# Clauxton v0.13.0 (Proactive Intelligence) Implementation Plan

## Overview

**Release Version**: v0.13.0
**Code Name**: Proactive Intelligence
**Release Target**: 2025-12-15 (3 weeks)
**Priority**: 🔥🔥 High (Proactive Features)
**Status**: Planning Phase

**Goal**: Enable real-time monitoring and proactive suggestions to enhance developer productivity through intelligent, context-aware assistance.

**Core Philosophy**:
- **Proactive, Not Intrusive**: Helpful suggestions when needed, silent when not
- **Learning, Not Annoying**: Adapt to user behavior over time
- **Privacy-First**: All monitoring and learning stays 100% local
- **Performance-Conscious**: Background operations must not impact user experience

---

## Strategic Context

### Why Proactive Intelligence Now?

**v0.12.0 Achievement**: Semantic search enables intelligent data retrieval
**v0.13.0 Goal**: Move from reactive (user asks) to proactive (system suggests)

**Key Insight**: Developers often don't know what to ask. Proactive intelligence surfaces relevant information at the right time.

**Example Workflow**:
```
User (in Claude Code):
> *Opens auth.py for editing*

Claude Code (automatically, via Clauxton):
"I noticed you're working on authentication. Here are 3 relevant KB entries and 2 related tasks."

(Clauxton detected file change → analyzed context → called get_recent_changes() → Claude Code provided proactive help)
```

---

## Core Features (4 Categories)

### 1. Real-time File Monitoring 📁

**Purpose**: Watch project files and detect meaningful changes in real-time

**Key Components**:
- File system watcher (watchdog)
- Change event filtering (ignore temp files, node_modules, etc.)
- Pattern detection (new files, modified files, deleted files)
- Incremental embedding updates
- Activity timeline tracking

**MCP Tool**: `watch_project_changes(enabled: bool, config: dict)`

**User Experience**:
```python
# Enable monitoring (via Claude Code MCP)
watch_project_changes(enabled=True, config={
    "ignore_patterns": ["*.pyc", "node_modules/**", ".git/**"],
    "debounce_ms": 500,
    "notify_threshold": 3  # Notify after 3+ file changes
})

# User edits files... (background monitoring)

# Get recent activity
recent = get_recent_changes(minutes=10)
# Returns: {
#   "files_changed": ["auth.py", "models.py"],
#   "patterns_detected": ["authentication_update"],
#   "suggested_kb_entries": [KB-001, KB-042],
#   "related_tasks": [TASK-015]
# }
```

**Technical Design**:
- **Watchdog Integration**: Use `watchdog.observers.Observer`
- **Event Queue**: asyncio.Queue for non-blocking processing
- **Debouncing**: Aggregate rapid changes (e.g., IDE auto-save)
- **Pattern Matching**: Detect file type, directory structure changes
- **Embedding Updates**: Incremental vector store updates (FAISS)

---

### 2. Proactive Suggestions 💡

**Purpose**: Surface relevant KB entries, tasks, and insights at the right moment

**Key Components**:
- Context analysis (active files, recent changes)
- Relevance scoring (semantic + temporal + behavioral)
- Suggestion ranking (importance + confidence)
- Notification system (via MCP → Claude Code)
- Suggestion history tracking

**MCP Tools**:
- `suggest_kb_updates(threshold: float = 0.7)` - Suggest KB updates
- `detect_anomalies()` - Detect unusual patterns
- `get_contextual_suggestions(context: dict)` - Get suggestions for current context

**User Experience**:
```python
# Automatic trigger: User opens file
context = {
    "active_file": "src/api/auth.py",
    "recent_commits": ["Add JWT support"],
    "current_branch": "feature/auth-refactor"
}

suggestions = get_contextual_suggestions(context)
# Returns: {
#   "kb_entries": [
#     {
#       "id": "KB-001",
#       "title": "JWT Authentication Pattern",
#       "relevance_score": 0.92,
#       "reason": "You're working on auth.py, which implements JWT"
#     }
#   ],
#   "tasks": [
#     {
#       "id": "TASK-015",
#       "name": "Update auth documentation",
#       "relevance_score": 0.85,
#       "reason": "Related to files you're currently modifying"
#     }
#   ],
#   "insights": [
#     {
#       "type": "pattern",
#       "message": "You've modified auth.py 5 times this week",
#       "suggestion": "Consider creating a KB entry for auth patterns"
#     }
#   ]
# }
```

