project-initialization-phase

name

project-initialization-phase

description

Orchestrates /init-project command execution through interactive questionnaire (15 questions), brownfield codebase scanning (tech stack detection, ERD from migrations), and 8-document generation (overview, architecture, tech-stack, data, API, capacity, deployment, workflow). Use when user runs /init-project, requests project documentation generation, or asks about architecture setup for greenfield/brownfield projects. (project)

Guide execution of the /init-project command to generate 8 comprehensive project-level design documents through interactive questionnaire, brownfield codebase scanning, and cross-document consistency validation. Embodies "Planning is 80% of the project, 20% code" philosophy.

This skill orchestrates one-time project setup, producing foundation documentation that all features must align with.

Core responsibilities:

• Detect project type (greenfield vs brownfield)
• Run interactive questionnaire (15 questions covering vision, scale, tech stack, constraints)
• Scan existing codebase (if brownfield) to auto-fill docs
• Launch project-architect agent to generate 8 documentation files
• Validate cross-document consistency
• Present summary with [NEEDS CLARIFICATION] count

Inputs: User answers (15 questions), existing codebase (if brownfield), templates from .spec-flow/templates/project/ Outputs: 8 markdown files in docs/project/ (overview, architecture, tech-stack, data, API, capacity, deployment, workflow) Expected duration: 15-20 minutes (10 min questions + 5-10 min generation/review)

<quick_start> Execute /init-project workflow in 6 steps:

1. Detect project type - Check for package.json, requirements.txt, etc. (greenfield vs brownfield)
2. Interactive questionnaire - Ask 15 questions (project name, vision, scale, tech stack, constraints)
3. Brownfield scanning (if applicable) - Auto-detect tech stack, ERD from migrations, architecture pattern
4. Launch project-architect agent - Generate 8 docs from templates + answers + scan results
5. Validate documentation - Verify all 8 files exist, [NEEDS CLARIFICATION] count < 20, Mermaid diagrams present
6. Summary report - Show coverage (greenfield: 70% filled, brownfield: 80% filled), next steps

Key principle: Mark unknowns with [NEEDS CLARIFICATION] instead of hallucinating. </quick_start>

<success_criteria> Project initialization phase complete when:

• All 8 files generated in docs/project/ (overview, system-architecture, tech-stack, data-architecture, api-strategy, capacity-planning, deployment-strategy, development-workflow)
• [NEEDS CLARIFICATION] count < 20 (indicates good coverage from questionnaire/scan)
• Mermaid diagrams present and valid (C4 in system-architecture, ERD in data-architecture)
• Cross-document consistency validated (tech stack, database, deployment model aligned)
• Tech stack accurately detected (brownfield only)
• User informed of next steps (review docs, fill clarifications, commit)

If [NEEDS CLARIFICATION] count > 30, re-run questionnaire with more detailed answers or manually fill post-generation. </success_criteria>

**Environment checks**: - Templates exist in `.spec-flow/templates/project/` (8 files: overview-template, system-architecture-template, etc.) - `docs/` directory exists or can be created - Project-architect agent available at `.claude/agents/phase/project-architect.md`

Knowledge requirements:

• Understanding of greenfield vs brownfield projects
• Familiarity with tech stack detection strategies (package.json, requirements.txt parsing)
• ERD generation from database migrations (Alembic, Prisma)
• Cross-document consistency validation patterns

Before running: ⚠️ WARNING: /init-project generates 8 files in docs/project/. If directory already exists, offer user to:

• A) Backup existing docs to docs/project-backup-{timestamp}/
• B) Append to existing docs (merge new sections)
• C) Abort (user will manually update)

**Detect Project Type (Greenfield vs Brownfield)**

Determine if starting from scratch (greenfield) or with existing codebase (brownfield).

