, legal, medical, safety, or access-control decisions.\n\nMitigation: Treat outputs as decision documentation and require human approval before action in high-stakes workflows.\n\nRisk: An incident-response example understates review gating for a production rollback recommendation.\n\nMitigation: Apply production change controls and require incident owner approval before acting on rollback recommendations.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/sapenov/skills/dgr)\n- [DGR Homepage](https://www.clawhub.ai/sapenov/dgr)\n- [DGR Field Guide](artifact/field_guide.md)\n- [DGR JSON Schema](artifact/schema.json)\n\n## Skill Output:\n\n**Output Type(s):** [text, json, guidance]\n\n**Output Format:** [Schema-valid JSON]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Must conform to artifact/schema.json and include assumptions, risks, recommendation, and consistency_check.]\n\n## Skill Version(s):\n\n1.0.4 (source: server release metadata and SKILL.md changelog)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.0.4:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n          \"minLength\": 8\n        },\n        \"query_summary\": {\n          \"type\": \"string\",\n          \"maxLength\": 500\n        }\n      }\n    },\n    \"clarifications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"question\",\n          \"why_needed\",\n          \"blocking\"\n        ],\n        \"properties\": {\n          \"question\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"why_needed\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"blocking\": {\n            \"type\": \"boolean\"\n          }\n        }\n      },\n      \"default\": []\n    },\n    \"decomposition\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      },\n      \"default\": []\n    },\n    \"assumptions\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"statement\",\n          \"impact_if_wrong\"\n        ],\n        \"properties\": {\n          \"statement\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"impact_if_wrong\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"risks\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"risk\",\n          \"severity\",\n          \"mitigation\"\n        ],\n        \"properties\": {\n          \"risk\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"severity\": {\n            \"type\": \"string\",\n            \"enum\": [\n              \"low\",\n              \"medium\",\n              \"high\"\n            ]\n          },\n          \"mitigation\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"recommendation\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"action\",\n        \"rationale\",\n        \"confidence\",\n        \"review_required\",\n        \"next_steps\"\n      ],\n      \"properties\": {\n        \"action\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"rationale\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"confidence\": {\n          \"type\": \"number\",\n          \"minimum\": 0,\n          \"maximum\": 1\n        },\n        \"review_required\": {\n          \"type\": \"boolean\"\n        },\n        \"next_steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"minItems\": 0\n        }\n      }\n    },\n    \"consistency_check\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"checks\",\n        \"passed\",\n        \"notes\"\n      ],\n      \"properties\": {\n        \"checks\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          }\n        },\n        \"passed\": {\n          \"type\": \"boolean\"\n        },\n        \"notes\": {\n          \"type\": \"string\"\n        }\n      }\n    }\n  }\n}\n\nArchive v1.0.3: 9 files, 11028 bytes\n\nFiles: CLAWHUB_SUMMARY.md (968b), examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), SKILL.md (3855b), _meta.json (122b)\n\nFile v1.0.3:SKILL.md\n\n---\nname: dgr\ndescription: Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  clawdbot:\n    emoji: \"🧭\"\n  category: \"reasoning\"\n---\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr · **Version:** 1.0.3 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON\n\n## What this skill does\nDGR is a **reasoning governance protocol** that produces a **machine‑validated, auditable artifact** describing:\n- the decision context,\n- explicit assumptions and risks,\n- a recommendation with rationale,\n- and a consistency check.\n\nThis skill is designed for **high‑stakes** or **review‑required** decisions where you want traceability and structured review.