docs-ai-prd

# PRDs & Project Context Create product requirements and project context that humans and coding assistants can execute effectively. **Two capabilities:** 1. **PRDs & Specs** - Requirements, specs, stories, acceptance criteria 2. **Project Context** - Architecture, conventions, tribal knowledge (CLAUDE.md) **Modern Best Practices (Jan 2026)**: Context engineering (right info, right format, right time), decision-first docs, testable requirements with acceptance criteria, metrics with formula + timeframe + data source, cross-tool portability. ## Workflow (Use This Order) 1. Pick the deliverable (PRD, AI PRD, tech spec, story map, CLAUDE.md). 2. Gather inputs (problem evidence, users, constraints, dependencies, risks). 3. Fill the template (write decisions first; keep requirements testable). 4. Validate with checklists (requirements, edge cases, security/compliance as needed). 5. Hand off with next actions (implementation plan, owners, open questions). ### Docs Folder + LLM Iteration Option (Any Repo) Use this when a repository has a `docs/` folder with: - research docs prepared for LLM consumption - feature docs/specs generated by LLMs during implementation Run this flow before finalizing PRDs/specs: 1. Classify each file by purpose (`Tutorial`, `How-to`, `Reference`, `Explanation`) to prevent mixed doc types. 2. Tag each non-canonical file with lifecycle metadata (`status`, `owner`, `last_verified`, `integrates_into`, `delete_by`). 3. Pick one canonical doc per feature/...