msd-core/commands/gsd/analyze-codebase.md

name, description, argument-hint, allowed-tools

name	description	argument-hint	allowed-tools
gsd:analyze-codebase	Scan existing codebase and populate .planning/intel/ with file index, conventions, and semantic entity files		Read Bash Glob Write Task

Scan codebase to populate .planning/intel/ with file index, conventions, and semantic entity files.

Works standalone (without /gsd:new-project) for brownfield codebases. Creates summary.md for context injection at session start. Generates entity files that capture file PURPOSE (what it does, why it exists), not just syntax.

Output: .planning/intel/index.json, conventions.json, summary.md, entities/*.md

This command performs bulk codebase scanning to bootstrap the Codebase Intelligence system.

Use for:

• Brownfield projects before /gsd:new-project
• Refreshing intel after major changes
• Standalone intel without full project setup

After initial scan, the PostToolUse hook (hooks/intel-index.js) maintains incremental updates.

Execution model (Steps 2-3 - Indexing):

• Orchestrator finds file paths via Glob (Step 2)
• Spawns gsd-indexer subagent with file paths only (Step 3)
• Subagent reads files in fresh 200k context, applies regex patterns
• Subagent writes index.json directly to disk
• Subagent returns statistics only (not file contents or index data)
• This prevents context exhaustion on large codebases (500+ files)

Execution model (Step 9 - Entity Generation):

• Orchestrator selects files for entity generation (up to 50 based on priority)
• Spawns gsd-entity-generator subagent with file list (paths only, not contents)
• Subagent reads files in fresh 200k context, generates entities, writes to disk
• PostToolUse hook automatically syncs entities to graph.db
• Subagent returns statistics only (not entity contents)
• This preserves orchestrator context for large codebases (500+ files)
• Users can skip Step 9 if they only want the index (faster)

Step 1: Create directory structure

mkdir -p .planning/intel

Step 2: Find all indexable files

Use Glob tool with pattern: **/*.{js,ts,jsx,tsx,mjs,cjs}

Exclude directories (skip any path containing):

• node_modules
• dist
• build
• .git
• vendor
• coverage
• .next
• pycache

Filter results to remove excluded paths. Store as file_paths array.

Output: List of absolute file paths for indexing. Do NOT read file contents.

Step 3: Spawn indexer subagent

Spawn gsd-indexer subagent with the file paths from Step 2.

Why subagent delegation:

• Orchestrator would exhaust context reading 500+ files inline
• Subagent gets fresh 200k context for file reading
• Orchestrator only handles file paths (small)
• Subagent writes index.json directly (large)

Task tool invocation:

file_list = "\n".join(file_paths)  # From Step 2 Glob results

Task(
  prompt=f"""Index codebase files by extracting exports and imports.

You are a GSD indexer. Read source files and extract exports/imports using regex patterns.

**Parameters:**
- Files to process: {len(file_paths)}
- Output path: .planning/intel/index.json

**Export patterns:**
- Named: export\\s*\\{{([^}}]+)\\}}
- Declaration: export\\s+(?:const|let|var|function\\*?|async\\s+function|class)\\s+(\\w+)
- Default: export\\s+default\\s+(?:function\\s*\\*?\\s*|class\\s+)?(\w+)?
- CommonJS object: module\\.exports\\s*=\\s*\\{{([^}}]+)\\}}
- CommonJS single: module\\.exports\\s*=\\s*(\\w+)\\s*[;\\n]
- TypeScript: export\\s+(?:type|interface)\\s+(\\w+)

**Import patterns:**
- ES6: import\\s+(?:\\{{[^}}]*\\}}|\\*\\s+as\\s+\\w+|\\w+)\\s+from\\s+['\"]([^'\"]+)['\"]
- Side-effect: import\\s+['\"]([^'\"]+)['\"]
- CommonJS: require\\s*\\(\\s*['\"]([^'\"]+)['\"]\\s*\\)