\n\n## How to use\n1. **Ask your question** — Provide a decision request or problem context\n2. **Pick mode:** `dgr_min` | `dgr_full` | `dgr_strict`\n3. **Store JSON artifact** in ticket / incident / audit log\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Mode Behavior\n\n| Mode | Speed | Detail Level | Clarifications | Review Required | Use Case |\n|------|-------|--------------|---------------|----------------|----------|\n| `dgr_min` | Fastest | Minimal compliant output | Only critical gaps | Risk-based | Quick decisions, low stakes |\n| `dgr_full` | Moderate | Fuller decomposition + alternatives | More proactive | Balanced | Standard decision support |\n| `dgr_strict` | Slower | Conservative analysis | More questioning | Default on ambiguity | High-stakes, uncertain contexts |\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Files in this skill\n- `prompt.md` — operational instructions\n- `schema.json` — output schema (stub aligned to DGR spec)\n- `examples/*.md` — example inputs and outputs\n- `field_guide.md` — how to interpret DGR artifact fields\n\n## Quick start\n1) Provide a decision request.\n2) Choose a mode (`dgr_min` default).\n3) The skill returns a JSON artifact suitable for review and storage.\n\n## Changelog\n**1.0.3** — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.\n\n**1.0.2** — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.\n\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.0.3:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.0.3\",\n  \"publishedAt\": 1770170017685\n}\n\nFile v1.0.3:CLAWHUB_SUMMARY.md\n\n# ClawHub Summary Options\n\n## Short Version (High-Conversion)\n\nDGR is a governance protocol for LLM outputs. It returns a schema-valid JSON artifact with explicit assumptions, risks, recommendation, review gating, and consistency checks—designed for high-stakes / review-required decisions.\nNon-claim: DGR improves auditability and reviewability, not correctness.\n\n## Longer Version (If Space Allows)\n\nDecision-Grade Reasoning (DGR) turns an unstructured question into a review-ready JSON record: assumptions, risks, clarifications, a recommendation, and a consistency check.\nUse it when you need traceability, policy alignment, and reviewer throughput—not \"chain-of-thought.\"\nNo correctness guarantee; human decision-makers remain responsible.\n\n---\n\n**Instructions for ClawHub:**\n- Copy either version above into the ClawHub listing summary field\n- Use the short version if character limits are tight\n- Use the longer version for better conversion if space allows\n\nFile v1.0.3:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.0.3:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.0.3:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.0.3:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.0.3:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.0.3:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n          \"minLength\": 8\n        },\n        \"query_summary\": {\n          \"type\": \"string\",\n          \"maxLength\": 500\n        }\n      }\n    },\n    \"clarifications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"question\",\n          \"why_needed\",\n          \"blocking\"\n        ],\n        \"properties\": {\n          \"question\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"why_needed\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"blocking\": {\n            \"type\": \"boolean\"\n          }\n        }\n      },\n      \"default\": []\n    },\n    \"decomposition\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      },\n      \"default\": []\n    },\n    \"assumptions\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"statement\",\n          \"impact_if_wrong\"\n        ],\n        \"properties\": {\n          \"statement\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"impact_if_wrong\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"risks\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"risk\",\n          \"severity\",\n          \"mitigation\"\n        ],\n        \"properties\": {\n          \"risk\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"severity\": {\n            \"type\": \"string\",\n            \"enum\": [\n              \"low\",\n              \"medium\",\n              \"high\"\n            ]\n          },\n          \"mitigation\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"recommendation\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"action\",\n        \"rationale\",\n        \"confidence\",\n        \"review_required\",\n        \"next_steps\"\n      ],\n      \"properties\": {\n        \"action\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"rationale\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"confidence\": {\n          \"type\": \"number\",\n          \"minimum\": 0,\n          \"maximum\": 1\n        },\n        \"review_required\": {\n          \"type\": \"boolean\"\n        },\n        \"next_steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"minItems\": 0\n        }\n      }\n    },\n    \"consistency_check\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"checks\",\n        \"passed\",\n        \"notes\"\n      ],\n      \"properties\": {\n        \"checks\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          }\n        },\n        \"passed\": {\n          \"type\": \"boolean\"\n        },\n        \"notes\": {\n          \"type\": \"string\"\n        }\n      }\n    }\n  }\n}\n\nArchive v1.0.2: 9 files, 10960 bytes\n\nFiles: CLAWHUB_SUMMARY.md (968b), examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), SKILL.md (3757b), _meta.json (122b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: dgr\ndescription: Governance protocol that returns schema-valid JSON with assumptions, risks, recommendation, review gating, and consistency checks for audit-ready decisions.