Claude-Code-Workflow/.claude/commands/ddd/scan.md

name, description, argument-hint, allowed-tools

name	description	argument-hint	allowed-tools
scan	Scan existing codebase to build document index without specs. Analyzes code structure, infers features, discovers components, and reverse-engineers project knowledge graph.	[-y|--yes] [--from-scratch] [--scope <dir>] "optional project description"	TodoWrite(*), Agent(*), AskUserQuestion(*), Read(*), Grep(*), Glob(*), Bash(*), Edit(*), Write(*), mcp__ace-tool__search_context(*)

Auto Mode

When --yes or -y: Auto-confirm feature groupings, component naming, skip interactive review.

DDD Scan Command (/ddd:scan)

Purpose

For existing projects without specifications: analyze codebase to construct the document index by reverse-engineering project structure. This is the code-first entry point — no spec-generator required.

Codebase → Components → Features (inferred) → Requirements (inferred) → doc-index.json

When to Use

• Existing project, no spec-generator outputs
• Want to start using doc-driven workflow on a legacy codebase
• Quick project mapping for onboarding or audit

Prerequisite

• A codebase must exist (src/, lib/, app/, or similar source directories)
• Git repository recommended (for action history seeding)

Storage Location

.workflow/.doc-index/
├── doc-index.json              ← Central index (primary output)
├── feature-maps/               ← Inferred feature documentation
│   ├── _index.md
│   └── {feature-slug}.md
├── tech-registry/              ← Discovered component documentation
│   ├── _index.md
│   └── {component-slug}.md
└── action-logs/                ← Git history seeds
    ├── _index.md
    └── {act-hash}.md

Phase 1: Project Structure Analysis

1.1 Framework & Stack Detection

ccw cli -p "PURPOSE: Analyze project structure, tech stack, and architecture for documentation indexing.
TASK:
• Detect language/framework from manifest files (package.json, go.mod, Cargo.toml, requirements.txt, etc.)
• Map directory structure: source dirs, test dirs, config dirs, entry points
• Identify architectural pattern: monolith, microservices, monorepo, library, CLI tool
• Detect key dependencies and their roles (ORM, HTTP framework, auth library, etc.)
• List all major source directories with brief purpose description
MODE: analysis
CONTEXT: @**/*
EXPECTED: JSON with: {
  project_name, language, framework, architecture_pattern,
  source_dirs: [{ path, purpose, file_count }],
  dependencies: [{ name, role }],
  entry_points: [{ path, description }]
}
CONSTRAINTS: Prioritize source directories | Ignore node_modules, dist, build, vendor" --tool gemini --mode analysis

1.2 Merge with project-tech.json

If .workflow/project-tech.json exists, merge to reduce redundant analysis.

Phase 2: Component Discovery

2.1 Deep Module Scan

ccw cli -p "PURPOSE: Discover all significant code components/modules for documentation indexing.
TASK:
• For each source directory, identify distinct modules/components
• For each component extract:
  - Name (class name, module name, or logical group)
  - Type: service | controller | model | util | hook | route | config | middleware | component
  - File paths (primary file + related files)
  - Exported symbols (public API: classes, functions, types, constants)
  - Internal dependencies: what other modules it imports from within the project
  - Responsibility: one-line description of what it does
• Group small utility files under parent module when they share domain
MODE: analysis
CONTEXT: @{source_dirs from Phase 1}
EXPECTED: JSON array: [{ name, type, files, symbols, depends_on, responsibility }]
CONSTRAINTS: Focus on business logic | Min threshold: components with 2+ exports or clear domain purpose | Group utilities under parent domain" --tool gemini --mode analysis

2.2 Generate Component IDs

For each discovered component:

• ID: tech-{kebab-case-name} (e.g., tech-auth-service, tech-user-model)
• Validate uniqueness, append counter on collision

2.3 Build Dependency Graph

From depends_on fields, construct internal dependency edges:

tech-auth-service → tech-user-model
tech-auth-service → tech-jwt-util
tech-order-controller → tech-auth-service

Phase 3: Feature Inference

Key step: group components into logical features without formal specs.