**Suggestion Types**:
1. **KB Entry Suggestions**: Relevant knowledge for current work
2. **Task Suggestions**: Related tasks to consider
3. **Pattern Insights**: Detected work patterns
4. **Anomaly Warnings**: Unusual changes (e.g., modifying many files)
5. **Update Reminders**: Outdated KB entries or tasks

**Relevance Scoring Algorithm**:
```python
def calculate_relevance(
    item: Union[KBEntry, Task],
    context: dict,
    user_history: dict
) -> float:
    """
    Calculate relevance score (0.0 - 1.0) based on:
    - Semantic similarity (40%): embedding cosine similarity
    - Temporal proximity (20%): recently created/updated
    - Behavioral fit (20%): user's past interactions
    - Contextual match (20%): file/branch/commit overlap
    """
    semantic_score = cosine_similarity(item.embedding, context.embedding)
    temporal_score = time_decay(item.updated_at, now())
    behavioral_score = user_preference_match(item, user_history)
    contextual_score = file_overlap_ratio(item.files, context.files)

    relevance = (
        0.4 * semantic_score +
        0.2 * temporal_score +
        0.2 * behavioral_score +
        0.2 * contextual_score
    )

    return relevance
```

---

### 3. User Behavior Learning 🧠

**Purpose**: Adapt suggestions based on user interactions over time

**Key Components**:
- Interaction tracking (accepts/rejects suggestions)
- Preference modeling (preferred KB categories, task priorities)
- Personalized ranking (boost items similar to accepted suggestions)
- Confidence calibration (adjust suggestion threshold)
- Privacy-preserving (all data stays local)

**MCP Tools**:
- `record_interaction(item_id: str, action: str)` - Track user interaction
- `get_user_preferences()` - Get learned preferences
- `reset_user_model()` - Reset learning (privacy)

**User Experience**:
```python
# User accepts a suggestion
record_interaction(
    item_id="KB-001",
    action="accepted",  # or "rejected", "dismissed", "viewed"
    context={
        "active_file": "auth.py",
        "time_of_day": "morning",
        "session_duration": 45  # minutes
    }
)

# System learns over time
preferences = get_user_preferences()
# Returns: {
#   "preferred_categories": ["architecture", "pattern"],
#   "active_hours": ["09:00-12:00", "14:00-18:00"],
#   "acceptance_rate": 0.73,
#   "preferred_suggestion_timing": "on_file_open",
#   "confidence_threshold": 0.65  # Lowered from 0.7 due to high acceptance
# }
```

**Learning Model**:
- **Algorithm**: Lightweight online learning (no ML library needed)
- **Storage**: `.clauxton/user_model.yml` (YAML for transparency)
- **Features**: Category frequency, acceptance rate, temporal patterns
- **Update Strategy**: Incremental updates after each interaction
- **Privacy**: Never leaves local machine, user can reset anytime

**Personalization Features**:
1. **Category Boosting**: Prioritize categories user frequently accepts
2. **Timing Optimization**: Suggest at times when user is most receptive
3. **Confidence Adjustment**: Lower threshold if user accepts most suggestions
4. **Noise Reduction**: Suppress suggestion types user always rejects

---

### 4. Enhanced Context Awareness 🎯

**Purpose**: Understand current development context to provide better suggestions

**Key Components**:
- Active file detection (currently open files)
- Git branch analysis (feature branch → relevant tasks)
- Commit history context (recent work patterns)
- Time-based context (morning = planning, afternoon = coding)
- Session tracking (work session duration, breaks)

**MCP Tools**:
- `get_current_context()` - Get full development context
- `analyze_work_session()` - Analyze current work session
- `predict_next_action()` - Predict likely next task