Detection Logic:

# Check for existing codebase indicators
if [ -f "package.json" ]; then
  PROJECT_TYPE="brownfield"
  TECH_DETECTED="Node.js"
elif [ -f "requirements.txt" ] || [ -f "pyproject.toml" ]; then
  PROJECT_TYPE="brownfield"
  TECH_DETECTED="Python"
elif [ -f "Cargo.toml" ]; then
  PROJECT_TYPE="brownfield"
  TECH_DETECTED="Rust"
elif [ -f "go.mod" ]; then
  PROJECT_TYPE="brownfield"
  TECH_DETECTED="Go"
elif [ -f "Gemfile" ]; then
  PROJECT_TYPE="brownfield"
  TECH_DETECTED="Ruby"
else
  PROJECT_TYPE="greenfield"
fi

Inform user:

if [ "$PROJECT_TYPE" = "brownfield" ]; then
  echo "✅ Detected existing codebase ($TECH_DETECTED)"
  echo "   Will scan codebase to auto-fill project docs"
else
  echo "ℹ️  No existing codebase detected (greenfield project)"
  echo "   Will generate templates with [NEEDS CLARIFICATION] markers"
fi

Quality check: Correct project type detected? If unclear (e.g., multiple languages), ask user which is primary.

**Interactive Questionnaire (15 Questions)**

Gather essential project context to fill documentation templates.

Question Flow (full list in reference.md):

# Q1: Project name
read -p "Q1. Project name (e.g., FlightPro): " PROJECT_NAME

# Q2: Vision (1 sentence)
read -p "Q2. Vision - What problem does this solve? (1 sentence): " VISION

# Q3: Primary users
read -p "Q3. Primary users (e.g., CFIs, students): " PRIMARY_USERS

# Q4: Scale tier
echo "Q4. Scale tier:"
echo "   1) Micro (100 users, $40/mo)"
echo "   2) Small (1K users, $95/mo)"
echo "   3) Medium (10K users, $415/mo)"
echo "   4) Large (100K+ users, $2K+/mo)"
read -p "   Choice (1-4): " SCALE_CHOICE
case $SCALE_CHOICE in
  1) SCALE="micro" ;;
  2) SCALE="small" ;;
  3) SCALE="medium" ;;
  4) SCALE="large" ;;
esac

# Q5-Q15: Team size, architecture style, database, deployment platform,
#         API style, auth provider, budget, privacy, git workflow,
#         deployment model, frontend framework

Validation:

• Required: PROJECT_NAME, VISION, PRIMARY_USERS
• Optional (can be "unknown" or "TBD"): Budget, specific versions

Store answers:

# Save to temporary file for project-architect agent
cat > /tmp/project-init-answers.json <<EOF
{
  "project_name": "$PROJECT_NAME",
  "vision": "$VISION",
  "primary_users": "$PRIMARY_USERS",
  "scale": "$SCALE",
  "team_size": "$TEAM_SIZE",
  "architecture": "$ARCHITECTURE",
  "database": "$DATABASE",
  "deployment_platform": "$DEPLOYMENT_PLATFORM",
  "api_style": "$API_STYLE",
  "auth_provider": "$AUTH_PROVIDER",
  "budget_monthly": "$BUDGET",
  "privacy_reqs": "$PRIVACY",
  "git_workflow": "$GIT_WORKFLOW",
  "deployment_model": "$DEPLOYMENT_MODEL",
  "frontend_framework": "$FRONTEND"
}
EOF

Quality check: All required fields filled? User satisfied with answers?

**Brownfield Codebase Scanning** (Skip if greenfield)

Auto-detect tech stack, architecture patterns, and generate ERD from migrations.

Tech Stack Scanning:

# Frontend detection
if [ -f "package.json" ]; then
  # Detect Next.js
  if grep -q '"next":' package.json; then
    FRONTEND_FRAMEWORK=$(jq -r '.dependencies.next // .devDependencies.next' package.json)
  fi

  # Detect React
  if grep -q '"react":' package.json; then
    REACT_VERSION=$(jq -r '.dependencies.react' package.json)
  fi