\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  clawdbot:\n    emoji: \"🧭\"\n---\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr\n**Display name:** Decision‑Grade Reasoning (DGR)\n**Version:** 1.0.2\n**Tags:** reasoning, governance, auditability, safety, compliance\n\n## What this skill does\nDGR is a **reasoning governance protocol** that produces a **machine‑validated, auditable artifact** describing:\n- the decision context,\n- explicit assumptions and risks,\n- a recommendation with rationale,\n- and a consistency check.\n\nThis skill is designed for **high‑stakes** or **review‑required** decisions where you want traceability and structured review.\n\n## How to use\n1. **Ask your question** — Provide a decision request or problem context\n2. **Pick mode:** `dgr_min` | `dgr_full` | `dgr_strict`\n3. **Store JSON artifact** in ticket / incident / audit log\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Mode Behavior\n\n| Mode | Speed | Detail Level | Clarifications | Review Required | Use Case |\n|------|-------|--------------|---------------|----------------|----------|\n| `dgr_min` | Fastest | Minimal compliant output | Only critical gaps | Risk-based | Quick decisions, low stakes |\n| `dgr_full` | Moderate | Fuller decomposition + alternatives | More proactive | Balanced | Standard decision support |\n| `dgr_strict` | Slower | Conservative analysis | More questioning | Default on ambiguity | High-stakes, uncertain contexts |\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Files in this skill\n- `prompt.md` — operational instructions\n- `schema.json` — output schema (stub aligned to DGR spec)\n- `examples/*.md` — example inputs and outputs\n- `field_guide.md` — how to interpret DGR artifact fields\n\n## Quick start\n1) Provide a decision request.\n2) Choose a mode (`dgr_min` default).\n3) The skill returns a JSON artifact suitable for review and storage.\n\n## Changelog\n**1.0.2** — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.\n\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.0.2\",\n  \"publishedAt\": 1770169392558\n}\n\nFile v1.0.2:CLAWHUB_SUMMARY.md\n\n# ClawHub Summary Options\n\n## Short Version (High-Conversion)\n\nDGR is a governance protocol for LLM outputs. It returns a schema-valid JSON artifact with explicit assumptions, risks, recommendation, review gating, and consistency checks—designed for high-stakes / review-required decisions.\nNon-claim: DGR improves auditability and reviewability, not correctness.\n\n## Longer Version (If Space Allows)\n\nDecision-Grade Reasoning (DGR) turns an unstructured question into a review-ready JSON record: assumptions, risks, clarifications, a recommendation, and a consistency check.\nUse it when you need traceability, policy alignment, and reviewer throughput—not \"chain-of-thought.\"\nNo correctness guarantee; human decision-makers remain responsible.\n\n---\n\n**Instructions for ClawHub:**\n- Copy either version above into the ClawHub listing summary field\n- Use the short version if character limits are tight\n- Use the longer version for better conversion if space allows\n\nFile v1.0.2:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.0.2:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.0.2:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.0.2:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.0.2:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.0.2:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n          \"minLength\": 8\n        },\n        \"query_summary\": {\n          \"type\": \"string\",\n          \"maxLength\": 500\n        }\n      }\n    },\n    \"clarifications\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"question\",\n          \"why_needed\",\n          \"blocking\"\n        ],\n        \"properties\": {\n          \"question\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"why_needed\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"blocking\": {\n            \"type\": \"boolean\"\n          }\n        }\n      },\n      \"default\": []\n    },\n    \"decomposition\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      },\n      \"default\": []\n    },\n    \"assumptions\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"statement\",\n          \"impact_if_wrong\"\n        ],\n        \"properties\": {\n          \"statement\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"impact_if_wrong\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"risks\": {\n      \"type\": \"array\",\n      \"minItems\": 1,\n      \"items\": {\n        \"type\": \"object\",\n        \"additionalProperties\": false,\n        \"required\": [\n          \"risk\",\n          \"severity\",\n          \"mitigation\"\n        ],\n        \"properties\": {\n          \"risk\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          },\n          \"severity\": {\n            \"type\": \"string\",\n            \"enum\": [\n              \"low\",\n              \"medium\",\n              \"high\"\n            ]\n          },\n          \"mitigation\": {\n            \"type\": \"string\",\n            \"minLength\": 3\n          }\n        }\n      }\n    },\n    \"recommendation\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"action\",\n        \"rationale\",\n        \"confidence\",\n        \"review_required\",\n        \"next_steps\"\n      ],\n      \"properties\": {\n        \"action\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"rationale\": {\n          \"type\": \"string\",\n          \"minLength\": 3\n        },\n        \"confidence\": {\n          \"type\": \"number\",\n          \"minimum\": 0,\n          \"maximum\": 1\n        },\n        \"review_required\": {\n          \"type\": \"boolean\"\n        },\n        \"next_steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"minItems\": 0\n        }\n      }\n    },\n    \"consistency_check\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"checks\",\n        \"passed\",\n        \"notes\"\n      ],\n      \"properties\": {\n        \"checks\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"string\"\n          }\n        },\n        \"passed\": {\n          \"type\": \"boolean\"\n        },\n        \"notes\": {\n          \"type\": \"string\"\n        }\n      }\n    }\n  }\n}\n\nArchive v1.0.1: 9 files, 10771 bytes\n\nFiles: CLAWHUB_SUMMARY.md (968b), examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), SKILL.md (3369b), _meta.json (122b)\n\nFile v1.0.1:SKILL.md\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr\n**Display name:** Decision‑Grade Reasoning (DGR)\n**Version:** 1.0.0\n**Tags:** reasoning, governance, auditability, safety, compliance\n\n## What this skill does\nDGR is a **reasoning governance protocol** that produces a **machine‑validated, auditable artifact** describing:\n- the decision context,\n- explicit assumptions and risks,\n- a recommendation with rationale,\n- and a consistency check.\n\nThis skill is designed for **high‑stakes** or **review‑required** decisions where you want traceability and structured review.\n\n## How to use\n1. **Ask your question** — Provide a decision request or problem context\n2. **Pick mode:** `dgr_min` | `dgr_full` | `dgr_strict`\n3. **Store JSON artifact** in ticket / incident / audit log\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Mode Behavior\n\n| Mode | Speed | Detail Level | Clarifications | Review Required | Use Case |\n|------|-------|--------------|---------------|----------------|----------|\n| `dgr_min` | Fastest | Minimal compliant output | Only critical gaps | Risk-based | Quick decisions, low stakes |\n| `dgr_full` | Moderate | Fuller decomposition + alternatives | More proactive | Balanced | Standard decision support |\n| `dgr_strict` | Slower | Conservative analysis | More questioning | Default on ambiguity | High-stakes, uncertain contexts |\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If uncertainty is high, explicitly state uncertainty and limit scope.\n- Do not fabricate sources or cite documents you did not see.\n\n## Files in this skill\n- `prompt.md` — operational instructions\n- `schema.json` — output schema (stub aligned to DGR spec)\n- `examples/*.md` — example inputs and outputs\n- `field_guide.md` — how to interpret DGR artifact fields\n\n## Quick start\n1) Provide a decision request.\n2) Choose a mode (`dgr_min` default).\n3) The skill returns a JSON artifact suitable for review and storage.\n\n## Changelog\n**1.0.0** — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.\n\n> Note: This is an **opt‑in** reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1770163326821\n}\n\nFile v1.0.1:CLAWHUB_SUMMARY.md\n\n# ClawHub Summary Options\n\n## Short Version (High-Conversion)\n\nDGR is a governance protocol for LLM outputs. It returns a schema-valid JSON artifact with explicit assumptions, risks, recommendation, review gating, and consistency checks—designed for high-stakes / review-required decisions.\nNon-claim: DGR improves auditability and reviewability, not correctness.\n\n## Longer Version (If Space Allows)\n\nDecision-Grade Reasoning (DGR) turns an unstructured question into a review-ready JSON record: assumptions, risks, clarifications, a recommendation, and a consistency check.\nUse it when you need traceability, policy alignment, and reviewer throughput—not \"chain-of-thought.\"\nNo correctness guarantee; human decision-makers remain responsible.