ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Document decisions, not just implementations. ADRs for architectural choices, inline docs for non-obvious code, and runbooks for operational knowledge.
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Document decisions, not just implementations. ADRs for architectural choices, inline docs for non-obvious code, and runbooks for operational knowledge.
name: documentation description: Document decisions, not just implementations. ADRs for architectural choices, inline docs for non-obvious code, and runbooks for operational knowledge. category: review applies-to: [claude, gemini, cursor, copilot, any] version: 1.0.0
Code explains what. Documentation explains why. The most valuable documentation records decisions that aren't obvious from reading the code: why this architecture, why this tradeoff, why not the obvious alternative.
For every significant architectural decision: 1. Write an ADR with:
2. Store ADRs in `docs/decisions/` as numbered markdown files.
**Verify:** Every significant decision in the last sprint has an ADR.
3. Document the WHY, not the WHAT:
4. Document non-obvious algorithmic choices. 5. Document external constraints (rate limits, API quirks, platform limitations). 6. Remove comments that state the obvious — they add noise.
**Verify:** Every non-obvious code block has a "why" comment.
7. For every production process that humans execute, write a runbook:
8. Runbooks live in `docs/runbooks/`.
**Verify:** Every on-call alert has a linked runbook.
9. README reflects current state (not v1 state). 10. Setup instructions work on a fresh machine. 11. Architecture diagram updated after significant changes.
| Excuse | Rebuttal | |--------|----------| | "The code is self-documenting" | Code says what; documentation says why. Both are needed. | | "I'll document it later" | The context in your head right now is irreplaceable. Write it now. | | "Docs go stale" | Outdated docs are better than no docs. Update when you touch the code. |
AI agent skills for production grade applications
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Design stable, versioned, self-documenting APIs. Easy to use correctly, hard to use incorrectly. Apply Hyrum's Law from day one.
Automated quality gates from commit to production. Every merge to main is potentially shippable. No manual steps in the deployment path.
Get layered, context-aware explanations of unfamiliar code. Understand what it does, why it was written that way, and how to work with it safely.
Structured code review focusing on correctness, security, and maintainability. Correctness before style. Every reviewer comment must be actionable.
Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.