  # Detect TypeScript
  if grep -q '"typescript":' package.json; then
    TS_DETECTED=true
  fi
fi

# Backend detection
if [ -f "requirements.txt" ]; then
  # Detect FastAPI
  if grep -q 'fastapi' requirements.txt; then
    BACKEND_FRAMEWORK="FastAPI"
    BACKEND_VERSION=$(grep 'fastapi' requirements.txt | grep -o '[0-9]\+\.[0-9]\+\.[0-9]\+')
  fi

  # Detect Django
  if grep -q 'django' requirements.txt; then
    BACKEND_FRAMEWORK="Django"
  fi
fi

# Database detection (from dependencies)
if grep -q '"pg":' package.json || grep -q 'psycopg2' requirements.txt; then
  DATABASE="PostgreSQL"
fi

# Database detection (from migrations)
if [ -d "alembic/versions" ]; then
  DATABASE_MIGRATION_TOOL="Alembic"
  MIGRATION_COUNT=$(ls alembic/versions/*.py 2>/dev/null | wc -l)
fi

Architecture Pattern Detection:

# Detect microservices
if [ -d "services" ] || [ -d "microservices" ]; then
  ARCHITECTURE="microservices"
elif [ -f "docker-compose.yml" ]; then
  # Check for multiple services in docker-compose
  SERVICE_COUNT=$(grep -c 'image:' docker-compose.yml)
  if [ "$SERVICE_COUNT" -gt 2 ]; then
    ARCHITECTURE="microservices"
  fi
else
  ARCHITECTURE="monolith"
fi

Deployment Platform Detection:

if [ -f "vercel.json" ]; then
  DEPLOYMENT_PLATFORM="Vercel"
elif [ -f "railway.json" ] || [ -f "railway.toml" ]; then
  DEPLOYMENT_PLATFORM="Railway"
elif [ -d ".github/workflows" ]; then
  # Inspect deploy workflow for platform
  WORKFLOW_FILE=$(find .github/workflows -name "*deploy*" -type f | head -1)
  if grep -q 'vercel' "$WORKFLOW_FILE"; then
    DEPLOYMENT_PLATFORM="Vercel"
  elif grep -q 'railway' "$WORKFLOW_FILE"; then
    DEPLOYMENT_PLATFORM="Railway"
  fi
fi

ERD Generation from Migrations:

# If Alembic migrations exist, generate ERD
if [ -d "alembic/versions" ]; then
  # Scan migration files for create_table statements
  ENTITIES=()
  for migration in alembic/versions/*.py; do
    # Extract table names
    TABLE_NAME=$(grep -oP "create_table\('\K[^']+" "$migration")
    if [ -n "$TABLE_NAME" ]; then
      ENTITIES+=("$TABLE_NAME")
    fi
  done

  # Result: ENTITIES=("users" "students" "lessons" "progress")
fi

Quality check: Tech stack accurately detected? ERD entities match database schema?

**Launch Project-Architect Agent**

Invoke specialized agent to generate 8 documentation files.

Agent Invocation:

# Pass answers + scan results to project-architect agent
# Agent responsibilities:
# - Read 8 templates from .spec-flow/templates/project/
# - Fill templates with questionnaire answers
# - Inject scan results (if brownfield)
# - Mark unknowns with [NEEDS CLARIFICATION]
# - Generate Mermaid diagrams (C4, ERD)
# - Validate cross-document consistency
# - Write 8 files to docs/project/

Monitor progress:

echo "🤖 Project-architect agent generating documentation..."
echo "   - Reading 8 templates"
echo "   - Filling with questionnaire answers"
echo "   - Injecting scan results (brownfield)"
echo "   - Generating Mermaid diagrams"
echo "   - Validating cross-document consistency"

Quality check: Agent completed successfully? No errors during generation?

**Validate Generated Documentation**

Verify all files exist, check quality metrics, validate Mermaid diagrams.

