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Use when the user mentions AI coding, Agent coding, architecture collapse, Harness Engineering, design intent, acceptance rules, golden rules, architecture tests, or wants the codebase to be safer for AI Agent modifications.\n---\n\n# AI Coding Architecture Guardrails\n\n## Goal\n\nHelp coding Agents modify large or complex projects without breaking existing architecture, core functionality, or long-term design intent.\n\nWhen using this Skill, treat the repository as the source of truth, but treat human-maintained design intent as the highest-level guidance. Do not rely on long conversation history for critical context; key rules must be captured in repo docs or executable checks.\n\n## Core model\n\nUse a four-layer guardrail model:\n\n```text\n1. Human design-intent layer\n2. Agent-synced architecture and acceptance docs layer\n3. Hard automated constraints layer\n4. Human review and golden-rules feedback layer\n```\n\nThe Agent’s job is not free-form improvisation, but safe execution within clear boundaries and feedback loops.\n\n## Recommended doc layout\n\nWhen creating or improving guardrails for a project, prefer this minimal structure:\n\n```text\nAGENTS.md\ndocs/DESIGN_INTENT.md\ndocs/ARCHITECTURE.md\ndocs/ACCEPTANCE_RULES.md\ndocs/GOLDEN_RULES.md\ndocs/ARCHITECTURE_DRIFT.md\n```\n\n`DESIGN_INTENT.md` is maintained by humans. It records project goals, core architectural principles, non-negotiable tradeoffs, and historical design decisions. It is the “constitution”; do not let the Agent overwrite human intent with the current implementation.\n\n`ARCHITECTURE.md` records the currently confirmed architecture map. It may be periodically synced by the Agent from code and `DESIGN_INTENT.md`, but accidental drift must not be automatically legitimized.\n\n`ACCEPTANCE_RULES.md` records how to verify core features, architectural commitments, and non-regression behavior.\n\n`GOLDEN_RULES.md` records strong rules distilled from real incidents. Each rule should include incident source, forbidden behavior, required behavior, and how to enforce it automatically.\n\n`ARCHITECTURE_DRIFT.md` records gaps between design intent and current implementation, classified as: aligned with intent, reasonable evolution, technical debt, needs human decision, or violates design.\n\n## Before you start coding\n\nBefore any non-trivial code change:\n\n1. Read `AGENTS.md` if it exists.\n2. Read `docs/DESIGN_INTENT.md`, `docs/ARCHITECTURE.md`, `docs/ACCEPTANCE_RULES.md`, and `docs/GOLDEN_RULES.md` if they exist.\n3. Clarify architectural boundaries and behaviors this change must not break.\n4. State the scope of this change before editing.\n5. Do not proactively perform large refactors, renames, migrations, or abstraction overhauls unless the user explicitly asks.\n\nIf the project has no guardrail docs yet, create a minimal viable set first; do not invent a huge documentation system in one shot.\n\n## Design-intent maintenance workflow\n\nHuman design intent is the top anchor. When updating `DESIGN_INTENT.md`:\n\n1. Preserve historical decisions; do not simply overwrite old content.\n2. Append new intent with dated decision records.\n3. Record *why* something changed, not only *what* changed.\n4. Keep it short enough for the Agent to read before coding.\n5. Do not allow the Agent to replace human design tradeoffs with implementation convenience.\n\nRecommended format:\n\n```markdown\n## YYYY-MM-DD - [Decision title]\n\nDesign intent:\n[What the system must preserve or evolve toward.]\n\nRationale:\n[Why this direction matters.]\n\nImpact:\n- [Constraints future changes must obey.]\n- [What the Agent must not break.]\n```\n\n## Architecture sync workflow\n\nAfter a phase of work completes, or before a major task starts, sync architecture docs:\n\n1. Read `DESIGN_INTENT.md`.\n2. Inspect relevant code.\n3. Compare design intent with current code.\n4. Classify each gap:\n   - `aligned with intent`\n   - `reasonable evolution`\n   - `technical debt`\n   - `needs human decision`\n   - `violates design`\n5. Write only confirmed architecture into `ARCHITECTURE.md`.\n6. Write unconfirmed or suspicious gaps into `ARCHITECTURE_DRIFT.md`.\n\nDo not assume “current code is the correct architecture.” Current code may already have collapsed or drifted.\n\n## Architecture acceptance tests\n\nTurn architectural commitments into executable checks. For each architectural cornerstone:\n\n1. Identify cornerstone nodes from `ARCHITECTURE.md`.\n2. From code, find observable call chains, data flows, or invariants that prove the cornerstone still holds.\n3. Write focused unit tests, integration tests, lint rules, or structural checks.\n4. Add checks to the normal verification workflow.\n\nExample:\n\n```text\nArchitectural cornerstone:\nPlanAgent must maintain multi-turn persistent memory.\n\nObservable pattern:\nAfter 10 consecutive dialogue turns, retrieve, summarize/update, and persist in the memory-management chain must run.\n\nAcceptance test:\nSimulate 10 dialogue turns; assert expected MemoryManager methods were called and results entered persistence or context assembly.\n```\n\nPrefer deterministic checks over AI judgment alone:\n\n```text\nType checking > unit tests > integration tests > architecture tests > lint > CI > AI review > prompt reminders\n```\n\nAI review is only a semantic supplement, not a core guardrail.