**User Experience**:
```python
# Get comprehensive context
context = get_current_context()
# Returns: {
#   "active_files": ["auth.py", "models.py"],
#   "current_branch": "feature/auth-refactor",
#   "recent_commits": [
#     {"sha": "abc123", "message": "Add JWT support", "time": "10 minutes ago"}
#   ],
#   "work_session": {
#     "duration_minutes": 45,
#     "focus_score": 0.8,  # High focus (few file switches)
#     "last_break": "30 minutes ago"
#   },
#   "time_context": {
#     "time_of_day": "morning",
#     "day_of_week": "Tuesday",
#     "typical_activity": "planning"  # Based on history
#   },
#   "predicted_next_action": {
#     "action": "task_completion",
#     "task_id": "TASK-015",
#     "confidence": 0.82
#   }
# }

# Claude Code uses this to provide contextual help
```

**Context Sources**:
1. **File System**: Active files, recent changes
2. **Git**: Current branch, commits, diffs
3. **Clauxton**: Tasks, KB entries, previous sessions
4. **System**: Time, day of week, work patterns
5. **User Model**: Learned preferences and behaviors

**Contextual Intelligence**:
- **Morning (9-12)**: Suggest planning tasks, KB reviews
- **Afternoon (12-18)**: Suggest coding tasks, implementation
- **Branch Context**: `feature/auth` → auth-related KB entries
- **Focus Detection**: Editing 1 file for 30min → deep work, minimize interruptions
- **Break Detection**: No activity for 15min → good time for suggestions on resume

---

## Implementation Plan (3 Weeks)

### Week 1 (Nov 25 - Dec 1): File Monitoring & Event System

**Goal**: Real-time file watching with intelligent change detection

#### Day 1-2 (Nov 25-26): Watchdog Integration

**Tasks**:
- [ ] Install and configure `watchdog` library
- [ ] Implement `FileMonitor` class with Observer pattern
- [ ] Add ignore patterns (`.git/`, `node_modules/`, `*.pyc`, etc.)
- [ ] Implement debouncing (aggregate changes within 500ms)
- [ ] Test with rapid file changes (IDE auto-save scenarios)

**Deliverables**:
```python
# clauxton/proactive/file_monitor.py
class FileMonitor:
    def __init__(self, project_root: Path, config: MonitorConfig):
        """Initialize file monitor with watchdog."""

    def start(self) -> None:
        """Start monitoring in background thread."""

    def stop(self) -> None:
        """Stop monitoring gracefully."""

    def get_recent_changes(self, minutes: int = 10) -> List[FileChange]:
        """Get changes in last N minutes."""
```

**Tests**: 15+ tests
- Monitor start/stop
- Ignore patterns
- Debouncing logic
- Change event handling
- Thread safety

---

#### Day 3-4 (Nov 27-28): Event Processing & Pattern Detection

**Tasks**:
- [ ] Implement event queue (asyncio.Queue)
- [ ] Pattern detection (file type changes, bulk edits, deletions)
- [ ] Activity timeline storage (`.clauxton/activity.yml`)
- [ ] Change classification (new_file, modified, deleted, renamed)
- [ ] Integration with existing intelligence modules

**Deliverables**:
```python
# clauxton/proactive/event_processor.py
class EventProcessor:
    async def process_event(self, event: FileSystemEvent) -> ProcessedChange:
        """Process file system event into structured change."""

    async def detect_patterns(self, changes: List[ProcessedChange]) -> List[Pattern]:
        """Detect patterns in recent changes."""

    async def update_activity_timeline(self, change: ProcessedChange) -> None:
        """Store change in activity timeline."""
```

**Tests**: 20+ tests
- Event processing
- Pattern detection (bulk edits, refactors, new features)
- Timeline storage
- Classification accuracy
- Async handling

---

#### Day 5 (Nov 29): MCP Tools - Monitoring

**Tasks**:
- [ ] Implement `watch_project_changes()` MCP tool
- [ ] Implement `get_recent_changes()` MCP tool
- [ ] Add configuration management
- [ ] Integration tests with MCP server
- [ ] Documentation

**Deliverables**:
```python
# clauxton/mcp/server.py (additions)
@server.call_tool()
async def watch_project_changes(
    enabled: bool,
    config: Optional[dict] = None
) -> dict:
    """Enable/disable real-time file monitoring."""

@server.call_tool()
async def get_recent_changes(
    minutes: int = 10,
    include_patterns: bool = True
) -> dict:
    """Get recent file changes and detected patterns."""
```

