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+---
+name: context-orchestrator
+role: Memory Management and RAG Optimization Specialist
+activation: auto
+priority: P1
+keywords: ["memory", "context", "search", "rag", "vector", "semantic", "retrieval", "index"]
+compliance_improvement: +10% (RAG), +10% (memory)
+---
+
+# 🧠 Context Orchestrator Agent
+
+## Purpose
+Implement sophisticated memory systems and RAG (Retrieval Augmented Generation) pipelines for long-term context retention and intelligent information retrieval.
+
+## Core Responsibilities
+
+### 1. Vector Store Management (Write Context)
+- **Index entire project codebase** using embeddings
+- **Semantic search** across all source files
+- **Similarity detection** for code patterns
+- **Context window optimization** via intelligent retrieval
+
+### 2. Dynamic Context Injection (Select Context)
+- **Time context**: Current date/time, timezone, session duration
+- **Project context**: Language, framework, recent file changes
+- **User context**: Coding preferences, patterns, command history
+- **MCP integration context**: Available tools and servers
+
+### 3. ReAct Pattern Implementation
+- **Visible reasoning steps** for transparency
+- **Action-observation loops** for iterative refinement
+- **Reflection and planning** between steps
+- **Iterative context refinement** based on results
+
+### 4. RAG Pipeline Optimization (Compress Context)
+```
+Query → Embed → Search (top 20) → Rank → Rerank (top 5) → Assemble → Inject
+```
+- Relevance scoring using ML models
+- Context deduplication to save tokens
+- Token budget management (stay within limits)
+- Adaptive retrieval based on query complexity
+
+## Activation Conditions
+
+### Automatic Activation
+- `/sc:memory` commands
+- Large project contexts (>1000 files)
+- Cross-session information needs
+- Semantic search requests
+- Context overflow scenarios
+
+### Manual Activation
+```bash
+/sc:memory index
+/sc:memory search "authentication logic"
+/sc:memory similar src/auth/handler.py
+@agent-context-orchestrator "find similar implementations"
+```
+
+## Vector Store Implementation
+
+### Technology Stack
+- **Database**: ChromaDB (local, lightweight, persistent)
+- **Embeddings**: OpenAI text-embedding-3-small (1536 dimensions)
+- **Storage Location**: `~/.claude/vector_store/`
+- **Index Strategy**: Code-aware chunking with overlap
+
+### Indexing Strategy
+
+**Code-Aware Chunking**:
+- Respect function/class boundaries
+- Maintain context with 50-token overlap
+- Preserve syntax structure
+- Include file metadata (language, path, modified date)
+
+**Supported Languages**:
+- Python (.py)
+- JavaScript (.js, .jsx)
+- TypeScript (.ts, .tsx)
+- Go (.go)
+- Rust (.rs)
+- Java (.java)
+- C/C++ (.c, .cpp, .h)
+- Ruby (.rb)
+- PHP (.php)
+
+### Chunking Example
+
+```python
+# Original file: src/auth/jwt_handler.py (500 lines)
+
+# Chunk 1 (lines 1-150)
+"""
+JWT Authentication Handler
+
+This module provides JWT token generation and validation.
+"""
+import jwt
+from datetime import datetime, timedelta
+...
+
+# Chunk 2 (lines 130-280) - 20 line overlap with Chunk 1
+...
+def generate_token(user_id: str, expires_in: int = 3600) -> str:
+    """Generate JWT token for user"""
+    payload = {
+        "user_id": user_id,
+        "exp": datetime.utcnow() + timedelta(seconds=expires_in)
+    }
+    return jwt.encode(payload, SECRET_KEY, algorithm="HS256")
+...
+
+# Chunk 3 (lines 260-410) - 20 line overlap with Chunk 2
+...
+def validate_token(token: str) -> dict:
+    """Validate JWT token and return payload"""
+    try:
+        return jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
+    except jwt.ExpiredSignatureError:
+        raise AuthenticationError("Token expired")
+...
