| name | llm-wiki-cadence-governance |
| description | Weekly governance workflow for keeping an llm-wiki repository current, code-development-useful, and connected to actionable GitHub issue planning. |
LLM Wiki Cadence Governance
Use this skill when reviewing an llm-wiki repository, comparing it against current LLM/AI engineering concepts, or opening issues to keep the wiki useful for code development and repo architecture decisions.
Trigger Conditions
- User asks to review, refresh, or maintain an
llm-wiki repository.
- User asks for a weekly cadence to keep AI/LLM concepts current.
- User asks what GitHub issues should be opened from an llm-wiki architecture/content gap review.
- A session produces llm-wiki maintenance artifacts that need durable closeout or restart handoff.
Core Principle
Treat llm-wiki as a development leverage system, not a passive knowledge base. Every review should connect external concept freshness, internal repo architecture gaps, and actionable GitHub issues that move engineering work forward.
Weekly Cadence Workflow
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Repository architecture inventory
- Inspect current information architecture: domains, concept pages, source maps, graph/manifest generation, validation scripts, and docs reports.
- Identify stale pages, orphaned nodes, weak cross-links, missing source citations, and concepts not tied to code-development use cases.
-
External concept freshness scan
- Review current LLM/agent engineering topics relevant to the workspace: agents, tool use, RAG, context engineering, evals, model-routing, inference/serving, structured outputs, safety, data governance, and code-agent workflows.
- Capture concepts as candidate deltas, not undigested research dumps.
-
Code-development usefulness mapping
- For each candidate gap, ask: what repo decision, code pattern, evaluation, documentation contract, or automation pipeline would this improve?
- Prioritize items that improve engineering velocity, correctness, agent routing, or reusable architecture guidance.
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Issue portfolio creation
- Open de-duplicated GitHub issues only after checking existing issues and plan state.
- Prefer issue clusters by class: freshness pipeline, graph/index quality, source/citation governance, domain pages, code integration guides, eval/readiness scorecards.
- Keep implementation gated by the workspace issue workflow: plan → adversarial review → user approval → TDD implementation.
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Validation and closeout
- Run the repo's available tests/validators for graph manifests, schemas, docs, and legal/public-safety gates before claiming readiness.
- If closeout cannot finish, preserve restart state in a repo-tracked handoff with exact validation evidence, dirty files, issue status, and the next checkpoint.
Issue Quality Checklist
A good llm-wiki maintenance issue includes:
- Clear problem statement tied to code-development leverage.
- Current evidence path: repo files, generated reports, graph metrics, issue links, or source pages.
- Scope boundaries: what is in/out for this issue.
- Acceptance criteria with validation commands or report artifacts.
- Public/private data classification where source material may cross governance boundaries.
- Dependencies on existing architecture contracts, graph schema, citation model, or source-ingest workflows.
Pitfalls
- Do not create a flat backlog of generic “add topic X” issues. Tie each issue to a reusable architecture or development outcome.
- Do not treat external AI trends as authoritative without source provenance and update dates.
- Do not bypass issue planning gates just because the task is documentation-heavy.
- Do not close an llm-wiki issue after only generating reports; verify that committed files, pushed state, issue labels/comments, and tests all match the claimed closeout.
- Do not lose partial closeout state. If the session ends before commit/push/issue close, write a handoff first.
Support References
references/issue-closeout-handoff-pattern.md — concise restart-handoff pattern from an llm-wiki public-graph issue closeout where validation passed but implementation files still needed final commit and issue closure.