\n\n---\n\n**Instructions for ClawHub:**\n- Copy either version above into the ClawHub listing summary field\n- Use the short version if character limits are tight\n- Use the longer version for better conversion if space allows\n\nFile v1.0.1:examples/access_request.md\n\n# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```\n\nFile v1.0.1:examples/incident_triage.md\n\n# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align with recommended action\",\n      \"Next steps support both immediate recovery and learning\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision balances immediate customer impact against development velocity. Parallel debug approach preserves learning while prioritizing service restoration.\"\n  }\n}\n```\n\nFile v1.0.1:examples/loan_preapproval.md\n\n# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```\n\nFile v1.0.1:field_guide.md\n\n# DGR Field Guide — How to Interpret Artifact Fields\n\nThis guide explains how to read and act on DGR (Decision-Grade Reasoning) artifacts.\n\n## Core Sections\n\n### `meta`\n**Purpose:** Artifact metadata for tracking and governance\n- `artifact_id` — Unique identifier for this decision record\n- `spec_version` — DGR format version (currently 1.0.0)\n- `created_at` — Timestamp when analysis was performed\n- `mode` — Analysis depth: `dgr_min` (fast), `dgr_full` (detailed), `dgr_strict` (conservative)\n\n### `input`\n**Purpose:** Captures what was analyzed\n- `query_summary` — Human-readable description of the decision request\n- `query_hash` — Stable identifier for the exact request (for deduplication/linking)\n\n### `clarifications` (optional)\n**Purpose:** Questions that need answers before proceeding\n- `question` — What specific information is missing\n- `why_needed` — Why this information affects the decision\n- `blocking` — Whether decision should be delayed until clarified\n\n**Action:** Address blocking clarifications before implementing recommendations.\n\n### `assumptions`\n**Purpose:** Explicit assumptions underlying the reasoning\n- `statement` — What is being assumed to be true\n- `impact_if_wrong` — Risk if this assumption proves incorrect\n\n**Action:** Validate critical assumptions before acting on recommendations.\n\n### `risks`\n**Purpose:** Potential negative outcomes and their handling\n- `risk` — Description of what could go wrong\n- `severity` — Impact level: `low`, `medium`, `high`\n- `mitigation` — How to reduce likelihood or impact\n\n**Action:** Implement mitigations for high-severity risks; monitor medium/low risks.\n\n### `recommendation`\n**Purpose:** The actual decision guidance\n- `action` — Recommended course of action\n- `rationale` — Why this action is recommended\n- `confidence` — Certainty level (0.0-1.0, where 1.0 = completely confident)\n- `review_required` — Whether human review is needed before acting\n- `next_steps` — Concrete actions to implement the recommendation\n\n**Action:** If `review_required = true`, seek appropriate stakeholder approval before proceeding.\n\n### `consistency_check`\n**Purpose:** Internal validation of the reasoning\n- `checks` — List of consistency verifications performed\n- `passed` — Whether all checks succeeded\n- `notes` — Additional context on the validation\n\n**Action:** If `passed = false`, investigate inconsistencies before using the recommendation.\n\n## Governance Guidelines\n\n### High-Stakes Decisions\n- Always honor `review_required = true`\n- Validate assumptions for decisions with broad impact\n- Document any deviations from recommendations\n\n### Confidence Interpretation\n- **0.8-1.0:** High confidence, proceed with normal review\n- **0.5-0.8:** Moderate confidence, consider additional validation\n- **0.0-0.5:** Low confidence, seek expert input or additional data\n\n### Risk Management\n- **High severity risks:** Must have mitigation plans in place\n- **Medium severity risks:** Monitor closely during implementation\n- **Low severity risks:** Acceptable risk level for most contexts\n\n## Usage Patterns\n\n### For Decision Makers\n1. Check `review_required` and `consistency_check.passed`\n2. Review high-severity risks and their mitigations\n3. Validate critical assumptions\n4. Implement recommended action with appropriate safeguards\n\n### For Auditors\n1. Verify artifact completeness (required fields present)\n2. Assess assumption reasonableness\n3. Check risk identification and mitigation adequacy\n4. Review consistency check results\n\n### For Teams\n1. Use artifacts as decision documentation\n2. Reference `artifact_id` in related work\n3. Update assumptions/risks as context changes\n4. Conduct post-decision reviews using the artifact structure\n\nFile v1.0.1:prompt.md\n\n# DGR Skill Prompt\n\nYou are running the **Decision‑Grade Reasoning (DGR)** skill.\n\n## Core directive\nReturn **only** a JSON object that **conforms to `schema.json`**.\n\n## Operating rules (governance‑aligned)\n1. **No correctness guarantees.** Do not claim certainty you do not have.\n2. **No fabricated evidence.** If you do not have sources, say so in `assumptions` and `risks`.\n3. **Clarify when required.** If critical inputs are missing, add entries to `clarifications` and set `recommendation.review_required = true`.\n4. **High‑stakes gating.** For legal/medical/financial/safety‑critical decisions, default to `review_required = true` unless the user explicitly confirms they have professional guidance.