3.1 Inference Strategy (priority order)

Strategy 1 — Directory grouping:
  src/auth/**       → feat-auth
  src/orders/**     → feat-orders
  src/payments/**   → feat-payments

Strategy 2 — Route/endpoint grouping (web apps):
  /api/users/*      → feat-user-management
  /api/orders/*     → feat-order-management

Strategy 3 — Dependency clustering:
  Components that heavily import each other → same feature

Strategy 4 — Domain keyword extraction:
  Class names + file names → domain terms → feature names

3.2 Gemini Feature Synthesis

ccw cli -p "PURPOSE: Infer high-level features from discovered code components. This project has no formal specification.
TASK:
Given these discovered components:
{component list from Phase 2: names, types, files, responsibilities, dependencies}

• Group them into logical features (3-10 features for a typical project)
• For each feature:
  - name: human-readable (Chinese OK)
  - component_ids: which components belong
  - description: what the feature does (inferred from code)
  - inferred_requirements: what this feature needs to accomplish (1-3 per feature)
  - status: 'implemented' (code complete) or 'partial' (incomplete patterns)
  - tags: search keywords
• Identify cross-cutting concerns (logging, auth middleware, error handling) as separate features
MODE: analysis
CONTEXT: {component list JSON}
EXPECTED: JSON: { features: [{ name, description, component_ids, inferred_requirements: [{ id, title }], status, tags }] }
CONSTRAINTS: Every component must belong to at least 1 feature | Prefer fewer broad features over many narrow ones" --tool gemini --mode analysis

3.3 Interactive Feature Review (unless -y)

Present inferred features to user:

• Allow renaming, merging, splitting
• Allow reassigning components between features
• Confirm final feature list

Phase 4: Implicit Requirement & Architecture Extraction

4.1 Inferred Requirements

For each feature, generate lightweight requirement entries from its components:

Feature: feat-auth (User Authentication)
  → IREQ-001: "Users can log in with email and password"     (from LoginController)
  → IREQ-002: "JWT tokens for session management"            (from AuthMiddleware + jwt dep)
  → IREQ-003: "Password reset via email"                     (from PasswordResetService)

ID Convention: IREQ-NNN — distinguishes inferred from formal REQ-NNN.

4.2 Inferred Architecture Decisions

Detect patterns from code + dependencies:

Express.js + JWT middleware    → IADR-001: "REST API with JWT authentication"
Prisma ORM + PostgreSQL        → IADR-002: "PostgreSQL via Prisma ORM"
React + Redux                  → IADR-003: "React frontend with Redux state"

ID Convention: IADR-NNN — distinguishes inferred from formal ADR-NNN.

4.3 Glossary Generation

Extract domain terms from:

• Class/function names (CamelCase → terms)
• Key business terms in comments and strings
• Framework-specific terminology

Write to .workflow/.doc-index/glossary.json.

Phase 5: Git History Seeds

git log --oneline --since="3 months ago" --no-merges --format="%H|%s|%ai" | head -30

For each significant commit:

• Match changed files to discovered components
• Create action entry with type: "historical"

Phase 6: Assemble doc-index.json

Write the index with code-first markers:

