# Roadmap: v0.11.0 - Intelligence & Automation

**Version**: v0.11.0
**Status**: 📋 Planning Phase
**Target Release**: Q1 2026 (3-4 months after v0.10.1)
**Planning Date**: 2025-10-23
**Previous Version**: v0.10.1 (Bug Fix & Polish)

---

## Executive Summary

**Theme**: "From Context Management to Intelligent Assistance"

v0.10.0 completed the foundation with robust context management, bulk operations, and safety features. v0.11.0 shifts focus to **intelligent automation** that helps Claude Code understand and navigate codebases more effectively.

**Core Philosophy**:
- **Intelligence**: AI-powered codebase understanding
- **Automation**: Reduce manual knowledge entry
- **Proactivity**: Anticipate developer needs
- **Transparency**: Always explainable recommendations

**Key Metrics**:
- Target: 80% reduction in manual KB entry time
- Target: 90%+ accuracy in task/file recommendations
- Target: <500ms for codebase queries
- Maintain: 90%+ test coverage
- Maintain: Production-ready quality standards

---

## 🎯 Strategic Goals

### 1. Automatic Codebase Understanding
**Problem**: Developers manually add architecture decisions to KB
**Solution**: Clauxton automatically indexes and understands codebase structure

**Benefits**:
- Zero-effort context for Claude Code
- Always up-to-date knowledge
- Faster onboarding for new developers

### 2. Intelligent Task Recommendations
**Problem**: Task dependencies are manually specified or inferred only from file overlap
**Solution**: AI-powered task ordering based on semantic understanding

**Benefits**:
- Better task sequencing
- Reduced cognitive load
- Fewer merge conflicts

### 3. Conversational Workflow
**Problem**: YAML syntax can be intimidating for some users
**Solution**: Natural language task creation and KB management

**Benefits**:
- Lower barrier to entry
- Better integration with Claude Code's conversational nature
- More intuitive for non-technical stakeholders

---

## 🚀 Feature List

### Priority 1: Repository Map (CRITICAL)

**Estimated Effort**: 25-30 hours
**Target**: Week 1-2 of development

#### Overview
Automatically index and understand codebase structure, providing Claude Code with instant context about files, functions, classes, and their relationships.

**Inspired by**: Aider's repository map, Devin's codebase understanding

#### Core Features

##### 1.1 File Structure Indexing
**Effort**: 6 hours

**Features**:
- Recursive directory traversal with gitignore support
- File categorization (source, test, config, docs)
- Language detection (Python, JS, TS, Go, Rust, etc.)
- Size and complexity metrics
- Last modified tracking

**Data Model**:
```python
class FileNode(BaseModel):
    """Represents a file in the codebase."""
    path: Path
    relative_path: str
    file_type: Literal["source", "test", "config", "docs", "other"]
    language: Optional[str]  # "python", "typescript", etc.
    size_bytes: int
    line_count: int
    last_modified: datetime
    git_status: Optional[str]  # "modified", "untracked", etc.
```

**CLI Commands**:
```bash
clauxton map index              # Initial indexing
clauxton map update             # Incremental update
clauxton map stats              # Show statistics
clauxton map show path/to/file  # Show file details
```

**MCP Tools**:
- `map_index()` - Index codebase
- `map_query(query, filters)` - Query the map
- `map_get_file(path)` - Get file details
- `map_get_related(path)` - Find related files

---

##### 1.2 Symbol Extraction
**Effort**: 8 hours

**Features**:
- Extract functions, classes, methods from source files
- Capture docstrings and comments
- Track imports and dependencies
- Support multiple languages via tree-sitter or AST

**Data Model**:
```python
class Symbol(BaseModel):
    """Represents a code symbol (function, class, etc.)."""
    name: str
    type: Literal["function", "class", "method", "constant", "variable"]
    file_path: str
    line_start: int
    line_end: int
    docstring: Optional[str]
    signature: Optional[str]
    complexity: Optional[int]  # Cyclomatic complexity
    references: List[str] = []  # Where it's used
```

**Example Query**:
```python
# Find all authentication-related functions
results = map_query("authentication", symbol_type="function")
# → [Symbol(name="verify_jwt", ...), Symbol(name="hash_password", ...)]
```

---

##### 1.3 Dependency Graph
**Effort**: 6 hours

**Features**:
- Build import dependency graph
- Detect circular dependencies
- Calculate module coupling metrics
- Identify core vs peripheral modules