\n\n## Hard-constraint priorities\n\nWhen building guardrails, prioritize preventing:\n\n- Cross-layer calls or imports that violate architectural direction\n- Bypassing core service, repository, memory manager, validator, or provider\n- Competing duplicate implementations for existing subsystems\n- Deleting, weakening, or circumventing regression tests\n- Changing external behavior without updating acceptance rules\n- Replacing stable abstractions with direct data access\n- Unbounded complexity, global state, or hidden side effects\n\nIf the project has clear layering, encode allowed dependency direction as tests or lint rules.\n\n## Golden-rules workflow\n\nWhen existing guardrails fail to stop architecture collapse, feature regression, or drift:\n\n1. Summarize the incident.\n2. Determine why existing docs or tests did not prevent it.\n3. Add a rule to `GOLDEN_RULES.md`.\n4. Add or propose a deterministic check to enforce the rule.\n5. Link the rule to tests, lint, CI, or a review checklist.\n\nRecommended format:\n\n```markdown\n## [Rule title]\n\nIncident source:\n[What happened and when.]\n\nForbidden behavior:\n[What the Agent must not do again.]\n\nRequired behavior:\n[Correct architectural behavior.]\n\nEnforcement:\n- [Tests, lint, CI checks, or review steps.]\n```\n\nDo not turn `GOLDEN_RULES.md` into generic advice. Keep it short, high-signal, and grounded in real incidents or high-risk patterns.\n\n## Collapse-risk review\n\nAfter the Agent completes a meaningful change, before the final reply, check:\n\n1. Did this change alter architectural boundaries?\n2. Did it bypass existing abstractions?\n3. Did it delete, weaken, or avoid existing tests?\n4. Did it change core call chains?\n5. Did it introduce competing implementations for an existing subsystem?\n6. Did it move responsibilities to the wrong module?\n7. Where should human review focus first?\n\nIf any answer indicates risk, either fix it or record it as architectural drift needing human decision.\n\n## Output expectations\n\nWhen the user asks to design guardrails, deliver:\n\n- Minimal set of new documents to add\n- Architectural invariants that should be encoded as hard constraints\n- First batch of tests, lint, or structural checks to implement\n- Golden-rules feedback workflow\n- Short rollout plan\n\nWhen the user asks to apply guardrails to code, read existing docs and tests first, make small scoped changes, and validate with the project’s strongest automated checks.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn76tv8qxh5ap072zm27re50gh835yje\",\n  \"slug\": \"ai-architecture-harness-en\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1779204728973\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nEstablish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iterations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[hgvgfgvh](https://clawhub.ai/user/hgvgfgvh)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineering teams use this skill to establish repository guardrails for AI-assisted coding, including design-intent docs, architecture acceptance rules, automated constraints, and golden-rule review loops.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill may propose or update architecture guardrail documents and tests in a repository.\n\nMitigation: Keep documentation and test changes scoped to the project, and review them before adopting them as constraints.\n\nRisk: Overbroad architecture rules can freeze reasonable design evolution or encode incorrect assumptions.\n\nMitigation: Start with a minimal guardrail set, preserve human-maintained design intent, and classify uncertain drift for human decision.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/hgvgfgvh/skills/ai-architecture-harness-en)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown with optional code and shell command blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create or update project documentation and tests when the user asks to apply guardrails.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\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.","readmeExcerpt":"Skill: ai-architecture-harness-en Owner: hgvgfgvh Summary: Establish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iter... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-19T15:32:08.973Z | user - Initial release of ai-architecture-harness skill. - Provides a 4-layer model for architectural guardrails in AI-assisted ","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"1. Human design-intent layer\n2. Agent-synced architecture and acceptance docs layer\n3. Hard automated constraints layer\n4. Human review and golden-rules feedback layer"},{"language":"text","snippet":"AGENTS.md\ndocs/DESIGN_INTENT.md\ndocs/ARCHITECTURE.md\ndocs/ACCEPTANCE_RULES.md\ndocs/GOLDEN_RULES.md\ndocs/ARCHITECTURE_DRIFT.md"},{"language":"markdown","snippet":"## YYYY-MM-DD - [Decision title]\n\nDesign intent:\n[What the system must preserve or evolve toward.]\n\nRationale:\n[Why this direction matters.]\n\nImpact:\n- [Constraints future changes must obey.]\n- [What the Agent must not break.]"},{"language":"text","snippet":"Architectural cornerstone:\nPlanAgent must maintain multi-turn persistent memory.\n\nObservable pattern:\nAfter 10 consecutive dialogue turns, retrieve, summarize/update, and persist in the memory-management chain must run.\n\nAcceptance test:\nSimulate 10 dialogue turns; assert expected MemoryManager methods were called and results entered persistence or context assembly."