{
  "version": "1.0",
  "project": "{project-name}",
  "build_path": "code-first",
  "spec_session": null,
  "last_updated": "ISO8601",
  "glossary": [...],
  "features": [{
    "id": "feat-{slug}",
    "name": "Feature Name",
    "epicId": null,
    "status": "implemented|partial",
    "docPath": "feature-maps/{slug}.md",
    "requirementIds": ["IREQ-NNN"],
    "tags": ["tag"]
  }],
  "requirements": [{
    "id": "IREQ-NNN",
    "title": "Inferred requirement",
    "source": "inferred",
    "priority": "inferred",
    "sourcePath": null,
    "techComponentIds": ["tech-{slug}"],
    "featureId": "feat-{slug}"
  }],
  "technicalComponents": [{
    "id": "tech-{slug}",
    "name": "ComponentName",
    "type": "service|controller|model|...",
    "responsibility": "One-line description",
    "adrId": "IADR-NNN|null",
    "docPath": "tech-registry/{slug}.md",
    "codeLocations": [{ "path": "src/...", "symbols": [...] }],
    "dependsOn": ["tech-{other}"],
    "featureIds": ["feat-{slug}"],
    "actionIds": []
  }],
  "architectureDecisions": [{
    "id": "IADR-NNN",
    "title": "Inferred decision",
    "source": "inferred",
    "sourcePath": null,
    "componentIds": ["tech-{slug}"]
  }],
  "actions": [{
    "id": "act-{short-hash}",
    "description": "Commit message",
    "type": "historical",
    "status": "historical",
    "affectedComponents": ["tech-{slug}"],
    "relatedCommit": "full-hash",
    "timestamp": "ISO8601"
  }],
  "freshness": {
    "thresholds": { "warning": 0.3, "stale": 0.7 },
    "weights": { "time": 0.1, "churn": 0.4, "symbol": 0.5 },
    "time_decay_k": 0.05,
    "auto_regenerate": false
  },
  "deepwiki_feature_to_symbol_index": {}
}

Phase 6.5: Build DeepWiki Feature-to-Symbol Index

If DeepWiki is available (.codexlens/deepwiki_index.db exists):

1. Collect all codeLocations[].path from technicalComponents[]
2. Query DeepWiki: POST /api/deepwiki/symbols-for-paths { paths: [...] }
3. Build deepwiki_feature_to_symbol_index by traversing: feature → requirementIds → techComponentIds → codeLocations → symbols

"deepwiki_feature_to_symbol_index": {
  "feat-auth": [
    "deepwiki:symbol:src/auth/jwt.ts#L30-L55",
    "deepwiki:symbol:src/models/user.ts#L12-L40"
  ]
}

Symbol URN format: deepwiki:symbol:<file_path>#L<start>-L<end>

Graceful degradation: If DeepWiki is unavailable, set deepwiki_feature_to_symbol_index: {} and log warning.

Phase 7: Generate Documents

7.1 Feature Maps

For each feature → feature-maps/{slug}.md:

• Frontmatter with id, name, status, inferred requirements, components, tags
• Sections: Overview, Inferred Requirements, Technical Components, Dependencies, Change History
• Mark inferred content: > Inferred from code analysis

7.2 Tech Registry

For each component → tech-registry/{slug}.md:

• Frontmatter with id, name, type, code_locations, depends_on
• Sections: Responsibility, Code Locations, Related Features, Dependencies (in/out)

7.3 Index Documents

• feature-maps/_index.md — feature table
• tech-registry/_index.md — component table
• action-logs/_index.md — recent git history table

Phase 8: Validation & Report

Scan Report

Project: {name} ({language}/{framework})
Architecture: {pattern}
Source dirs: {N}

Discovered:
  Components: {N} ({by type breakdown})
  Features: {N} (inferred)
  Requirements: {N} (IREQ, inferred)
  Architecture Decisions: {N} (IADR, inferred)
  Historical Actions: {N} (from git)

Coverage:
  Components → Features: {%}
  Dependencies mapped: {%}

Recommendations:
  - Run /spec-generator to formalize {N} inferred requirements
  - {N} components have unclear responsibility — review tech-registry docs
  - Use /ddd:plan to start planning tasks with this index

Flags

Flag	Effect
-y, --yes	Auto-confirm all decisions
--from-scratch	Delete existing index and rebuild
--scope <dir>	Limit scan to specific directory (e.g., --scope src/auth)

Upgrade Path: scan → spec

When a scanned project later runs spec-generator + /ddd:index-build:

• /ddd:index-build detects existing code-first index
• Merges: IREQ-NNN → REQ-NNN, IADR-NNN → ADR-NNN where content overlaps
• Updates build_path to "spec-first"
• Preserves all tech-* and feat-* entries (updates links only)

Integration Points

• Input from: Codebase, git history, project-tech.json
• Output to: ddd:plan, ddd:sync, ddd:update, ddd:index-build (upgrade)
• Standalone: Can be used independently on any project