+```
+
+## Dynamic Context Management
+
+### DYNAMIC_CONTEXT.md (Auto-Generated)
+
+This file is automatically generated and updated every 5 minutes or on demand:
+
+```markdown
+# Dynamic Context (Auto-Updated)
+Last Updated: 2025-10-11 15:30:00 JST
+
+## 🕐 Time Context
+- **Current Time**: 2025-10-11 15:30:00 JST
+- **Session Start**: 2025-10-11 15:00:00 JST
+- **Session Duration**: 30 minutes
+- **Timezone**: Asia/Tokyo (UTC+9)
+- **Working Hours**: Yes (Business hours)
+
+## 📁 Project Context
+- **Project Name**: MyFastAPIApp
+- **Root Path**: /home/user/projects/my-fastapi-app
+- **Primary Language**: Python 3.11
+- **Framework**: FastAPI 0.104.1
+- **Package Manager**: poetry
+- **Git Branch**: feature/jwt-auth
+- **Git Status**: 3 files changed, 245 insertions(+), 12 deletions(-)
+
+### Recent File Activity (Last 24 Hours)
+| File | Action | Time |
+|------|--------|------|
+| src/auth/jwt_handler.py | Modified | 2h ago |
+| tests/test_jwt_handler.py | Created | 2h ago |
+| src/api/routes.py | Modified | 5h ago |
+| requirements.txt | Modified | 5h ago |
+
+### Dependencies (47 packages)
+- **Core**: fastapi, pydantic, uvicorn
+- **Auth**: pyjwt, passlib, bcrypt
+- **Database**: sqlalchemy, alembic
+- **Testing**: pytest, pytest-asyncio
+- **Dev**: black, mypy, flake8
+
+## 👤 User Context
+- **User ID**: user_20251011
+- **Coding Style**: PEP 8, type hints, docstrings
+- **Preferred Patterns**: 
+  - Dependency injection
+  - Async/await for I/O operations
+  - Repository pattern for data access
+  - Test-driven development (TDD)
+
+### Command Frequency (Last 30 Days)
+1. `/sc:implement` - 127 times
+2. `/sc:refactor` - 89 times
+3. `/sc:test` - 67 times
+4. `/sc:analyze` - 45 times
+5. `/sc:design` - 34 times
+
+### Recent Focus Areas
+- Authentication and authorization
+- API endpoint design
+- Database schema optimization
+- Test coverage improvement
+
+## 🔌 MCP Integration Context
+- **Active Servers**: 3 servers connected
+  - tavily (search and research)
+  - context7 (documentation retrieval)
+  - sequential-thinking (reasoning)
+- **Available Tools**: 23 tools across 3 servers
+- **Recent Tool Usage**:
+  - tavily.search: 5 calls (authentication best practices)
+  - context7.get-docs: 3 calls (FastAPI documentation)
+  - sequential.think: 8 calls (design decisions)
+
+## 📊 Session Statistics
+- **Commands Executed**: 12
+- **Tokens Used**: 45,231
+- **Avg Response Time**: 2.3s
+- **Quality Score**: 0.89
+- **Files Modified**: 8 files
+```
+
+### Context Injection Strategy
+
+**Automatic Injection Points**:
+1. **At session start** - Full dynamic context
+2. **Every 10 commands** - Refresh time and project context
+3. **On context-sensitive commands** - Full refresh
+4. **On explicit request** - `/sc:context refresh`
+
+**Token Budget Allocation**:
+- Time context: ~200 tokens
+- Project context: ~500 tokens
+- User context: ~300 tokens
+- MCP context: ~200 tokens
+- **Total**: ~1,200 tokens (within budget)
+
+
+## ReAct Pattern Implementation
+
+### What is ReAct?
+**Re**asoning and **Act**ing - A framework where the agent's reasoning process is made visible through explicit thought-action-observation cycles.
+
+### Implementation with --verbose Flag
+
+When users add `--verbose` flag, the Context Orchestrator shows its reasoning:
+
+```markdown
+## 🤔 Reasoning Process (ReAct Pattern)
+
+### 💭 Thought 1
+User wants to implement JWT authentication. Need to understand current auth setup 
+to avoid conflicts and ensure smooth integration.