**Index schema:**
```json
{{
  "version": 1,
  "updated": {{timestamp}},
  "files": {{
    "/absolute/path/file.js": {{
      "exports": ["name1", "name2"],
      "imports": ["source1", "source2"],
      "indexed": {{timestamp}}
    }}
  }}
}}

Process: For each file path below:

1. Read file content using Read tool
2. Apply export regex patterns, collect export names
3. Apply import regex patterns, collect import sources
4. Store in index structure with absolute path as key

Files: {file_list}

Return format: When complete, return ONLY statistics:

INDEXING COMPLETE

Files processed: {{N}} Exports found: {{M}} Imports found: {{K}} Errors: {{E}}

Index written to: .planning/intel/index.json

Do NOT return index contents. """, subagent_type="gsd-indexer" )

**Wait for completion:** Task() blocks until subagent finishes.

**Verify index created:**
```bash
ls -la .planning/intel/index.json

Step 4: Detect conventions

Analyze the collected index for patterns.

Naming conventions (require 5+ exports, 70%+ match rate):

• camelCase: ^[a-z][a-z0-9]*(?:[A-Z][a-z0-9]+)+$ or single lowercase ^[a-z][a-z0-9]*$
• PascalCase: ^[A-Z][a-z0-9]+(?:[A-Z][a-z0-9]+)*$ or single ^[A-Z][a-z0-9]+$
• snake_case: ^[a-z][a-z0-9]*(?:_[a-z0-9]+)+$
• SCREAMING_SNAKE: ^[A-Z][A-Z0-9]*(?:_[A-Z0-9]+)+$ or single ^[A-Z][A-Z0-9]*$
• Skip 'default' when counting (it's a keyword, not naming convention)

Directory patterns (use lookup table):

components -> UI components
hooks -> React/custom hooks
utils, lib -> Utility functions
services -> Service layer
api, routes -> API endpoints
types -> TypeScript types
models -> Data models
tests, __tests__, test, spec -> Test files
controllers -> Controllers
middleware -> Middleware
config -> Configuration
constants -> Constants
pages -> Page components
views -> View templates

Suffix patterns (require 5+ occurrences):

.test.*, .spec.* -> Test files
.service.* -> Service layer
.controller.* -> Controllers
.model.* -> Data models
.util.*, .utils.* -> Utility functions
.helper.*, .helpers.* -> Helper functions
.config.* -> Configuration
.types.*, .type.* -> TypeScript types
.hook.*, .hooks.* -> React/custom hooks
.context.* -> React context
.store.* -> State store
.slice.* -> Redux slice
.reducer.* -> Redux reducer
.action.*, .actions.* -> Redux actions
.api.* -> API layer
.route.*, .routes.* -> Route definitions
.middleware.* -> Middleware
.schema.* -> Schema definitions
.mock.*, .mocks.* -> Mock data
.fixture.*, .fixtures.* -> Test fixtures

Step 5: Read index.json

The gsd-indexer subagent wrote index.json in Step 3. Read it back for convention detection and statistics.

cat .planning/intel/index.json

Expected schema:

{
  "version": 1,
  "updated": 1737360330000,
  "files": {
    "/absolute/path/to/file.js": {
      "exports": ["functionA", "ClassB"],
      "imports": ["react", "./utils"],
      "indexed": 1737360330000
    }
  }
}

Step 6: Write conventions.json

Write to .planning/intel/conventions.json:

{
  "version": 1,
  "updated": 1737360330000,
  "naming": {
    "exports": {
      "dominant": "camelCase",
      "count": 42,
      "percentage": 85
    }
  },
  "directories": {
    "components": { "purpose": "UI components", "files": 15 },
    "hooks": { "purpose": "React/custom hooks", "files": 8 }
  },
  "suffixes": {
    ".test.js": { "purpose": "Test files", "count": 12 }
  }
}

Step 7: Generate summary.md

Write to .planning/intel/summary.md:

# Codebase Intelligence Summary

Last updated: [ISO timestamp]
Indexed files: [N]

## Naming Conventions

- Export naming: [case] ([percentage]% of [count] exports)

## Key Directories

- `[dir]/`: [purpose] ([N] files)
- ... (top 5)

## File Patterns

- `*[suffix]`: [purpose] ([count] files)
- ... (top 3)

Total exports: [N]

Target: < 500 tokens. Keep concise for context injection.