File Existence Check:

DOCS_DIR="docs/project"
REQUIRED_FILES=(
  "overview.md"
  "system-architecture.md"
  "tech-stack.md"
  "data-architecture.md"
  "api-strategy.md"
  "capacity-planning.md"
  "deployment-strategy.md"
  "development-workflow.md"
)

for file in "${REQUIRED_FILES[@]}"; do
  if [ ! -f "$DOCS_DIR/$file" ]; then
    echo "❌ Missing file: $DOCS_DIR/$file"
    exit 1
  fi
done

echo "✅ All 8 documentation files generated"

Quality Metrics:

# Count [NEEDS CLARIFICATION] markers
CLARIFICATION_COUNT=$(grep -r "NEEDS CLARIFICATION" "$DOCS_DIR" | wc -l)
echo "ℹ️  Found $CLARIFICATION_COUNT [NEEDS CLARIFICATION] sections"

# Validate Mermaid diagrams
if grep -q '```mermaid' "$DOCS_DIR/system-architecture.md"; then
  echo "✅ Mermaid diagrams present in system-architecture.md"
else
  echo "⚠️  Warning: No Mermaid diagrams in system-architecture.md"
fi

# Cross-document consistency check
# - Tech stack in tech-stack.md matches system-architecture.md
# - Database in tech-stack.md matches data-architecture.md
# - Deployment model in deployment-strategy.md matches capacity-planning.md

Quality Standards:

• All 8 files generated: ✅
• [NEEDS CLARIFICATION] count < 20: ✅ (good coverage)
• Mermaid diagrams present: ✅
• Cross-document consistency: ✅

**Summary Report**

Display generation results and guide user to next steps.

Summary Display:

echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "✅ PROJECT DOCUMENTATION GENERATED"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo ""
echo "📊 Coverage:"
if [ "$PROJECT_TYPE" = "greenfield" ]; then
  echo "   - Filled from questionnaire: 70%"
  echo "   - Inferred from defaults: 10%"
  echo "   - Needs clarification: 20%"
else
  echo "   - Filled from questionnaire: 50%"
  echo "   - Inferred from codebase: 30%"
  echo "   - Needs clarification: 20%"
fi
echo ""
echo "📍 [NEEDS CLARIFICATION] Sections: $CLARIFICATION_COUNT"
if [ "$CLARIFICATION_COUNT" -gt 0 ]; then
  echo "   Review and fill these sections:"
  grep -r "NEEDS CLARIFICATION" docs/project/ --with-filename | head -5
  echo "   ..."
fi
echo ""
echo "✅ Generated Files:"
for file in "${REQUIRED_FILES[@]}"; do
  echo "   - docs/project/$file"
done
echo ""
echo "💡 Next Steps:"
echo "   1. Review docs/project/ files"
echo "   2. Fill [NEEDS CLARIFICATION] sections"
echo "   3. Commit: git add docs/project/ && git commit -m 'docs: add project architecture'"
echo "   4. Start building: /roadmap or /feature"
echo ""

Quality check: User understands next steps? Documentation looks complete?

After completing workflow, verify:

• All 8 files exist in docs/project/ with reasonable file sizes (not empty)
• [NEEDS CLARIFICATION] markers are specific (e.g., "[NEEDS CLARIFICATION: Who are your main competitors?]" not just "[TODO]")
• Mermaid diagrams use valid syntax (run through Mermaid validator if available)
• Cross-document references align:
  • Tech stack mentioned in multiple docs is consistent
  • Database choice appears in tech-stack.md, data-architecture.md, capacity-planning.md
  • Deployment model in deployment-strategy.md matches capacity-planning.md cost model
• Brownfield scan results (if applicable) accurately reflect codebase
• No Lorem Ipsum or generic placeholder text
• User has clear understanding of what to do next

<anti_patterns> ❌ Don't: Make up features, user metrics, or competitor names not provided by user ✅ Do: Use [NEEDS CLARIFICATION] for unknowns

Why: Documentation must reflect actual project, not assumptions. Hallucinated details lead to misalignment.

Example (bad):

## Competitors
- Foreflight (market leader, $250/year)
- CloudAhoy ($99/year)
- ForeFlight Pro ($500/year)

User never mentioned these!

