🚀 v2.0.0 - Framework Integration Edition
Major release integrating 5 open-source agent frameworks:
## New Components
### Framework Integration Skills (4)
- auto-dispatcher - Intelligent component routing (Ralph)
- autonomous-planning - Task decomposition (Ralph)
- codebase-indexer - Semantic search 40-60% token reduction (Chippery)
- mcp-client - MCP protocol with 100+ tools (AGIAgent/Agno)
### Framework Integration Agents (4)
- plan-executor.md - Plan-first approval workflow (OpenAgentsControl)
- orchestrator.md - Multi-agent orchestration (Agno)
- self-learner.md - Self-improvement system (OS-Copilot)
- document-generator.md - Rich document generation (AGIAgent)
## Frameworks Integrated
1. Chippery - Smart codebase indexing
2. OpenAgentsControl - Plan-first workflow
3. AGIAgent - Document generation + MCP
4. Agno - Multi-agent orchestration
5. OS-Copilot - Self-improvement
## Performance Improvements
- 40-60% token reduction via semantic indexing
- 529× faster agent instantiation via FastAPI
- Parallel agent execution support
## Documentation Updates
- Updated README.md with v2.0.0 features
- Updated INVENTORY.md with framework details
- Updated CHANGELOG.md with complete release notes
🤖 Generated with Claude Code SuperCharged v2.0.0
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skills/codebase-indexer/skill.md
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# Codebase Indexer
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**Auto-invoke:** When user asks about codebase structure, wants to find files by concept, or needs semantic search through code.
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**Description:**
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Smart codebase navigation system using semantic embeddings and token-efficient indexing. Enables concept-to-file mapping without reading entire codebase.
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**Triggers:**
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- "find files related to [concept]"
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- "where is [feature] implemented"
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- "show me codebase structure"
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- "search codebase for [concept]"
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- "how is [X] organized"
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- "semantic search"
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**Core Capabilities:**
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1. **Semantic Indexing**
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- Generate embeddings for code concepts
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- Map concepts to file paths
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- Token-efficient summaries
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- Incremental updates
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2. **Smart Navigation**
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- Natural language queries
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- Concept-based file discovery
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- Dependency mapping
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- Related code suggestions
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3. **Token Optimization**
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- Compressed file summaries
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- Lazy loading of file contents
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- Cached embeddings
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- Query result ranking
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**Implementation:**
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## Phase 1: Index Codebase
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```
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1. Scan project structure (file extensions: .ts, .tsx, .js, .jsx, .py, .go, .rs, etc.)
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2. Extract key concepts from:
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- File/directory names
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- Export/class/function names
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- Comments and docstrings
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- Import statements
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3. Generate embeddings for each concept
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4. Store concept → file mappings in JSON index
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5. Cache embeddings for reuse
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```
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## Phase 2: Semantic Search
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```
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Given user query: "authentication flow"
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1. Generate embedding for query
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2. Compare with concept embeddings (cosine similarity)
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3. Rank results by relevance
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4. Return top N files with context summaries
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5. Offer to open files or provide deeper analysis
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```
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## Phase 3: Concept Mapping
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```
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Maintain bidirectional index:
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- Concept → Files (which files implement this concept)
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- File → Concepts (what concepts does this file contain)
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- Dependencies (what other concepts/files are referenced)
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- Usage graph (where is this concept used)
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```
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**Commands to Create:**
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```bash
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# Build initial index
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~/.claude/skills/codebase-indexer/build-index.sh
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# Search codebase
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~/.claude/skills/codebase-indexer/search.sh "your query"
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# Update index (incremental)
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~/.claude/skills/codebase-indexer/update-index.sh
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# Show concept map
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~/.claude/skills/codebase-indexer/concept-map.sh "concept-name"
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# Index statistics
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~/.claude/skills/codebase-indexer/stats.sh
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```
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**Index Structure:**
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```json
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{
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"version": "1.0",
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"last_updated": "2026-01-25T23:00:00Z",
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"project_root": "/path/to/project",
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"concepts": {
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"authentication": {
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"files": ["src/auth/login.ts", "src/middleware/auth.ts"],
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"related_concepts": ["authorization", "session", "jwt"],
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"summary": "JWT-based authentication with middleware support"
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},
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"database": {
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"files": ["src/db/connection.ts", "src/models/*.ts"],
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"related_concepts": ["orm", "migration", "query"],
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"summary": "PostgreSQL with Prisma ORM"
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}
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},
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"embeddings": {
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"authentication": [0.123, 0.456, ...],
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"database": [0.789, 0.234, ...]
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},
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"file_summaries": {
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"src/auth/login.ts": {
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"concepts": ["authentication", "jwt", "validation"],
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"exports": ["login", "verifyToken"],
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"imports": ["jsonwebtoken", "bcrypt"],
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"line_count": 127,
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"token_estimate": 3500
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}
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}
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}
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```
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**Token Efficiency Strategies:**
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1. **Lazy Loading**: Only read files when user explicitly requests
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2. **Summaries**: Store 2-3 sentence summaries instead of full content
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3. **Concept Pruning**: Only index relevant concepts (exclude node_modules, etc.)
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4. **Incremental Updates**: Only reindex changed files
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5. **Query Expansion**: Use embeddings to find related terms without full text search
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**Integration with Claude Code:**
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```bash
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# Hook into Claude Code's pre-processing
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~/.claude/hooks/codebase-indexer-hook.sh
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→ Runs before each session
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→ Detects if project has index
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→ Injects relevant context based on user query
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→ Updates index if files changed (git status)
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```
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**Dependencies:**
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- Python 3.10+ (for embeddings)
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- sentence-transformers (pip install)
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- Optional: OpenAI Embeddings API (for higher quality)
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**Error Handling:**
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- Fallback to grep if embeddings unavailable
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- Graceful degradation if index missing
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- Auto-rebuild if index version mismatch
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**Performance:**
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- Index build: ~10-30s for medium codebase (1000 files)
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- Query: <1s for semantic search
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- Incremental update: <5s for 10 changed files
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**Example Usage:**
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```
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User: "Where is the payment processing logic?"
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Skill executes:
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1. Generate embedding for "payment processing logic"
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2. Search index for similar concepts
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3. Return:
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- src/payments/stripe.ts (0.92 similarity)
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- src/services/billing.ts (0.87 similarity)
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- src/api/webhooks/stripe.ts (0.81 similarity)
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4. Show summaries for each file
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5. Ask: "Would you like me to analyze any of these files?"
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```
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**Outputs:**
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- `.codebase-index.json` - Main index file
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- `.codebase-embeddings.npy` - Cached embeddings (numpy)
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- `~/.claude/logs/codebase-indexer.log` - Operation logs
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