prp-project-context

name	prp-project-context
description	Generate PROJECT.md from code analysis, PRDs, and business context. Creates compact project documentation for AI token efficiency and human readability.
metadata	{"short-description":"Generate PROJECT.md"}

Agent Mode Detection

If your input context contains [WORKSPACE CONTEXT] (injected by a multi-agents framework), you are running as a sub-agent. Apply these optimizations:

• Skip CLAUDE.md reading — already loaded by parent session.

All other phases run unchanged.

---

PRP Project Context — Generate PROJECT.md

Input

Arguments: $ARGUMENTS

Format: [--from-prd <path>] [--from-business-context <path>]

Mission

Generate a PROJECT.md file that serves both AI and human readers. This is a compact project context document that answers: what is this project, why does it exist, and how should I navigate it?

Key Principles:

• Human writes "why" (problem, decisions, constraints) — AI maintains "what" (architecture, context map, exports)
• AUTO-GEN sections are updated automatically by gen-ai-context.sh
• Non-AUTO-GEN sections require human review
• Prefer bullets over prose, paths over descriptions, commands over explanations

---

Phase 1: GATHER — Collect Context from Existing Sources

1.1 Read Existing Documentation

Check and read these files if they exist:

# Project docs
cat CLAUDE.md 2>/dev/null
cat README.md 2>/dev/null
cat ARCHITECTURE.md 2>/dev/null

# PRP artifacts
ls .prp-output/prds/*.md 2>/dev/null
ls .prp-output/prds/drafts/*.md 2>/dev/null
cat .prp-output/BUSINESS-CONTEXT.md 2>/dev/null

# Other docs
ls docs/*.md 2>/dev/null

1.2 Analyze Codebase

# Directory structure
find . -maxdepth 2 -type d | grep -v node_modules | grep -v .git | grep -v __pycache__

# Tech stack indicators
ls package.json requirements.txt pyproject.toml go.mod Cargo.toml 2>/dev/null

# Entry points
ls main.py app.py server.py src/index.ts src/index.js bin/* 2>/dev/null

# API routes (if any)
grep -rn '@app\.\(get\|post\|put\|delete\)' --include="*.py" . 2>/dev/null | head -10
grep -rn 'router\.\(get\|post\|put\|delete\)' --include="*.ts" --include="*.js" . 2>/dev/null | head -10

# Test coverage
ls test/ tests/ __tests__/ 2>/dev/null
find . -name "test_*.py" -o -name "*.test.ts" -o -name "*.spec.ts" 2>/dev/null | wc -l

1.3 Extract from PRDs (if --from-prd provided or .prp-output/prds/ exists)

From PRD, extract:

• Problem Statement → PROJECT.md "Problem" section
• Users & Context → PROJECT.md "Users" section
• MoSCoW priorities → PROJECT.md "Requirements" section
• Key Hypothesis → PROJECT.md "What & Why" one-liner context
• Decisions Log → PROJECT.md "Key Decisions" section

1.4 Extract from Business Context (if available)

From BUSINESS-CONTEXT.md, extract:

• Product description → "What & Why" section
• Target customer → "Users" section
• Differentiation → "Key Decisions" context
• Constraints → "Constraints & Gotchas" section

PHASE_1_CHECKPOINT:

• CLAUDE.md read (if exists)
• README.md read (if exists)
• PRDs checked and extracted (if exist)
• BUSINESS-CONTEXT checked (if exists)
• Codebase structure analyzed
• Tech stack identified

---

Phase 2: SYNTHESIZE — Draft PROJECT.md Sections

2.1 Draft Human Sections

Write draft content for sections that need human review:

Section	Source Priority
What & Why	PRD problem statement > README first paragraph > CLAUDE.md description
Problem	PRD evidence + problem statement > README "why" section
Users	PRD users & context > BUSINESS-CONTEXT target customer > CLAUDE.md
Requirements	PRD MoSCoW > README features list > code analysis
Key Decisions	CLAUDE.md architecture decisions > PRD decisions log > code patterns
Constraints	PRD risks > CLAUDE.md gotchas > runtime observations

Rules for drafting:

• Use bullets, not paragraphs
• Include {TODO: verify} markers for anything you're unsure about
• For Requirements: mark completed items as [x] based on code analysis
• For Key Decisions: always include "ทำไม" (why) column — guess if needed but mark as {TODO: verify}