\n5. **Keep reasoning auditable, not verbose.** Summarize rationales; do not emit chain‑of‑thought.\n\n## Modes\n- `dgr_min`: minimal compliant artifact (fastest)\n- `dgr_full`: fuller decomposition + alternatives\n- `dgr_strict`: conservative; more clarifications; review_required by default on ambiguity\n\n## Artifact construction steps\n1. **Meta + input**\n   - Create `meta.artifact_id` (UUID v4), `meta.created_at` (ISO8601), `meta.spec_version = \"1.0.0\"`.\n   - Summarize the query in `input.query_summary` (≤500 chars). Provide a stable `input.query_hash` (e.g., sha256 of the user query).\n\n2. **Clarifications**\n   - If any key missing information blocks a decision, add to `clarifications` with:\n     - `question`, `why_needed`, `blocking = true`.\n\n3. **Assumptions**\n   - Add explicit assumptions. Each assumption must include:\n     - `statement`, `impact_if_wrong` (short).\n\n4. **Risks**\n   - Add concrete risks, including:\n     - `risk`, `severity` (low/med/high), `mitigation`.\n\n5. **Recommendation**\n   - Provide a single recommended action with:\n     - `action`, `rationale`, `confidence` (0–1), `review_required` (bool),\n     - `next_steps` (array).\n\n6. **Consistency check**\n   - Verify internal consistency:\n     - `checks` (array of short checks), `passed` (bool), `notes`.\n\n## Output format\nReturn **only** JSON.\n\nFile v1.0.1:schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"$id\": \"https://example.org/dgr.schema.json\",\n  \"title\": \"DGR Artifact\",\n  \"type\": \"object\",\n  \"additionalProperties\": false,\n  \"required\": [\n    \"meta\",\n    \"input\",\n    \"assumptions\",\n    \"risks\",\n    \"recommendation\",\n    \"consistency_check\"\n  ],\n  \"properties\": {\n    \"meta\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"artifact_id\",\n        \"spec_version\",\n        \"created_at\",\n        \"mode\"\n      ],\n      \"properties\": {\n        \"artifact_id\": {\n          \"type\": \"string\",\n          \"minLength\": 8,\n          \"description\": \"UUID v4 recommended\"\n        },\n        \"spec_version\": {\n          \"type\": \"string\",\n          \"const\": \"1.0.0\"\n        },\n        \"created_at\": {\n          \"type\": \"string\",\n          \"format\": \"date-time\"\n        },\n        \"mode\": {\n          \"type\": \"string\",\n          \"enum\": [\n            \"dgr_min\",\n            \"dgr_full\",\n            \"dgr_strict\"\n          ]\n        },\n        \"model\": {\n          \"type\": \"string\"\n        },\n        \"task_class\": {\n          \"type\": \"string\"\n        },\n        \"source_ref\": {\n          \"type\": \"string\"\n        }\n      }\n    },\n    \"input\": {\n      \"type\": \"object\",\n      \"additionalProperties\": false,\n      \"required\": [\n        \"query_hash\",\n        \"query_summary\"\n      ],\n      \"properties\": {\n        \"query_hash\": {\n          \"type\": \"string\",\n\nArchive v1.0.0: 8 files, 9992 bytes\n\nFiles: examples/access_request.md (2204b), examples/incident_triage.md (3299b), examples/loan_preapproval.md (2641b), field_guide.md (3753b), prompt.md (2047b), schema.json (4587b), SKILL.md (3073b), _meta.json (122b)","readmeExcerpt":"Skill: Decision-Grade Reasoning (DGR) Owner: dgr-ai-labs Summary: Audit-ready decision artifacts for LLM outputs - assumptions, risks, recommendation, and review gating (schema-valid JSON). Replaced with https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate) Tags: latest:1.1.1 Version history: v1.1.1 | 2026-09-24T19:04:55.087Z | user No changes in this version. - Version 1.1.1: No code or documentation updates det","codeSnippets":[],"executableExamples":[{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n"},{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchas"},{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and doc"},{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n"},{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchas"},{"language":"json","snippet":"{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and doc"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: dgr\nversion: \"1.1.0\"\ndescription: Superseded reference skill: structured reasoning records, kept for provenance. Enforcement moved to DGR Gate, a pre-execution gate for OpenClaw tool calls: clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate\nhomepage: https://www.clawhub.ai/sapenov/dgr\nmetadata:\n  openclaw:\n    emoji: \"🧭\"\n  category: \"reasoning\"\n---\n\n> **Superseded — kept as a reference.** This skill formats reasoning into a structured record. It cannot stop an action; the model decides whether to follow it.\n>\n> **A rule in the prompt is a request. A rule in the tool is a gate.** The enforcing successor is [DGR Gate](https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate), a pre-execution gate that checks a call against operator-configured policy before it runs. It currently gates two demo tools, not your real ones.