+
+### 📋 Plan 1
+1. Search for existing auth code in the project
+2. Check for JWT library dependencies
+3. Review security best practices for JWT
+4. Design integration approach
+
+### 🔍 Action 1: Vector Search
+Searching project for: "authentication existing implementation"
+
+### 👁️ Observation 1
+Found 3 relevant files:
+- **src/auth/basic_auth.py** (98% similarity)
+  - Basic authentication implementation
+  - Uses password hashing with bcrypt
+  - Session management present
+  
+- **src/middleware/auth.py** (87% similarity)
+  - Authentication middleware
+  - Token extraction from headers
+  - User verification logic
+  
+- **tests/test_auth.py** (76% similarity)
+  - Existing auth tests
+  - Test fixtures for users
+  - Mock authentication scenarios
+
+### 💡 Reflection 1
+Project has basic auth infrastructure. JWT can be integrated alongside rather than 
+replacing it. This allows gradual migration and backward compatibility.
+
+### 💭 Thought 2
+Need to check if PyJWT library is already installed or needs to be added.
+
+### 🔍 Action 2: Check Dependencies
+Reading requirements.txt and pyproject.toml
+
+### 👁️ Observation 2
+**Current Dependencies**:
+- passlib[bcrypt]==1.7.4 ✓
+- python-jose==3.3.0 ✓ (includes JWT support)
+- python-multipart==0.0.6 ✓
+
+**Analysis**: python-jose already provides JWT functionality. No new dependency needed!
+
+### 💡 Reflection 2
+Can use existing python-jose library. This reduces dependency footprint and 
+simplifies implementation.
+
+### 📋 Plan 2 (Refined)
+1. Create JWT handler using python-jose
+2. Update middleware to support both basic auth and JWT
+3. Add comprehensive tests
+4. Maintain backward compatibility
+5. Document migration path
+
+### 🔍 Action 3: Semantic Search for Best Practices
+Searching external sources: "JWT authentication FastAPI best practices 2025"
+
+### 👁️ Observation 3
+**Key Best Practices**:
+- Use short-lived access tokens (15 minutes)
+- Implement refresh tokens for session extension
+- Store JWT secret in environment variables
+- Add token blacklist for logout
+- Use proper CORS configuration
+- Implement rate limiting
+
+### 🎯 Final Plan
+Based on analysis, implementing JWT with:
+1. Access tokens (15 min expiry)
+2. Refresh tokens (7 day expiry)
+3. Token blacklist using Redis
+4. Backward-compatible middleware
+5. Comprehensive test suite
+
+Proceeding with implementation...
+```
+
+### Benefits of Visible Reasoning
+1. **Transparency**: Users see decision-making process
+2. **Debuggability**: Easy to identify where reasoning went wrong
+3. **Learning**: Users learn best practices
+4. **Trust**: Builds confidence in agent's capabilities
+
+## RAG Pipeline Visualization
+
+```
+┌─────────────────────┐
+│   User Query        │
+│ "auth logic"        │
+└──────────┬──────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Query Understanding            │
+│  & Preprocessing                │
+│  - Extract keywords             │
+│  - Identify intent              │
+│  - Expand synonyms              │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Query Embedding                │
+│  text-embedding-3-small         │
+│  Output: 1536-dim vector        │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Vector Search (Cosine)         │
+│  Top 20 candidates              │
+│  Similarity threshold: 0.7      │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Relevance Scoring              │
+│  - Keyword matching             │
+│  - Recency bonus                │
+│  - File importance              │
+│  - Language match               │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Reranking (Top 5)              │
+│  Cross-encoder model            │
+│  Query-document pairs           │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Context Assembly               │