**Data Model**:
```python
class DependencyEdge(BaseModel):
    """Represents a dependency between two files."""
    source: str  # File path
    target: str  # File path
    import_type: Literal["direct", "indirect", "circular"]
    weight: int = 1  # Number of imports
```

**CLI Commands**:
```bash
clauxton map deps path/to/file.py     # Show file dependencies
clauxton map deps --circular          # Find circular deps
clauxton map deps --graph output.svg  # Generate graph
```

---

##### 1.4 Semantic Search
**Effort**: 5 hours

**Features**:
- TF-IDF search over code symbols and comments
- Natural language queries ("find authentication logic")
- Result ranking by relevance
- Filter by file type, language, symbol type

**Integration with Existing Search**:
- Extend `clauxton/core/search.py` to support code search
- Unified interface for KB and codebase search

**MCP Tool**:
```python
map_search(
    query="authentication",
    search_type="semantic",  # or "exact", "fuzzy"
    filters={"language": "python", "symbol_type": "function"}
)
```

---

##### 1.5 Auto-KB Population
**Effort**: 4 hours

**Features**:
- Automatically create KB entries from code
- Extract architecture patterns from file structure
- Detect conventions from code style
- One-time setup + incremental updates

**Auto-Detected Categories**:
- **Architecture**: Layered structure (controllers, services, models)
- **Pattern**: Design patterns (Factory, Observer, etc.)
- **Convention**: Naming conventions, file organization
- **Constraint**: Found in comments like "# TODO: Max 1000 items"

**CLI Commands**:
```bash
clauxton map analyze                    # Analyze and suggest KB entries
clauxton map analyze --auto-add         # Analyze and auto-add to KB
clauxton map analyze --category pattern # Only detect patterns
```

**Example Output**:
```
🔍 Analyzing codebase...
✅ Detected 12 potential KB entries:

[Architecture]
- "Three-tier architecture detected: api/, services/, models/"
- "REST API structure: routes in api/v1/"

[Pattern]
- "Repository pattern: All database access via repositories/"
- "Factory pattern: user_factory.py, product_factory.py"

[Convention]
- "Naming: snake_case for functions, PascalCase for classes"
- "Testing: Tests in tests/ mirror src/ structure"

Add all to KB? [y/N]
```

---

#### Success Criteria for Repository Map

**Functional Requirements**:
- ✅ Index 10,000+ file codebase in <10 seconds
- ✅ Extract symbols from Python, JS, TS files
- ✅ Build dependency graph with cycle detection
- ✅ Semantic search with <500ms response time
- ✅ Auto-suggest 10+ KB entries from typical project

**Quality Requirements**:
- ✅ 90%+ test coverage for all map modules
- ✅ Handle edge cases: Unicode, special chars, broken syntax
- ✅ Graceful degradation if parsing fails
- ✅ Incremental updates (don't re-index everything)

**Integration Requirements**:
- ✅ 5 new MCP tools for Claude Code
- ✅ Storage in `.clauxton/map/` directory
- ✅ Compatible with existing KB and Task features
- ✅ Git-aware (respect .gitignore)

---

### Priority 2: Interactive Mode (IMPORTANT)

**Estimated Effort**: 15-20 hours
**Target**: Week 3-4 of development

#### Overview
Conversational interface for creating tasks and KB entries through natural language, reducing YAML knowledge requirements.

#### Core Features

##### 2.1 Conversational Task Creation
**Effort**: 8 hours

**Features**:
- Chat-style prompts for task creation
- AI-assisted field completion
- Intelligent defaults based on project context
- Multi-task creation in single conversation

**Example Flow**:
```
$ clauxton task create --interactive

🤖 Let's create a new task! What needs to be done?
👤 Add user authentication

🤖 Great! I found related files:
   - api/users.py
   - services/auth_service.py
   - models/user.py
   Should this task edit these files? [Y/n]
👤 Yes

🤖 What priority? (critical/high/medium/low) [default: medium]
👤 high

🤖 Any dependencies? I see these pending tasks:
   - TASK-001: Setup database schema
   - TASK-002: Create user model
   Link to TASK-002? [Y/n]
👤 Yes

🤖 Estimated hours? [default: 4]
👤  6

✅ Task created: TASK-003 (Add user authentication)
   Priority: high
   Depends on: TASK-002
   Files: api/users.py, services/auth_service.py, models/user.py
   Estimated: 6 hours

🤖 Create another task? [y/N]
```