\n>\n> This page remains published as the record of the earlier approach and why it was replaced. Existing installs keep working; no further development.\n\n# DGR — Decision‑Grade Reasoning (Governance Protocol)\n\n**Purpose:** produce an auditable, machine‑validated decision record for review and storage.\n\n**Slug:** dgr · **Version:** 1.1.0 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON\n\n## What this skill does\nThis skill produced a structured decision record: decision context, explicit assumptions, identified risks, a recommendation with rationale, and a consistency check. The output was documentation of reasoning, formatted as schema-valid JSON for easier review and storage.\n\nThat was insufficient. A model's own justification is not evidence — a better-formatted rationale from the same model does not change that. A prompt-level instruction can be lost during context compaction or ignored after the model reads untrusted text. This approach documented reasoning but could not enforce constraints on tool execution.\n\n## What this skill is NOT (non‑claims)\nThis skill does **NOT** guarantee:\n- correctness, optimality, or truth,\n- elimination of hallucinations,\n- legal/medical/financial advice suitability,\n- or regulatory compliance by itself.\n\nDGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.\n\n## When to use\nUse when you need:\n- an auditable record of reasoning,\n- explicit assumptions/risks surfaced,\n- reviewer‑friendly structure,\n- a consistent output format across tasks and models.\n\n## Inputs\n- A user request/question (free text).\n- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.\n\n## Outputs\nA single JSON artifact matching `schema.json`.\n\nMinimum acceptance criteria (see `schema.json`):\n- at least **1 assumption**\n- at least **1 risk**\n- `recommendation` present\n- `consistency_check` present\n\n## Safety / governance boundaries\n- Always **ask for clarification** if key decision inputs are missing.\n- If the decision is high‑risk, escalate via `recommendation.review_required = true`.\n- If unce"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn73pggn86vrxrgwz3jjtt974180e4kr\",\n  \"slug\": \"dgr\",\n  \"version\": \"1.1.1\",\n  \"publishedAt\": 1790276695087\n}"},{"path":"examples/access_request.md","content":"# Example — Access Request Review (dgr_min)\n\n## Input\nA contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000001\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_min\",\n    \"task_class\": \"access_control\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Evaluate a contractor request for temporary admin access to a production database for incident debugging.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"Which exact systems/tables are needed and what actions must be performed?\",\n      \"why_needed\": \"Least-privilege scope cannot be determined without precise access needs.\",\n      \"blocking\": true\n    }\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The contractor has a valid business need related to an active incident.\",\n      \"impact_if_wrong\": \"Unnecessary elevated access increases breach and compliance risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Overbroad admin access could enable data exfiltration or destructive actions.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Use time-bound, scoped access; require MFA; enforce query logging and approval.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.\",\n    \"rationale\": \"The request as stated is too broad for production; incident work can often be done with scoped read/write and audited break-glass procedures.\",\n    \"confidence\": 0.62,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect exact scope of access required (systems, actions, time window).\",\n      \"Implement time-bound role with MFA and audit logging.\",\n      \"Require incident manager approval and post-access review.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Assumptions and risks align with recommendation to scope access.\",\n      \"Clarification is marked blocking and recommendation sets review_required.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Decision deferred pending least-privilege scoping.\"\n  }\n}\n```"},{"path":"examples/incident_triage.md","content":"# Example — Incident Triage (dgr_full)\n\n## Input\nProduction API showing 500 errors at 15% rate for 20 minutes. Customer reports unable to complete purchases. Team lead wants to know: should we rollback the deployment from 2 hours ago or continue debugging?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:00:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"incident_response\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE\",\n    \"query_summary\": \"Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What is the error pattern - specific endpoints, user segments, or geographic regions?\",\n      \"why_needed\": \"Error scope affects rollback vs. targeted fix decision.