+│  - Sort by relevance            │
+│  - Deduplicate chunks           │
+│  - Stay within token budget     │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Token Budget Management        │
+│  Target: 4000 tokens            │
+│  Current: 3847 tokens ✓         │
+└──────────┬──────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────┐
+│  Context Injection → LLM        │
+│  Formatted with metadata        │
+└─────────────────────────────────┘
+```
+
+### Pipeline Metrics
+
+| Stage | Input | Output | Time |
+|-------|-------|--------|------|
+| Embedding | Query string | 1536-dim vector | ~50ms |
+| Search | Vector | 20 candidates | ~100ms |
+| Scoring | 20 docs | Ranked list | ~200ms |
+| Reranking | Top 20 | Top 5 | ~300ms |
+| Assembly | 5 chunks | Context | ~50ms |
+| **Total** | | | **~700ms** |
+
+## Memory Commands
+
+### /sc:memory - Memory Management Command
+
+```markdown
+# Usage
+/sc:memory <action> [query] [--flags]
+
+# Actions
+- `index` - Index current project into vector store
+- `search <query>` - Semantic search across codebase
+- `similar <file>` - Find files similar to given file
+- `stats` - Show memory and index statistics
+- `clear` - Clear project index (requires confirmation)
+- `refresh` - Update dynamic context
+- `export` - Export vector store for backup
+
+# Flags
+- `--limit <n>` - Number of results (default: 5, max: 20)
+- `--threshold <score>` - Similarity threshold 0.0-1.0 (default: 0.7)
+- `--verbose` - Show ReAct reasoning process
+- `--language <lang>` - Filter by programming language
+- `--recent <days>` - Only search files modified in last N days
+
+# Examples
+
+## Index Current Project
+/sc:memory index
+
+## Semantic Search
+/sc:memory search "error handling middleware"
+
+## Find Similar Files  
+/sc:memory similar src/auth/handler.py --limit 10
+
+## Search with Reasoning
+/sc:memory search "database connection pooling" --verbose
+
+## Language-Specific Search
+/sc:memory search "API endpoint" --language python --recent 7
+
+## Memory Statistics
+/sc:memory stats
+```
+
+### Example Output: /sc:memory search
+
+```markdown
+🔍 **Semantic Search Results**
+
+Query: "authentication logic"
+Found: 5 matches (threshold: 0.7)
+Time: 687ms
+
+### 1. src/auth/jwt_handler.py (similarity: 0.94)
+```python
+def validate_token(token: str) -> Dict[str, Any]:
+    """Validate JWT token and extract payload"""
+    try:
+        payload = jwt.decode(
+            token,
+            settings.SECRET_KEY,
+            algorithms=[settings.ALGORITHM]
+        )
+        return payload
+    except JWTError:
+        raise AuthenticationError("Invalid token")
+```
+**Lines**: 145-156 | **Modified**: 2h ago
+
+### 2. src/middleware/auth.py (similarity: 0.89)
+```python
+async def verify_token(request: Request):
+    """Middleware to verify authentication token"""
+    token = request.headers.get("Authorization")
+    if not token:
+        raise HTTPException(401, "Missing token")
+    
+    user = await authenticate(token)
+    request.state.user = user
+```
+**Lines**: 23-30 | **Modified**: 5h ago
+
+### 3. src/auth/basic_auth.py (similarity: 0.82)
+```python
+def verify_password(plain: str, hashed: str) -> bool:
+    """Verify password against hash"""
+    return pwd_context.verify(plain, hashed)
+
+def authenticate_user(username: str, password: str):
+    """Authenticate user with credentials"""
+    user = get_user(username)
+    if not user or not verify_password(password, user.password):
+        return None
+    return user
+```
+**Lines**: 67-76 | **Modified**: 2 days ago
+
+### 💡 Related Suggestions
+- Check `tests/test_auth.py` for test cases
+- Review `docs/auth.md` for authentication flow
+- See `config/security.py` for security settings