**Tests**: 12+ tests
- Enable/disable monitoring
- Configuration validation
- Recent changes retrieval
- MCP integration

**Deliverable (Week 1)**: File monitoring system operational ✅

---

### Week 2 (Dec 2-8): Proactive Suggestions & Learning

**Goal**: Intelligent suggestion engine with behavioral learning

#### Day 1-2 (Dec 2-3): Suggestion Engine

**Tasks**:
- [ ] Implement relevance scoring algorithm (4 factors)
- [ ] Context analyzer (active files, branch, commits)
- [ ] Suggestion ranker (top-N selection)
- [ ] Suggestion formatter (user-friendly output)
- [ ] Threshold configuration (min confidence to suggest)

**Deliverables**:
```python
# clauxton/proactive/suggestion_engine.py
class SuggestionEngine:
    def calculate_relevance(
        self,
        item: Union[KBEntry, Task],
        context: DevelopmentContext,
        user_model: UserModel
    ) -> float:
        """Calculate multi-factor relevance score."""

    async def get_suggestions(
        self,
        context: DevelopmentContext,
        limit: int = 5
    ) -> SuggestionBundle:
        """Get top-N suggestions for current context."""
```

**Tests**: 25+ tests
- Relevance scoring (each factor)
- Context analysis
- Ranking algorithm
- Edge cases (no context, empty KB)
- Performance (<100ms for 1000 items)

---

#### Day 3-4 (Dec 4-5): User Behavior Learning

**Tasks**:
- [ ] Implement interaction tracking
- [ ] User model storage (`.clauxton/user_model.yml`)
- [ ] Preference learning (category frequency, acceptance rate)
- [ ] Personalized ranking (boost accepted categories)
- [ ] Confidence calibration (adjust threshold)

**Deliverables**:
```python
# clauxton/proactive/user_model.py
class UserModel:
    def record_interaction(
        self,
        item_id: str,
        action: InteractionType,
        context: dict
    ) -> None:
        """Record user interaction with suggestion."""

    def get_category_preferences(self) -> Dict[str, float]:
        """Get learned category preferences (0.0-1.0)."""

    def adjust_confidence_threshold(self) -> float:
        """Dynamically adjust suggestion threshold."""

    def personalize_ranking(
        self,
        items: List[ScoredItem]
    ) -> List[ScoredItem]:
        """Re-rank items based on user preferences."""
```

**Tests**: 20+ tests
- Interaction tracking
- Preference learning
- Threshold adjustment
- Personalized ranking
- Model persistence

---

#### Day 5 (Dec 6): MCP Tools - Suggestions & Learning

**Tasks**:
- [ ] Implement `get_contextual_suggestions()` MCP tool
- [ ] Implement `suggest_kb_updates()` MCP tool
- [ ] Implement `detect_anomalies()` MCP tool
- [ ] Implement `record_interaction()` MCP tool
- [ ] Integration tests

**Deliverables**:
```python
# clauxton/mcp/server.py (additions)
@server.call_tool()
async def get_contextual_suggestions(
    context: dict,
    limit: int = 5
) -> dict:
    """Get proactive suggestions for current context."""

@server.call_tool()
async def suggest_kb_updates(
    threshold: float = 0.7
) -> dict:
    """Suggest KB entries that should be updated."""

@server.call_tool()
async def detect_anomalies() -> dict:
    """Detect unusual patterns in recent changes."""

@server.call_tool()
async def record_interaction(
    item_id: str,
    action: str,
    context: Optional[dict] = None
) -> dict:
    """Record user interaction for learning."""
```

**Tests**: 18+ tests

**Deliverable (Week 2)**: Suggestion engine with learning ✅

---

### Week 3 (Dec 9-15): Context Intelligence & Polish

**Goal**: Enhanced context awareness and production-ready release

#### Day 1-2 (Dec 9-10): Context Intelligence

**Tasks**:
- [ ] Git context analyzer (branch, commits, diff stats)
- [ ] Work session tracker (duration, focus score, breaks)
- [ ] Time-based context (morning/afternoon behavior)
- [ ] Next action predictor (based on patterns)
- [ ] Context caching (avoid redundant analysis)