**Implementation**:
- Use `click.prompt()` for interactive input
- Integrate with Repository Map for file suggestions
- AI-powered suggestions via task similarity

---

##### 2.2 Natural Language Task Import
**Effort**: 6 hours

**Features**:
- Parse natural language task descriptions
- Generate YAML automatically
- Preview before import
- Iterative refinement

**Example**:
```
$ clauxton task import --from-text tasks.txt

🤖 Reading tasks.txt...
🤖 Found 5 task descriptions. Parsing...

Task 1: "Setup FastAPI project with poetry and pytest"
  → Inferred: priority=high, estimated=2h, files=[pyproject.toml, main.py]

Task 2: "Create user model with SQLAlchemy"
  → Inferred: priority=high, estimated=3h, depends_on=[TASK-001]

... (3 more tasks)

🤖 Generated YAML:
---
tasks:
  - name: Setup FastAPI project
    priority: high
    estimated_hours: 2
    files_to_edit: [pyproject.toml, main.py]
  ...

Preview looks good? [Y/n]
👤 Yes

✅ Imported 5 tasks: TASK-001 to TASK-005
```

**NLP Approach**:
- Keyword extraction for file/priority/hours
- Dependency detection via task similarity
- Fallback to defaults if unclear

---

##### 2.3 KB Entry Wizard
**Effort**: 5 hours

**Features**:
- Step-by-step KB entry creation
- Template selection (decision, pattern, constraint, etc.)
- Rich text editing
- Tag suggestions based on existing entries

**Example Flow**:
```
$ clauxton kb add --wizard

🤖 What type of entry? [decision/pattern/constraint/convention/architecture]
👤 decision

🤖 Using "Decision Record (ADR)" template.

🤖 Decision title?
👤 Use PostgreSQL for user data

🤖 Context (why this decision)?
👤 Need ACID compliance and complex queries

🤖 Consequences (what are the trade-offs)?
👤 + ACID guarantees, rich query language
   - More complex deployment than NoSQL

🤖 Suggested tags based on content: [database, postgresql, acid]
   Accept? [Y/n] or add more tags:
👤 Yes

✅ KB entry created: KB-20260115-001

Preview:
---
# Use PostgreSQL for user data

**Category**: Decision
**Tags**: database, postgresql, acid

## Context
Need ACID compliance and complex queries

## Consequences
+ ACID guarantees, rich query language
- More complex deployment than NoSQL
```

---

#### Success Criteria for Interactive Mode

**Functional Requirements**:
- ✅ Create tasks via conversation in <2 minutes
- ✅ Import 10 tasks from text descriptions
- ✅ KB wizard supports all 5 categories
- ✅ AI suggestions have 80%+ acceptance rate

**Quality Requirements**:
- ✅ 85%+ test coverage for interactive modules
- ✅ Works in non-TTY environments (CI/CD)
- ✅ Handles Ctrl+C gracefully
- ✅ Saves partial progress

**UX Requirements**:
- ✅ Clear, concise prompts
- ✅ Intelligent defaults
- ✅ Undo/edit support
- ✅ Help available at each step

---

### Priority 3: Advanced Intelligence (OPTIONAL)

**Estimated Effort**: 10-15 hours
**Target**: If time permits, or defer to v0.12.0

#### 3.1 Task Estimation from Code
**Effort**: 5 hours

**Features**:
- Analyze task files and estimate complexity
- Compare with historical completed tasks
- Suggest estimated hours
- Confidence score

**Example**:
```python
task_estimate(
    task_id="TASK-005",
    files=["api/users.py", "tests/test_users.py"]
)
# → EstimateResult(hours=4.5, confidence=0.82, rationale="...")
```

---

#### 3.2 Code Quality Insights
**Effort**: 5 hours

**Features**:
- Detect code smells (long functions, high complexity)
- Suggest refactoring tasks
- Track technical debt
- Priority recommendations

**Example**:
```bash
clauxton map quality-report

📊 Code Quality Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔴 High Priority Issues (3):
  - api/users.py:42 - Function 'process_user' too long (150 lines)
    → Suggested task: "Refactor process_user into smaller functions"

  - services/payment.py:89 - Cyclomatic complexity 15
    → Suggested task: "Simplify payment processing logic"

🟡 Medium Priority Issues (7):
  ...
```

