docs-ai-prd

name	docs-ai-prd
description	Writes PRDs and specs optimized for coding assistants. Use when authoring requirements or project context for Claude Code, Cursor, or Copilot.

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/decision; merge duplicate drafts into it.
4. Convert long research notes into short evidence-backed claims in canonical docs; keep links/dates for external facts.
5. Maintain a compact canonical library for LLMs with root anchors: AGENTS.md (agent instructions) and README.md (human + AI entrypoint), then link deeper specs from docs/.
6. Delete integrated drafts by delete_by date; do not keep .archive/ mirrors in docs/ unless compliance explicitly requires retention.

Quick Reference

PRDs & Specs

Task	Template
PRD creation	assets/prd/prd-template.md
Tech spec	assets/spec/tech-spec-template.md
Planning checklist	assets/planning/planning-checklist.md
Story mapping	assets/stories/story-mapping-template.md
Gherkin/BDD	assets/stories/gherkin-example-template.md
AI PRD	assets/prd/ai-prd-template.md

Project Context (CLAUDE.md)

Context Type	Template	Priority
Architecture	assets/architecture-context.md	Critical
Conventions	assets/conventions-context.md	High
Key Files	assets/key-files-context.md	Critical
Minimal Start	assets/minimal-claudemd.md	5-min
Cross-Tool	assets/cross-tool-context.md	Multi-tool

---

Decision Tree

User needs:
    ├─► AI-Assisted Coding?
    │   ├─ Non-trivial (>3 files)? → Planning checklist + agentic session
    │   └─ Simple (<3 files)? → Direct implementation
    │
    ├─► Repo has a docs folder with LLM-generated research/feature docs?
    │   └─ Use Docs Folder + LLM Iteration Option, then validate with qa-docs-coverage
    │
    ├─► Project Onboarding?
    │   ├─ New to codebase? → Generate CLAUDE.md
    │   └─ Quick context? → Minimal CLAUDE.md
    │
    └─► Traditional PRD?
        ├─ Product requirements? → PRD template
        ├─ AI feature? → AI PRD template
        └─ Acceptance criteria? → Gherkin/BDD

---

Cross-Tool Context Files

Tool	Location	Notes
Claude Code	CLAUDE.md, .claude/	Auto-loaded
Cursor	.cursor/rules/	Project rules
Copilot	.github/copilot-instructions.md	Workspace context
Generic	AGENTS.md	Tool-agnostic

---

CLAUDE.md / AGENTS.md Guidance

• Start minimal: assets/minimal-claudemd.md
• Add only what’s needed: assets/architecture-context.md, assets/conventions-context.md, assets/key-files-context.md, assets/dependencies-context.md, assets/tribal-knowledge-context.md
• Keep it executable: commands must run; include no secrets; prefer file paths over pasted code

---

Do / Avoid

Do

• Start with executive summary (decision, users, scope, success)
• Define acceptance criteria in testable language
• Keep requirements unambiguous (must/should/may)
• Link to supporting docs instead of pasting

Avoid

• Vague requirements ("fast", "easy") without definitions
• Mixing draft notes and final requirements
• Metrics without measurement plan
• Docs with no owner or review cadence
• Dual-state wording that mixes live behavior, target behavior, and migration behavior in one statement

---

LLM Ambiguity Gate (Required for planning docs)

• Label every behavior as exactly one of: Live now, Target, or Transition (with owner + end condition).
• Label every metric as either Reference signal or Release blocker.
• Define one canonical feature-gating contract per feature; all other docs must link to it instead of restating variants.
• Keep assumptions/open questions separate from final decisions.
• If conflicts exist across docs, mark one canonical source and add follow-up tasks to resolve mirrors.

---

Context Extraction

Use:

• references/architecture-extraction.md for components/data flows
• references/convention-mining.md for naming/patterns
• references/tribal-knowledge-recovery.md for git-history “why”
• references/docs-audit-commands.md for audit commands and tool fallbacks

---

Quality Checklist

PRD Quality

• Clear problem statement
• Measurable success criteria
• Unambiguous acceptance criteria
• Edge cases documented
• AI can execute without clarification
• Every behavior is labeled Live now, Target, or Transition
• Metrics are labeled Reference signal or Release blocker
• Each feature-gating rule has one canonical source (no conflicting duplicates)

CLAUDE.md Quality

• Architecture reflects actual structure
• Key files exist at listed locations
• Conventions match actual patterns
• Commands actually work
• No sensitive information

---

Resources

Resource	Purpose
references/agentic-coding-best-practices.md	AI coding patterns
references/requirements-checklists.md	PRD validation
references/traditional-prd-writing.md	Classic PRD format
references/architecture-extraction.md	Mining architecture
references/convention-mining.md	Extracting conventions
references/tribal-knowledge-recovery.md	Git history analysis
references/docs-audit-commands.md	Audit shell commands
references/stakeholder-alignment.md	Stakeholder buy-in, RACI, conflict resolution
references/acceptance-criteria-patterns.md	Testable ACs, BDD, edge case coverage
references/prd-review-facilitation.md	Running PRD reviews, feedback categorization
data/sources.json	Curated external sources

Templates

Category	Templates
PRDs	prd-template, ai-prd-template, tech-spec-template
Planning	planning-checklist, agentic-session-template
Stories	story-mapping-template, gherkin-example-template
Context	architecture, conventions, key-files, minimal-claudemd
Stack-specific	nodejs-context, python-context, react-context, go-context

Related Skills

Skill	Purpose
docs-codebase	README, API docs, ADRs
qa-docs-coverage	Documentation gaps
product-management	Product strategy
software-architecture-design	System design

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
• Prefer primary sources; report source links and dates for volatile information.
• If web access is unavailable, state the limitation and mark guidance as unverified.