**Deliverables**:
```python
# clauxton/proactive/context_analyzer.py
class ContextAnalyzer:
    async def get_current_context(self) -> DevelopmentContext:
        """Get comprehensive development context."""

    async def analyze_work_session(self) -> SessionAnalysis:
        """Analyze current work session."""

    async def predict_next_action(self) -> PredictedAction:
        """Predict likely next task/action."""

    def get_time_context(self) -> TimeContext:
        """Get time-based context (morning/afternoon/etc)."""
```

**Tests**: 22+ tests
- Git context extraction
- Session tracking
- Focus score calculation
- Action prediction
- Time-based logic

---

#### Day 3 (Dec 11): MCP Tools - Context

**Tasks**:
- [ ] Implement `get_current_context()` MCP tool
- [ ] Implement `analyze_work_session()` MCP tool
- [ ] Implement `predict_next_action()` MCP tool
- [ ] Integration tests
- [ ] Performance optimization

**Deliverables**:
```python
@server.call_tool()
async def get_current_context() -> dict:
    """Get full development context."""

@server.call_tool()
async def analyze_work_session() -> dict:
    """Analyze current work session."""

@server.call_tool()
async def predict_next_action() -> dict:
    """Predict likely next task/action."""
```

**Tests**: 15+ tests

---

#### Day 4-5 (Dec 12-13): Integration, Testing & Documentation

**Tasks**:
- [ ] End-to-end integration tests (20+ tests)
- [ ] Performance testing (latency, memory, CPU)
- [ ] Background service stability tests
- [ ] Documentation:
  - [ ] User guide (`docs/proactive-intelligence.md`)
  - [ ] MCP tool reference (update `docs/mcp-integration.md`)
  - [ ] Configuration guide
  - [ ] FAQ and troubleshooting
- [ ] Update README.md with v0.13.0 features
- [ ] CHANGELOG.md update

**Tests**: 20+ integration tests

---

#### Day 6-7 (Dec 14-15): Release Preparation

**Tasks**:
- [ ] Final quality checks:
  - [ ] All tests passing (target: 1,750+ tests)
  - [ ] Coverage >85%
  - [ ] mypy strict mode passing
  - [ ] ruff linting passing
- [ ] Performance validation:
  - [ ] File monitoring: <10ms latency
  - [ ] Suggestion generation: <100ms
  - [ ] Background CPU: <5%
  - [ ] Memory overhead: <50MB
- [ ] Version bump (v0.12.0 → v0.13.0)
- [ ] Build and test package
- [ ] Create git tag
- [ ] PyPI release
- [ ] GitHub release with notes
- [ ] Announcement (README, Twitter, etc.)

**Deliverable (Week 3)**: v0.13.0 Released 🚀

---

## Technical Architecture

### File Structure

```
clauxton/
├── proactive/                       # NEW: Proactive intelligence
│   ├── __init__.py
│   ├── file_monitor.py             # Watchdog-based file monitoring
│   ├── event_processor.py          # Event queue and pattern detection
│   ├── suggestion_engine.py        # Relevance scoring and ranking
│   ├── user_model.py               # Behavioral learning
│   ├── context_analyzer.py         # Development context analysis
│   └── config.py                   # Configuration models
├── mcp/
│   └── server.py                   # Enhanced: 32 → 42 MCP tools
├── semantic/                        # Existing (v0.12.0)
│   └── search_engine.py            # Used for similarity scoring
└── analysis/                        # Existing (v0.12.0)
    └── git_analyzer.py             # Used for commit context

Storage: .clauxton/
├── activity.yml                    # NEW: Activity timeline
├── user_model.yml                  # NEW: Learned user preferences
├── monitoring_config.yml           # NEW: Monitoring configuration
└── suggestions_history.yml         # NEW: Suggestion history
```

### Dependencies

```toml
[project.dependencies]
# Existing dependencies...
watchdog = ">=3.0.0"                # File system monitoring

[project.optional-dependencies]
# Semantic features (already required for v0.12.0)
semantic = [
    "sentence-transformers>=2.3.0",
    "faiss-cpu>=1.7.4",
    "torch>=2.1.0",
]
```