Step 8: Report completion

Display summary statistics:

Codebase Analysis Complete

Files indexed: [N]
Exports found: [N]
Imports found: [N]

Conventions detected:
- Naming: [dominant case] ([percentage]%)
- Directories: [list]
- Patterns: [list]

Files created:
- .planning/intel/index.json
- .planning/intel/conventions.json
- .planning/intel/summary.md

Step 9: Generate semantic entities (optional)

Generate entity files that capture semantic understanding of key files. These provide PURPOSE, not just syntax.

Skip this step if: User only wants the index, or codebase has < 10 files.

9.1 Create entities directory

mkdir -p .planning/intel/entities

9.2 Select files for entity generation

Select up to 50 files based on these criteria (in priority order):

1. High-export files: 3+ exports (likely core modules)
2. Hub files: Referenced by 5+ other files (via imports analysis)
3. Key directories: Entry points (index.js, main.js, app.js), config files
4. Structural files: Files matching convention patterns (services, controllers, models)

From the index.json, identify candidates and limit to 50 files maximum per run.

9.3 Spawn entity generator subagent

Spawn gsd-entity-generator with the selected file list.

Pass to subagent:

• Total file count
• Output directory: .planning/intel/entities/
• Slug convention: src/lib/db.ts -> src-lib-db (replace / with -, remove extension, lowercase)
• Entity template (include full template from agent definition)
• List of absolute file paths (one per line)

Task tool invocation:

# Build file list (one absolute path per line)
file_list = "\n".join(selected_files)
today = date.today().isoformat()

Task(
  prompt=f"""Generate semantic entity documentation for key codebase files.

You are a GSD entity generator. Read source files and create semantic documentation that captures PURPOSE (what/why), not just syntax.

**Parameters:**
- Files to process: {len(selected_files)}
- Output directory: .planning/intel/entities/
- Date: {today}

**Slug convention:**
- Remove leading /
- Remove file extension
- Replace / and . with -
- Lowercase everything
- Example: src/lib/db.ts -> src-lib-db

**Entity template:**
```markdown
---
path: {{absolute_path}}
type: [module|component|util|config|api|hook|service|model|test]
updated: {today}
status: active
---

# {{filename}}

## Purpose

[1-3 sentences: What does this file do? Why does it exist? What problem does it solve?]

## Exports

- `functionName(params): ReturnType` - Brief description
- `ClassName` - What this class represents

If no exports: "None"

## Dependencies

- [[internal-file-slug]] - Why needed (for internal deps)
- external-package - What it provides (for npm packages)

If no dependencies: "None"

## Used By

TBD

Process: For each file path below:

1. Read file content using Read tool
2. Analyze purpose, exports, dependencies
3. Check if entity already exists (skip if so)
4. Write entity to .planning/intel/entities/{{slug}}.md
5. PostToolUse hook syncs to graph.db automatically

Files: {file_list}

Return format: When complete, return ONLY statistics:

ENTITY GENERATION COMPLETE

Files processed: {{N}} Entities created: {{M}} Already existed: {{K}} Errors: {{E}}

Entities written to: .planning/intel/entities/

Do NOT include entity contents in your response. """, subagent_type="gsd-entity-generator" )

**Wait for completion:** Task() blocks until subagent finishes.

**Parse result:** Extract entities_created count from response for final report.

### 9.4 Verify entity generation

Confirm entities were written:

```bash
ls .planning/intel/entities/*.md 2>/dev/null | wc -l

9.5 Report entity statistics

Entity Generation Complete

Entity files created: [N] (from subagent response)
Location: .planning/intel/entities/
Graph database: Updated automatically via PostToolUse hook

Next: Intel hooks will continue incremental updates as you code.

- .planning/intel/index.json - File index with exports and imports - .planning/intel/conventions.json - Detected naming and structural patterns - .planning/intel/summary.md - Concise summary for context injection - .planning/intel/entities/*.md - Semantic entity files (optional, Step 9)

<success_criteria>

• .planning/intel/ directory created
• All JS/TS files scanned (excluding node_modules, dist, build, .git, vendor, coverage)
• index.json populated with exports and imports for each file
• conventions.json has detected patterns (naming, directories, suffixes)
• summary.md is concise (< 500 tokens)
• Statistics reported to user
• Entity files generated for key files (if Step 9 executed)
• Entity files contain Purpose section with semantic understanding </success_criteria>