2.2 Generate AUTO-GEN Sections

Run the gen-ai-context.sh script to populate auto-generated sections:

# If gen-ai-context.sh is available (installed by prp-framework)
if [ -x "scripts/gen-ai-context.sh" ] || [ -x ".prp/scripts/gen-ai-context.sh" ]; then
    # Script handles: Architecture, Context Map, Exports
    # These will be populated after --init or --update
    echo "AUTO-GEN sections will be populated by gen-ai-context.sh"
fi

If the script is not available, manually generate:

• Architecture: Stack detection + entry points + brief diagram
• Context Map: Directory → task type mapping
• Exports: API endpoints, CLI commands, public functions

PHASE_2_CHECKPOINT:

• All human sections drafted with best-effort content
• {TODO} markers placed for uncertain content
• AUTO-GEN strategy determined

---

Phase 3: GENERATE — Write PROJECT.md

3.1 Template

Use the template from .prp/templates/PROJECT.md.tmpl if available. Otherwise, use this structure:

# PROJECT.md — {project-name}

## What & Why
**One-liner:** {synthesized from Phase 2}
**Status:** {active | maintenance | archived}
**Owner:** {from CLAUDE.md or {TODO}}

## Problem
{synthesized bullets}

## Users
{synthesized bullets}

## Requirements (Living)

### Must Have (P0)
{from PRD or code analysis, [x] for implemented}

### Should Have (P1)
{from PRD or code analysis}

### Nice to Have (P2)
{from PRD or nice-to-have features}

<!-- AUTO-GEN:BEGIN — Do not edit manually. Run: gen-ai-context.sh --update -->
## Architecture (Brief)

- **Stack:** {detected}
- **Entry points:** {detected}

## Context Map

| Task Type | Read These First |
|-----------|-----------------|
{generated from directories}

## Exports

{generated from code}
<!-- AUTO-GEN:END -->

## Key Decisions

| Decision | เลือก | ไม่เลือก | ทำไม |
|----------|-------|---------|------|
{from CLAUDE.md decisions or PRD}

## Constraints & Gotchas

{from PRD risks, CLAUDE.md, observations}

3.2 Write File

Write the generated content to PROJECT.md in the project root.

3.3 Run AUTO-GEN Update

# Populate AUTO-GEN sections with real data
if [ -x "scripts/gen-ai-context.sh" ]; then
    ./scripts/gen-ai-context.sh --update
elif [ -x ".prp/scripts/gen-ai-context.sh" ]; then
    .prp/scripts/gen-ai-context.sh --update
fi

PHASE_3_CHECKPOINT:

• PROJECT.md written to project root
• AUTO-GEN sections populated
• File is valid markdown

---

Phase 4: OUTPUT — Summary & Next Steps

4.1 Summary

## PROJECT.md Generated

**Project:** {name}
**Lines:** {count}
**Sections:** {list of sections}
**Sources used:** {CLAUDE.md, README.md, PRD, BUSINESS-CONTEXT, code analysis}

### Human Review Needed

The following sections contain draft content that should be reviewed:

- [ ] **What & Why** — verify one-liner captures the project's purpose
- [ ] **Problem** — verify pain points are accurate
- [ ] **Users** — verify user list is complete
- [ ] **Requirements** — verify priorities (P0/P1/P2) and completion status
- [ ] **Key Decisions** — verify "ทำไม" column is accurate
- [ ] **Constraints** — add any runtime gotchas not captured

### Auto-Maintained Sections

These sections update automatically via `gen-ai-context.sh --update`:
- Architecture (stack, entry points)
- Context Map (directory → task mapping)
- Exports (endpoints, CLI commands)

### Keeping It Fresh

- `gen-ai-context.sh --check` — validate freshness
- `gen-ai-context.sh --update` — refresh auto-gen sections
- Runs automatically after `$prp-cleanup` (post-merge)

---

Success Criteria

• PROJECT_MD_CREATED: PROJECT.md exists in project root
• HUMAN_SECTIONS_DRAFTED: What & Why, Problem, Users, Requirements, Key Decisions, Constraints all have content
• AUTO_GEN_MARKERS: AUTO-GEN:BEGIN and AUTO-GEN:END markers present
• TODO_MARKERS: Uncertain content marked with {TODO}
• COMPACT: Total file under 200 lines
• NO_PROSE: Bullets and tables preferred over paragraphs