What makes Decision-Grade Reasoning (DGR) notable?

4.5K downloads. Trust evidence available

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agentCLAWHUBUnverified

Decision-Grade Reasoning (DGR)

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). 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

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

4.5k

Updated

Oct 9, 2026

Version

1.1.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 4.5K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 4.5K downloadsadoption · observed Oct 9, 2026
Latest release: 1.1.1release · observed Sep 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s179065e52s0w3vtq1qbzc9feh8eynaw:dgr

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-dgr-ai-labs-dgr/snapshot"

Documentation

CLAWHUB

146,254 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: dgr
version: "1.1.0"
description: Superseded reference skill: structured reasoning records, kept for provenance.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
homepage: https://www.clawhub.ai/sapenov/dgr
metadata:
  openclaw:
    emoji: "🧭"
  category: "reasoning"
---

> **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.
>
> **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.
>
> This page remains published as the record of the earlier approach and why it was replaced. Existing installs keep working; no further development.

# DGR — Decision‑Grade Reasoning (Governance Protocol)

**Purpose:** produce an auditable, machine‑validated decision record for review and storage.

**Slug:** dgr · **Version:** 1.1.0 · **Modes:** dgr_min / dgr_full / dgr_strict · **Output:** schema-valid JSON

## What this skill does
This 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.

That 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.

## What this skill is NOT (non‑claims)
This skill does **NOT** guarantee:
- correctness, optimality, or truth,
- elimination of hallucinations,
- legal/medical/financial advice suitability,
- or regulatory compliance by itself.

DGR improves **process quality** (clarity, traceability, reviewability) — not outcome certainty.

## When to use
Use when you need:
- an auditable record of reasoning,
- explicit assumptions/risks surfaced,
- reviewer‑friendly structure,
- a consistent output format across tasks and models.

## Inputs
- A user request/question (free text).
- Optional: context identifiers (ticket ID, policy name), and desired **mode**: `dgr_min`, `dgr_full`, or `dgr_strict`.

## Outputs
A single JSON artifact matching `schema.json`.

Minimum acceptance criteria (see `schema.json`):
- at least **1 assumption**
- at least **1 risk**
- `recommendation` present
- `consistency_check` present

## Safety / governance boundaries
- Always **ask for clarification** if key decision inputs are missing.
- If the decision is high‑risk, escalate via `recommendation.review_required = true`.
- If unce

_meta.json

{
  "ownerId": "kn73pggn86vrxrgwz3jjtt974180e4kr",
  "slug": "dgr",
  "version": "1.1.1",
  "publishedAt": 1790276695087
}

examples/access_request.md

# Example — Access Request Review (dgr_min)

## Input
A contractor requests admin access to the production database for 2 weeks to debug an incident. Approve or deny?

## Output (JSON)
```json
{
  "meta": {
    "artifact_id": "00000000-0000-4000-8000-000000000001",
    "spec_version": "1.0.0",
    "created_at": "2026-02-03T12:00:00Z",
    "mode": "dgr_min",
    "task_class": "access_control"
  },
  "input": {
    "query_hash": "sha256:EXAMPLE",
    "query_summary": "Evaluate a contractor request for temporary admin access to a production database for incident debugging."
  },
  "clarifications": [
    {
      "question": "Which exact systems/tables are needed and what actions must be performed?",
      "why_needed": "Least-privilege scope cannot be determined without precise access needs.",
      "blocking": true
    }
  ],
  "assumptions": [
    {
      "statement": "The contractor has a valid business need related to an active incident.",
      "impact_if_wrong": "Unnecessary elevated access increases breach and compliance risk."
    }
  ],
  "risks": [
    {
      "risk": "Overbroad admin access could enable data exfiltration or destructive actions.",
      "severity": "high",
      "mitigation": "Use time-bound, scoped access; require MFA; enforce query logging and approval."
    }
  ],
  "recommendation": {
    "action": "Conditionally approve only after scoping to least privilege; otherwise deny admin-level access.",
    "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.",
    "confidence": 0.62,
    "review_required": true,
    "next_steps": [
      "Collect exact scope of access required (systems, actions, time window).",
      "Implement time-bound role with MFA and audit logging.",
      "Require incident manager approval and post-access review."
    ]
  },
  "consistency_check": {
    "checks": [
      "Assumptions and risks align with recommendation to scope access.",
      "Clarification is marked blocking and recommendation sets review_required."
    ],
    "passed": true,
    "notes": "Decision deferred pending least-privilege scoping."
  }
}
```