Example (good):

## Competitors
[NEEDS CLARIFICATION: Who are your main competitors? What features do they lack that you'll provide?]

**❌ Don't**: Skip brownfield scanning even when codebase exists **✅ Do**: Always scan for tech stack, ERD, architecture patterns

Why: Auto-detection saves time and reduces [NEEDS CLARIFICATION] markers by 20-30%.

Example (bad):

Detected brownfield project
Skipping scan (takes too long)
Result: 35 [NEEDS CLARIFICATION] markers

Example (good):

Detected brownfield project
Scanning package.json... Found Next.js 14, React 18, TypeScript
Scanning alembic/versions... Found User, Student, Lesson entities
Result: 12 [NEEDS CLARIFICATION] markers (67% reduction)

**❌ Don't**: Use generic "[TODO]" or "[FILL THIS]" placeholders **✅ Do**: Use specific [NEEDS CLARIFICATION: question] markers

Why: Specific questions guide user to fill gaps correctly.

Example (bad):

## Performance Targets
- API response time: [TODO]
- Database query time: [FILL THIS]

Example (good):

## Performance Targets
- API response time: [NEEDS CLARIFICATION: p95 < 500ms? 1s? 2s?]
- Database query time: [NEEDS CLARIFICATION: What's acceptable for complex joins? <100ms? <500ms?]

**❌ Don't**: Re-ask questions already answered in questionnaire **✅ Do**: Use questionnaire answers verbatim, cite source

Why: Wastes user time, creates inconsistencies.

Example (bad):

Questionnaire Q7: Database = PostgreSQL
Generated doc: "Database: [NEEDS CLARIFICATION: What database?]"

Example (good):

Questionnaire Q7: Database = PostgreSQL
Generated doc: "Database: PostgreSQL (from questionnaire Q7)"

**❌ Don't**: Generate 8 docs without checking consistency **✅ Do**: Validate tech stack, database, deployment model align across docs

Why: Inconsistencies confuse developers, lead to architectural mismatches.

Example (bad):

tech-stack.md: Database = PostgreSQL
data-architecture.md: Database = MongoDB (conflict!)

Example (good):

tech-stack.md: Database = PostgreSQL
data-architecture.md: Database = PostgreSQL (consistent)
capacity-planning.md: Database costs = PostgreSQL pricing (consistent)

<best_practices> Always cite where information came from (questionnaire, scan, inference, default).

Example:

## Tech Stack

**Backend**: FastAPI (detected from requirements.txt v0.109.0)
**Frontend**: Next.js 14 (detected from package.json)
**Database**: PostgreSQL (from questionnaire Q7)
**Architecture**: Monolith (inferred from team size: solo developer)
**Deployment**: Vercel (from questionnaire Q8)

Benefit: Transparency shows which answers need verification vs which are confirmed.

Make [NEEDS CLARIFICATION] markers actionable with specific questions.

Example:

## Success Metrics

- Monthly Active Users (MAU): [NEEDS CLARIFICATION: What's your target MAU at 6 months? 12 months?]
- Conversion rate: [NEEDS CLARIFICATION: What % of signups should convert to paid within 30 days?]
- Churn rate: [NEEDS CLARIFICATION: What monthly churn rate is acceptable? <5%? <10%?]

Benefit: User knows exactly what information is needed, not guessing.

For brownfield projects, scan codebase before filling templates to maximize auto-fill.

Workflow:

1. Detect project type
2. If brownfield: scan tech stack, ERD, architecture BEFORE questionnaire
3. Pre-fill questionnaire with scan results (user can override)
4. Generate docs with combined questionnaire + scan data

Benefit: Reduces user effort, increases accuracy, lowers [NEEDS CLARIFICATION] count.

Always generate Mermaid diagrams for system-architecture.md and data-architecture.md.

System Architecture (C4 Context diagram):

graph TD
    A[User] --> B[FlightPro App]
    B --> C[PostgreSQL]
    B --> D[Clerk Auth]
    B --> E[Vercel Edge Network]

Data Architecture (ERD):

erDiagram
    USER ||--o{ STUDENT : teaches
    STUDENT ||--o{ LESSON : takes
    LESSON ||--o{ PROGRESS : tracks

Benefit: Visual diagrams clarify architecture, catch design issues early.