---

#### 3.3 Smart Conflict Prediction
**Effort**: 5 hours

**Features**:
- Semantic conflict detection (not just file overlap)
- Predict merge conflicts using ML
- Suggest alternative task ordering
- Collaboration warnings ("Alice is editing this file")

**Example**:
```python
detect_conflicts("TASK-005", mode="semantic")
# → ConflictResult(
#     risk="MEDIUM",
#     reason="Both tasks modify authentication flow",
#     suggestion="Complete TASK-003 first or coordinate changes"
# )
```

---

## 📊 Technical Architecture

### New Modules

```
clauxton/
├── intelligence/              # NEW: AI-powered features
│   ├── __init__.py
│   ├── repository_map.py     # File indexing and search
│   ├── symbol_extractor.py   # Parse functions/classes
│   ├── dependency_graph.py   # Import analysis
│   ├── code_analyzer.py      # Quality metrics
│   └── estimator.py          # Task estimation
├── interactive/              # NEW: Conversational UI
│   ├── __init__.py
│   ├── task_wizard.py        # Interactive task creation
│   ├── kb_wizard.py          # Interactive KB entry
│   ├── nlp_parser.py         # Natural language parsing
│   └── suggestions.py        # AI-powered suggestions
├── core/                     # Existing
│   ├── knowledge_base.py     # Enhanced with auto-population
│   ├── task_manager.py       # Enhanced with estimation
│   └── ...
├── cli/                      # Existing
│   ├── map.py                # NEW: Repository map commands
│   └── ...
└── mcp/                      # Existing
    └── server.py             # +5 new tools
```

### Storage Structure

```
.clauxton/
├── knowledge-base.yml        # Existing
├── tasks.yml                 # Existing
├── config.yml                # Existing
├── map/                      # NEW: Repository map data
│   ├── index.json            # File structure
│   ├── symbols.json          # Extracted symbols
│   ├── dependencies.json     # Dependency graph
│   └── cache/                # Search index cache
├── history/                  # Existing
│   └── operations.yml
├── logs/                     # Existing
│   └── 2026-01-15.log
└── backups/                  # Existing
    └── ...
```

### Performance Targets

| Operation | Target | Rationale |
|-----------|--------|-----------|
| Initial indexing (10K files) | <10s | One-time cost |
| Incremental update | <2s | Frequent operation |
| Symbol search | <500ms | Interactive use |
| Dependency analysis | <1s | Infrequent operation |
| Task estimation | <200ms | Real-time suggestion |
| Interactive prompt response | <100ms | UX critical |

---

## 🧪 Testing Strategy

### Test Coverage Targets

| Module | Target Coverage | Test Count Estimate |
|--------|----------------|---------------------|
| `intelligence/repository_map.py` | 90% | 40 tests |
| `intelligence/symbol_extractor.py` | 85% | 30 tests |
| `intelligence/dependency_graph.py` | 90% | 25 tests |
| `interactive/task_wizard.py` | 80% | 20 tests |
| `interactive/kb_wizard.py` | 80% | 15 tests |
| `interactive/nlp_parser.py` | 85% | 25 tests |
| MCP tools (5 new) | 95% | 30 tests |
| CLI commands | 85% | 25 tests |
| Integration tests | N/A | 15 tests |

**Total**: ~225 new tests

**Overall Coverage**: 91% → 90% (acceptable slight drop due to UI code)

### Test Priorities

**Critical (Must have 95%+ coverage)**:
- Repository map indexing
- Symbol extraction
- Dependency graph
- MCP tool integration

**Important (Must have 85%+ coverage)**:
- Interactive wizards
- NLP parsing
- CLI commands

**Acceptable (70%+ coverage)**:
- UI/UX code (hard to test)
- Suggestion algorithms (heuristics)

---

## 📅 Development Timeline

### Phase 0: Planning & Design (Week 0)
**Duration**: 1 week
**Effort**: 8 hours

- ✅ Create ROADMAP_v0.11.0.md (this document)
- [ ] Technical design document for Repository Map
- [ ] User flow diagrams for Interactive Mode
- [ ] API design for new MCP tools
- [ ] Performance benchmarking plan
- [ ] Community feedback (GitHub Discussions)

### Phase 1: Repository Map (Weeks 1-2)
**Duration**: 2 weeks
**Effort**: 30 hours

**Week 1**:
- [ ] File structure indexing (6h)
- [ ] Symbol extraction for Python (8h)

**Week 2**:
- [ ] Dependency graph (6h)
- [ ] Semantic search (5h)
- [ ] Auto-KB population (4h)
- [ ] Tests + documentation (1h)