examples/incident_triage.md

# Example — Incident Triage (dgr_full)

## Input
Production 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?

## Output (JSON)
```json
{
  "meta": {
    "artifact_id": "00000000-0000-4000-8000-000000000002",
    "spec_version": "1.0.0",
    "created_at": "2026-02-03T12:00:00Z",
    "mode": "dgr_full",
    "task_class": "incident_response"
  },
  "input": {
    "query_hash": "sha256:EXAMPLE",
    "query_summary": "Decision on whether to rollback recent deployment vs. continue debugging production API errors affecting customer purchases."
  },
  "clarifications": [
    {
      "question": "What is the error pattern - specific endpoints, user segments, or geographic regions?",
      "why_needed": "Error scope affects rollback vs. targeted fix decision.",
      "blocking": false
    }
  ],
  "decomposition": [
    "Assess current business impact and customer exposure",
    "Evaluate rollback feasibility and time-to-recovery",
    "Consider debugging complexity and available resources",
    "Factor in deployment pipeline and testing coverage"
  ],
  "assumptions": [
    {
      "statement": "The deployment 2 hours ago introduced the errors (correlation observed).",
      "impact_if_wrong": "Rollback may not resolve the issue and could delay actual root cause remediation."
    },
    {
      "statement": "Rollback can be completed safely within 15-30 minutes.",
      "impact_if_wrong": "Extended downtime or additional system instability during rollback process."
    }
  ],
  "risks": [
    {
      "risk": "Continued debugging delays customer purchase recovery and increases revenue loss.",
      "severity": "high",
      "mitigation": "Set explicit debug time limit (30 min max) before mandatory rollback decision."
    },
    {
      "risk": "Hasty rollback could introduce different issues or data inconsistencies.",
      "severity": "medium",
      "mitigation": "Verify rollback safety with database state checks and staged deployment approach."
    }
  ],
  "recommendation": {
    "action": "Execute rollback immediately while maintaining parallel debug effort.",
    "rationale": "15% error rate on purchase flow represents significant business impact. Rollback provides fastest path to customer service restoration with manageable risk.",
    "confidence": 0.78,
    "review_required": false,
    "next_steps": [
      "Begin rollback process with database consistency verification",
      "Continue debugging in parallel using logs and monitoring data",
      "Implement additional monitoring before next deployment",
      "Conduct post-incident review within 48 hours"
    ]
  },
  "consistency_check": {
    "checks": [
      "Business impact (purchase failures) justifies rollback urgency",
      "Assumptions about deployment correlation and rollback safety are reasonable",
      "Risk mitigation strategies align w

examples/loan_preapproval.md

# Example — Loan Pre-Approval (dgr_full)

## Input
Should we pre-approve a personal loan applicant with 680 credit score, 40% DTI, and 2 late payments in the last 12 months?

## Output (JSON)
```json
{
  "meta": {
    "artifact_id": "00000000-0000-4000-8000-000000000002",
    "spec_version": "1.0.0",
    "created_at": "2026-02-03T12:05:00Z",
    "mode": "dgr_full",
    "task_class": "credit_decision"
  },
  "input": {
    "query_hash": "sha256:EXAMPLE2",
    "query_summary": "Assess personal loan pre-approval given credit score 680, DTI 40%, and two recent late payments."
  },
  "clarifications": [
    {
      "question": "What are the loan amount, term, interest rate policy, and the applicant’s income stability (employment tenure, volatility)?",
      "why_needed": "Risk depends strongly on exposure size, affordability, and stability of cash flow.",
      "blocking": true
    }
  ],
  "decomposition": [
    "Evaluate affordability (DTI, income stability, loan size/term).",
    "Evaluate credit behavior (late payments, trend, utilization).",
    "Check policy thresholds and adverse action requirements.",
    "Decide conditional approval vs decline vs request more info."
  ],
  "assumptions": [
    {
      "statement": "The applicant’s reported DTI is accurate and verified.",
      "impact_if_wrong": "Understated obligations could make the loan unaffordable and increase default risk."
    }
  ],
  "risks": [
    {
      "risk": "Recent delinquencies may indicate deteriorating financial stability.",
      "severity": "medium",
      "mitigation": "Require verification, consider smaller amount/shorter term, and price for risk per policy."
    }
  ],
  "recommendation": {
    "action": "Do not pre-approve as-is; request missing affordability inputs and consider conditional approval with limits if policy allows.",
    "rationale": "Score and DTI are borderline; recent delinquencies require additional verification and tighter exposure to avoid unacceptable loss risk.",
    "confidence": 0.58,
    "review_required": true,
    "next_steps": [
      "Collect loan parameters (amount/term) and verify income and obligations.",
      "Apply internal policy thresholds and document adverse action reasoning if declined.",
      "If approved, cap amount and require autopay / monitoring per policy."
    ]
  },
  "consistency_check": {
    "checks": [
      "Blocking clarification is set; recommendation requires review.",
      "Risks are mitigated by verification and exposure limits."
    ],
    "passed": true,
    "notes": "Cannot finalize without loan size/term and verified income obligations."
  }
}
```

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

About

Avoid when

Public facts

Install and run

Documentation

Extracted files

SKILL.md

_meta.json

examples/access_request.md

examples/incident_triage.md

examples/loan_preapproval.md

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