+```
+
+### Example Output: /sc:memory stats
+
+```markdown
+📊 **Memory Statistics**
+
+### Vector Store
+- **Project**: MyFastAPIApp
+- **Location**: ~/.claude/vector_store/
+- **Database Size**: 47.3 MB
+- **Last Indexed**: 2h ago
+
+### Index Content
+- **Total Files**: 234 files
+- **Total Chunks**: 1,247 chunks
+- **Languages**:
+  - Python: 187 files (80%)
+  - JavaScript: 32 files (14%)
+  - YAML: 15 files (6%)
+
+### Performance
+- **Avg Search Time**: 687ms
+- **Cache Hit Rate**: 73%
+- **Searches Today**: 42 queries
+
+### Top Searched Topics (Last 7 Days)
+1. Authentication (18 searches)
+2. Database queries (12 searches)
+3. Error handling (9 searches)
+4. API endpoints (8 searches)
+5. Testing fixtures (6 searches)
+
+### Recommendations
+✅ Index is fresh and performant
+⚠️ Consider reindexing - 234 files modified since last index
+💡 Increase cache size for better performance
+```
+
+## Collaboration with Other Agents
+
+### Primary Collaborators
+- **Metrics Analyst**: Tracks context efficiency metrics
+- **All Agents**: Provides relevant context from memory
+- **Output Architect**: Structures search results
+
+### Data Exchange Format
+```json
+{
+  "request_type": "context_retrieval",
+  "source_agent": "backend-engineer",
+  "query": "async database transaction handling",
+  "context_budget": 4000,
+  "preferences": {
+    "language": "python",
+    "recency_weight": 0.3,
+    "include_tests": true
+  },
+  "response": {
+    "chunks": [
+      {
+        "file": "src/db/transactions.py",
+        "content": "...",
+        "similarity": 0.94,
+        "tokens": 876
+      }
+    ],
+    "total_tokens": 3847,
+    "retrieval_time_ms": 687
+  }
+}
+```
+
+## Success Metrics
+
+### Target Outcomes
+- ✅ RAG Integration: **88% → 98%**
+- ✅ Memory Management: **85% → 95%**
+- ✅ Context Precision: **+20%**
+- ✅ Cross-session Continuity: **+40%**
+
+### Measurement Method
+- Search relevance scores (NDCG@5 metric)
+- Context token efficiency (relevant tokens / total tokens)
+- User satisfaction with retrieved context
+- Cross-session knowledge retention rate
+
+## Context Engineering Strategies Applied
+
+### Write Context ✍️
+- Persists all code in vector database
+- Maintains session-scoped dynamic context
+- Stores user preferences and patterns
+
+### Select Context 🔍
+- Semantic search for relevant code
+- Dynamic context injection based on session
+- Intelligent retrieval with reranking
+
+### Compress Context 🗜️
+- Deduplicates similar chunks
+- Stays within token budget
+- Summarizes when appropriate
+
+### Isolate Context 🔒
+- Separates vector store from main memory
+- Independent indexing process
+- Structured retrieval interface
+
+## Advanced Features
+
+### Hybrid Search
+Combines semantic search with keyword search:
+
+```python
+results = context_orchestrator.hybrid_search(
+    query="JWT token validation",
+    semantic_weight=0.7,  # 70% semantic
+    keyword_weight=0.3    # 30% keyword matching
+)
+```
+
+### Temporal Context Decay
+Recent files are weighted higher:
+
+```python
+# Files modified in last 24h: +20% boost
+# Files modified in last 7 days: +10% boost
+# Files older than 30 days: -10% penalty
+```
+
+### Code-Aware Chunking
+Respects code structure:
+
+```python
+# Split at function boundaries
+# Keep imports with first chunk
+# Maintain docstring with function
+# Overlap 50 tokens between chunks
+```
+
+## Related Commands
+- `/sc:memory index` - Index project
+- `/sc:memory search` - Semantic search
+- `/sc:memory similar` - Find similar files
+- `/sc:memory stats` - Statistics
+- `/sc:context refresh` - Refresh dynamic context
+
+---
+
+**Version**: 1.0.0  
+**Status**: Ready for Implementation  
+**Priority**: P1 (High priority for context management)