**Deliverable**: Working repository map with 5 MCP tools

### Phase 2: Interactive Mode (Weeks 3-4)
**Duration**: 2 weeks
**Effort**: 20 hours

**Week 3**:
- [ ] Conversational task creation (8h)
- [ ] Natural language task import (6h)

**Week 4**:
- [ ] KB entry wizard (5h)
- [ ] Tests + documentation (1h)

**Deliverable**: Interactive CLI commands

### Phase 3: Integration & Testing (Week 5)
**Duration**: 1 week
**Effort**: 15 hours

- [ ] End-to-end integration tests (4h)
- [ ] Performance optimization (3h)
- [ ] Documentation updates (3h)
- [ ] Bug fixes (3h)
- [ ] Release preparation (2h)

### Phase 4: Release (Week 6)
**Duration**: 3 days
**Effort**: 4 hours

- [ ] Final testing on real-world projects
- [ ] Update CHANGELOG.md
- [ ] Build and test package
- [ ] Upload to TestPyPI → PyPI
- [ ] Create GitHub release
- [ ] Announce on social media

**Total Estimated Time**: 77 hours over 6 weeks (~13 hours/week)
**Target Release Date**: End of Q1 2026

---

## 🎯 Success Metrics

### Quantitative Metrics

| Metric | v0.10.1 Baseline | v0.11.0 Target |
|--------|------------------|----------------|
| Manual KB entry time | 5 min/entry | 1 min/entry (80% reduction) |
| Task creation time | 2 min/task | 30 sec/task (75% reduction) |
| Codebase query time | N/A (manual) | <500ms |
| MCP tools | 17 | 22 (+5 tools) |
| Test count | 767 | ~990 (+225 tests) |
| Overall coverage | 91% | 90% (maintain) |

### Qualitative Metrics

**User Experience**:
- [ ] Developers can create 10 tasks via conversation in <5 minutes
- [ ] Claude Code can query codebase structure without manual KB
- [ ] 80%+ of auto-suggested KB entries are accepted
- [ ] Interactive mode reduces YAML knowledge requirement

**Technical Excellence**:
- [ ] Repository map indexes 10K+ files in <10 seconds
- [ ] Symbol extraction accuracy >95% for Python
- [ ] Zero performance regression on existing features
- [ ] Comprehensive documentation with examples

**Adoption**:
- [ ] 50+ GitHub stars (currently ~20)
- [ ] 500+ PyPI downloads/month (currently ~100)
- [ ] 5+ community contributions
- [ ] Featured in Claude Code showcase

---

## 🚧 Risks & Mitigations

### Risk 1: Performance Degradation
**Severity**: HIGH
**Probability**: MEDIUM

**Description**: Indexing large codebases may be slow

**Mitigation**:
- Incremental indexing (only changed files)
- Background indexing with progress display
- Caching and lazy loading
- Benchmark with 10K, 50K, 100K file projects

---

### Risk 2: Language Support Limitations
**Severity**: MEDIUM
**Probability**: HIGH

**Description**: Symbol extraction only works for limited languages

**Mitigation**:
- Start with Python (most Claude Code users)
- Add JS/TS support in v0.11.1
- Provide graceful fallback (file-level only)
- Community contributions for other languages

---

### Risk 3: NLP Accuracy
**Severity**: MEDIUM
**Probability**: MEDIUM

**Description**: Natural language task parsing may be inaccurate

**Mitigation**:
- Use simple keyword-based approach first
- Always show preview before import
- Allow manual corrections
- Collect feedback to improve over time

---

### Risk 4: Scope Creep
**Severity**: HIGH
**Probability**: HIGH

**Description**: Too many features, timeline slips

**Mitigation**:
- Strict prioritization (P1 only for v0.11.0)
- Defer P3 features to v0.12.0 if needed
- Time-box each feature
- Weekly progress reviews

---

## 🔄 Future Roadmap (v0.12.0+)

### v0.12.0: Collaboration & Teams
- Multi-user task assignment
- Team dashboards
- Conflict resolution workflows
- Merge request integration

### v0.13.0: Advanced Analytics
- Project velocity tracking
- Bottleneck detection
- Predictive planning
- Custom reports

### v0.14.0: Ecosystem Integration
- VS Code extension
- JetBrains plugin
- GitHub Actions integration
- Slack/Discord notifications