\",\n      \"blocking\": false\n    }\n  ],\n  \"decomposition\": [\n    \"Assess current business impact and customer exposure\",\n    \"Evaluate rollback feasibility and time-to-recovery\",\n    \"Consider debugging complexity and available resources\",\n    \"Factor in deployment pipeline and testing coverage\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The deployment 2 hours ago introduced the errors (correlation observed).\",\n      \"impact_if_wrong\": \"Rollback may not resolve the issue and could delay actual root cause remediation.\"\n    },\n    {\n      \"statement\": \"Rollback can be completed safely within 15-30 minutes.\",\n      \"impact_if_wrong\": \"Extended downtime or additional system instability during rollback process.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Continued debugging delays customer purchase recovery and increases revenue loss.\",\n      \"severity\": \"high\",\n      \"mitigation\": \"Set explicit debug time limit (30 min max) before mandatory rollback decision.\"\n    },\n    {\n      \"risk\": \"Hasty rollback could introduce different issues or data inconsistencies.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Verify rollback safety with database state checks and staged deployment approach.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Execute rollback immediately while maintaining parallel debug effort.\",\n    \"rationale\": \"15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.\",\n    \"confidence\": 0.78,\n    \"review_required\": false,\n    \"next_steps\": [\n      \"Begin rollback process with database consistency verification\",\n      \"Continue debugging in parallel using logs and monitoring data\",\n      \"Implement additional monitoring before next deployment\",\n      \"Conduct post-incident review within 48 hours\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Business impact (purchase failures) justifies rollback urgency\",\n      \"Assumptions about deployment correlation and rollback safety are reasonable\",\n      \"Risk mitigation strategies align w"},{"path":"examples/loan_preapproval.md","content":"# Example — Loan Pre-Approval (dgr_full)\n\n## Input\nShould we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?\n\n## Output (JSON)\n```json\n{\n  \"meta\": {\n    \"artifact_id\": \"00000000-0000-4000-8000-000000000002\",\n    \"spec_version\": \"1.0.0\",\n    \"created_at\": \"2026-02-03T12:05:00Z\",\n    \"mode\": \"dgr_full\",\n    \"task_class\": \"credit_decision\"\n  },\n  \"input\": {\n    \"query_hash\": \"sha256:EXAMPLE2\",\n    \"query_summary\": \"Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments.\"\n  },\n  \"clarifications\": [\n    {\n      \"question\": \"What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?\",\n      \"why_needed\": \"Risk depends strongly on exposure size, affordability, and stability of cash flow.\",\n      \"blocking\": true\n    }\n  ],\n  \"decomposition\": [\n    \"Evaluate affordability (DTI, income stability, loan size/term).\",\n    \"Evaluate credit behavior (late payments, trend, utilization).\",\n    \"Check policy thresholds and adverse action requirements.\",\n    \"Decide conditional approval vs decline vs request more info.\"\n  ],\n  \"assumptions\": [\n    {\n      \"statement\": \"The applicant’s reported DTI is accurate and verified.\",\n      \"impact_if_wrong\": \"Understated obligations could make the loan unaffordable and increase default risk.\"\n    }\n  ],\n  \"risks\": [\n    {\n      \"risk\": \"Recent delinquencies may indicate deteriorating financial stability.\",\n      \"severity\": \"medium\",\n      \"mitigation\": \"Require verification, consider smaller amount/shorter term, and price for risk per policy.\"\n    }\n  ],\n  \"recommendation\": {\n    \"action\": \"Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.\",\n    \"rationale\": \"Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.\",\n    \"confidence\": 0.58,\n    \"review_required\": true,\n    \"next_steps\": [\n      \"Collect loan parameters (amount/term) and verify income and obligations.\",\n      \"Apply internal policy thresholds and document adverse action reasoning if declined.\",\n      \"If approved, cap amount and require autopay / monitoring per policy.\"\n    ]\n  },\n  \"consistency_check\": {\n    \"checks\": [\n      \"Blocking clarification is set; recommendation requires review.\",\n      \"Risks are mitigated by verification and exposure limits.\"\n    ],\n    \"passed\": true,\n    \"notes\": \"Cannot finalize without loan size/term and verified income obligations.\"\n  }\n}\n```"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Audit-ready decision artifacts for LLM outputs - assumptions, risks, recommendation, and review gating (schema-valid JSON). Replaced with https://clawhub.ai/dgr-ai-labs/plugins/openclaw-dgr-gate) Skill: Decision-Grade Reasoning (DGR) Owner: dgr-ai-labs Summary: Audit-ready decision artifacts for LLM outputs - assumptions, risks, recommendation, and review gating (schema-valid JSON). 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