},{"language":"text","snippet":"Type checking > unit tests > integration tests > architecture tests > lint > CI > AI review > prompt reminders"},{"language":"markdown","snippet":"## [Rule title]\n\nIncident source:\n[What happened and when.]\n\nForbidden behavior:\n[What the Agent must not do again.]\n\nRequired behavior:\n[Correct architectural behavior.]\n\nEnforcement:\n- [Tests, lint, CI checks, or review steps.]"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: ai-architecture-harness\ndescription: Establish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iterations. Use when the user mentions AI coding, Agent coding, architecture collapse, Harness Engineering, design intent, acceptance rules, golden rules, architecture tests, or wants the codebase to be safer for AI Agent modifications.\n---\n\n# AI Coding Architecture Guardrails\n\n## Goal\n\nHelp coding Agents modify large or complex projects without breaking existing architecture, core functionality, or long-term design intent.\n\nWhen using this Skill, treat the repository as the source of truth, but treat human-maintained design intent as the highest-level guidance. Do not rely on long conversation history for critical context; key rules must be captured in repo docs or executable checks.\n\n## Core model\n\nUse a four-layer guardrail model:\n\n```text\n1. Human design-intent layer\n2. Agent-synced architecture and acceptance docs layer\n3. Hard automated constraints layer\n4. Human review and golden-rules feedback layer\n```\n\nThe Agent’s job is not free-form improvisation, but safe execution within clear boundaries and feedback loops.\n\n## Recommended doc layout\n\nWhen creating or improving guardrails for a project, prefer this minimal structure:\n\n```text\nAGENTS.md\ndocs/DESIGN_INTENT.md\ndocs/ARCHITECTURE.md\ndocs/ACCEPTANCE_RULES.md\ndocs/GOLDEN_RULES.md\ndocs/ARCHITECTURE_DRIFT.md\n```\n\n`DESIGN_INTENT.md` is maintained by humans. It records project goals, core architectural principles, non-negotiable tradeoffs, and historical design decisions. It is the “constitution”; do not let the Agent overwrite human intent with the current implementation.\n\n`ARCHITECTURE.md` records the currently confirmed architecture map. It may be periodically synced by the Agent from code and `DESIGN_INTENT.md`, but accidental drift must not be automatically legitimized.\n\n`ACCEPTANCE_RULES.md` records how to verify core features, architectural commitments, and non-regression behavior.\n\n`GOLDEN_RULES.md` records strong rules distilled from real incidents. Each rule should include incident source, forbidden behavior, required behavior, and how to enforce it automatically.\n\n`ARCHITECTURE_DRIFT.md` records gaps between design intent and current implementation, classified as: aligned with intent, reasonable evolution, technical debt, needs human decision, or violates design.\n\n## Before you start coding\n\nBefore any non-trivial code change:\n\n1. Read `AGENTS.md` if it exists.\n2. Read `docs/DESIGN_INTENT.md`, `docs/ARCHITECTURE.md`, `docs/ACCEPTANCE_RULES.md`, and `docs/GOLDEN_RULES.md` if they exist.\n3. Clarify architectural boundaries and behaviors this change must not break.\n4. State the scope of this change before editing.\n5. Do not proactively perform large refactors, renames, migrations, or abstraction overhauls unless the user explicitly asks.\n\nIf the project has no guardrail docs yet, create a "},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn76tv8qxh5ap072zm27re50gh835yje\",\n  \"slug\": \"ai-architecture-harness-en\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1779204728973\n}"},{"path":"skill-card.md","content":"## Description:\n\nEstablish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iterations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[hgvgfgvh](https://clawhub.ai/user/hgvgfgvh)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineering teams use this skill to establish repository guardrails for AI-assisted coding, including design-intent docs, architecture acceptance rules, automated constraints, and golden-rule review loops.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill may propose or update architecture guardrail documents and tests in a repository.\n\nMitigation: Keep documentation and test changes scoped to the project, and review them before adopting them as constraints.\n\nRisk: Overbroad architecture rules can freeze reasonable design evolution or encode incorrect assumptions.\n\nMitigation: Start with a minimal guardrail set, preserve human-maintained design intent, and classify uncertain drift for human decision.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/hgvgfgvh/skills/ai-architecture-harness-en)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration, Guidance]\n\n**Output Format:** [Markdown with optional code and shell command blocks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create or update project documentation and tests when the user asks to apply guardrails.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\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."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Establish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iter... Skill: ai-architecture-harness-en Owner: hgvgfgvh Summary: Establish and use architectural guardrails for AI-assisted coding to prevent architecture collapse, feature regression, and drift across long multi-turn iter... 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