Run cross-document consistency checks before writing files.

Validation Script:

# Extract database from tech-stack.md
TECH_DB=$(grep "Database:" docs/project/tech-stack.md | awk '{print $2}')

# Extract database from data-architecture.md
DATA_DB=$(grep "Database:" docs/project/data-architecture.md | awk '{print $2}')

# Compare
if [ "$TECH_DB" != "$DATA_DB" ]; then
  echo "❌ Inconsistency: tech-stack says $TECH_DB, data-architecture says $DATA_DB"
  exit 1
fi

Benefit: Prevents shipping inconsistent documentation, saves time fixing later. </best_practices>

<quality_standards> Good project initialization:

• All 8 files generated with meaningful content (not just templates)
• [NEEDS CLARIFICATION] count < 20 (indicates 80%+ coverage)
• Every [NEEDS CLARIFICATION] has specific question
• Mermaid diagrams present and syntactically valid
• Cross-document consistency validated (tech stack, database, deployment model)
• Brownfield scan (if applicable) accurately detected 70%+ of tech stack
• User clearly understands next steps (review, fill, commit)
• Reasonable defaults used (e.g., monolith for solo dev, PostgreSQL for relational)
• Sources cited (questionnaire Q7, detected from package.json, inferred from X)

Bad project initialization:

• Generic templates with [TODO] placeholders
• [NEEDS CLARIFICATION] count > 30 (poor coverage, mostly empty)
• Vague markers ("[TODO fill this later]")
• No Mermaid diagrams (all-text architecture descriptions)
• Conflicting info across docs (PostgreSQL in one, MongoDB in another)
• Brownfield scan skipped or inaccurate (wrong tech stack detected)
• No next steps provided
• Hallucinated details (made-up competitors, features, metrics)
• No source citations (can't tell what's from user vs inferred) </quality_standards>

**Issue**: "Too many [NEEDS CLARIFICATION] markers (>30)" - **Cause**: Greenfield project + incomplete questionnaire answers - **Fix**: Re-run /init-project with more detailed answers, OR manually fill clarifications post-generation

Issue: "Brownfield scan detected wrong tech stack"

• Cause: Multiple tech stacks in monorepo, or scan logic error
• Fix: Manually edit docs/project/tech-stack.md after generation, verify package.json/requirements.txt

Issue: "ERD generation failed (no entities)"

• Cause: Migration files not in expected format (not Alembic/Prisma), or custom migration tool
• Fix: Manually create ERD in data-architecture.md using Mermaid syntax, reference database schema

Issue: "Cross-document inconsistencies detected"

• Cause: Questionnaire answers conflict (e.g., said "PostgreSQL" but scan found "MongoDB")
• Fix: Review validation output, manually resolve conflicts in generated docs, update questionnaire answers

Issue: "Missing template files"

• Cause: Spec-Flow package not installed correctly, or templates deleted
• Fix: Run git pull origin main to get latest templates, or reinstall workflow package

Issue: "Agent failed to generate docs"

• Cause: Out of memory (8 docs is token-heavy), or template malformed
• Fix: Check agent logs, verify template syntax, increase token budget, run generation sequentially not parallel

<reference_guides> Supporting documentation:

• 15 Questions Mapping (reference.md) - What each question maps to in generated docs
• Brownfield Scanning (reference.md) - Tech stack detection patterns for Node.js, Python, Rust, Go
• ERD Generation (reference.md) - Parsing Alembic/Prisma migrations to generate Mermaid ERDs
• Cross-Document Consistency (reference.md) - Validation rules for aligning tech stack across 8 docs
• [NEEDS CLARIFICATION] Standards (reference.md) - When to use markers, how to phrase questions

Next steps after /init-project:

• Review generated docs in docs/project/
• Fill [NEEDS CLARIFICATION] sections with project-specific details
• Commit to repository: git add docs/project/ && git commit -m "docs: add project architecture"
• Start building features: Run /roadmap to plan features, or /feature "name" to implement first feature </reference_guides>