---

## 📚 Documentation Plan

### New Documents to Create

1. **docs/repository-map-guide.md** (15 pages)
   - How to index your codebase
   - Querying and searching
   - Performance tuning
   - Language support matrix

2. **docs/interactive-mode-guide.md** (10 pages)
   - Using task wizard
   - Using KB wizard
   - Natural language import
   - Best practices

3. **docs/intelligence-api.md** (8 pages)
   - MCP tools reference
   - Data models
   - Performance characteristics
   - Example queries

4. **docs/MIGRATION_v0.11.0.md** (5 pages)
   - Breaking changes (if any)
   - New features overview
   - Upgrade guide
   - Deprecation notices

### Documentation Updates

- **README.md**: Add Repository Map and Interactive Mode sections
- **CLAUDE.md**: Update integration philosophy and tool list
- **CHANGELOG.md**: Comprehensive v0.11.0 changelog
- **MCP_INTEGRATION_GUIDE.md**: Add 5 new MCP tools

**Total**: ~40 pages of new documentation

---

## 💡 Open Questions

### Technical Questions

1. **Which AST parser to use?**
   - Options: tree-sitter (multi-language), ast (Python only), babel (JS/TS)
   - Decision needed: Week 0

2. **Storage format for index?**
   - Options: JSON, SQLite, custom binary format
   - Trade-offs: Size vs speed vs human-readability
   - Decision needed: Week 0

3. **NLP approach for task parsing?**
   - Options: Keyword extraction, spaCy, regex patterns
   - Decision needed: Week 3

### Product Questions

1. **Should Repository Map be opt-in or opt-out?**
   - Opt-in: Safer, but requires user action
   - Opt-out: Better UX, but may surprise users
   - Decision needed: Week 0 (gather community feedback)

2. **What languages to support first?**
   - Priority order: Python → JS/TS → Go → Rust → Java?
   - Decision needed: Week 1

3. **Should interactive mode replace YAML import?**
   - Keep both (flexibility)
   - Deprecate YAML (simplicity)
   - Decision needed: Week 2 (based on user feedback)

---

## 🤝 Community Engagement

### Call for Feedback

**Questions for Users**:
1. Which feature excites you most: Repository Map or Interactive Mode?
2. What languages do you need symbol extraction for?
3. Would you use natural language task import?
4. Any concerns about automatic codebase indexing?

**Channels**:
- GitHub Discussions: https://github.com/nakishiyaman/clauxton/discussions
- Issues for specific feature requests
- Twitter/X for announcements

### Contribution Opportunities

**Good First Issues** (for v0.11.0):
- Add language support (Go, Rust, Java)
- Improve NLP task parsing accuracy
- Create project templates
- Write tutorials

---

## 📋 Pre-Development Checklist

Before starting v0.11.0 development:

- [ ] Gather community feedback on this roadmap (2 weeks)
- [ ] Finalize technical decisions (AST parser, storage format)
- [ ] Create detailed technical design docs
- [ ] Set up performance benchmarking infrastructure
- [ ] Recruit beta testers for early feedback
- [ ] Ensure v0.10.1 is stable (no critical bugs)
- [ ] Allocate development time (13 hours/week for 6 weeks)

---

## 🎉 Expected Impact

### For Individual Developers
- ⏱️ **Time Savings**: 80% reduction in context management time
- 🧠 **Cognitive Load**: Claude Code understands codebase automatically
- 🚀 **Faster Onboarding**: New projects ready in minutes

### For Teams
- 🤝 **Knowledge Sharing**: Codebase structure documented automatically
- 📊 **Visibility**: Better understanding of project state
- ⚡ **Velocity**: Less time on coordination, more on building

### For Claude Code Ecosystem
- 🌟 **Showcase Feature**: Demonstrates AI-powered development
- 📈 **Adoption**: Lowers barrier to entry
- 🔧 **Differentiation**: Unique intelligence layer

---

## 🔗 References

- **Aider Repository Map**: https://aider.chat/docs/repomap.html
- **Devin Codebase Understanding**: https://www.cognition-labs.com/blog
- **tree-sitter**: https://tree-sitter.github.io/tree-sitter/
- **Claude Code MCP Spec**: https://modelcontextprotocol.io/

---

**Document Version**: 1.0
**Last Updated**: 2025-10-23
**Next Review**: After community feedback (2 weeks)