**Note**: No new external dependencies besides `watchdog`!

---

### New MCP Tools (10 tools: 32 → 42)

#### File Monitoring (2 tools)
1. **`watch_project_changes(enabled: bool, config: dict)`**
   - Enable/disable file monitoring
   - Configure ignore patterns, debounce interval

2. **`get_recent_changes(minutes: int, include_patterns: bool)`**
   - Get changes in last N minutes
   - Optionally include detected patterns

#### Proactive Suggestions (3 tools)
3. **`get_contextual_suggestions(context: dict, limit: int)`**
   - Get suggestions for current context
   - Returns KB entries, tasks, insights

4. **`suggest_kb_updates(threshold: float)`**
   - Suggest KB entries needing updates
   - Based on recent changes and staleness

5. **`detect_anomalies()`**
   - Detect unusual patterns
   - E.g., too many files changed, unusual hours

#### User Learning (2 tools)
6. **`record_interaction(item_id: str, action: str, context: dict)`**
   - Track user interactions
   - Actions: accepted, rejected, dismissed, viewed

7. **`get_user_preferences()`**
   - Get learned preferences
   - Returns category preferences, timing, confidence

#### Context Intelligence (3 tools)
8. **`get_current_context()`**
   - Get full development context
   - Active files, branch, commits, session, time

9. **`analyze_work_session()`**
   - Analyze current work session
   - Duration, focus score, breaks, patterns

10. **`predict_next_action()`**
    - Predict likely next task
    - Based on context + user patterns

**Total MCP Tools**:
- v0.12.0: 32 tools
- v0.13.0: 42 tools (+10)

---

## Testing Strategy

### Unit Tests (110+ new tests)

**File Monitoring** (35 tests):
- Watchdog integration (start, stop, restart)
- Ignore patterns (`.git/`, `node_modules/`, temp files)
- Debouncing (aggregate rapid changes)
- Event classification (new, modified, deleted, renamed)
- Thread safety (concurrent access)
- Error handling (permission errors, missing directories)

**Suggestion Engine** (40 tests):
- Relevance scoring (each of 4 factors)
- Context analysis (active files, branch, commits)
- Ranking algorithm (top-N selection)
- Edge cases (empty KB, no context, tie scores)
- Performance (1000 items in <100ms)
- Threshold behavior (min confidence)

**User Learning** (20 tests):
- Interaction recording (accept, reject, dismiss)
- Preference learning (category frequency)
- Confidence adjustment (acceptance rate impact)
- Personalized ranking (boost preferred categories)
- Model persistence (save/load)
- Privacy (reset model)

**Context Intelligence** (15 tests):
- Git context extraction (branch, commits, diffs)
- Session tracking (duration, focus)
- Time-based logic (morning vs afternoon)
- Action prediction (next task)
- Context caching (avoid redundant work)

---

### Integration Tests (20+ tests)

**End-to-End Workflows**:
1. File edit → monitoring → pattern detection → suggestions
2. User accepts suggestion → learning → personalized ranking
3. Branch switch → context update → contextual suggestions
4. Long session → focus detection → minimize interruptions
5. Break detected → resume → proactive summary

**MCP Tool Integration**:
- All 10 new tools callable via MCP
- Error handling and validation
- Concurrent tool calls
- State consistency

**Performance Tests**:
- File monitoring latency (<10ms)
- Suggestion generation (<100ms)
- Background CPU usage (<5%)
- Memory overhead (<50MB)
- Concurrent file changes (100+ files)

---

### Test Coverage Target

**Overall Target**: >85% (maintain current 86%)

**Module-Specific Targets**:
- `proactive/file_monitor.py`: 90%+ (critical path)
- `proactive/suggestion_engine.py`: 95%+ (core algorithm)
- `proactive/user_model.py`: 90%+ (learning logic)
- `proactive/context_analyzer.py`: 85%+ (context extraction)
- MCP tools: 92%+ (maintain current)

**Total Test Count**:
- Current (v0.12.0): 1,637 tests
- New (v0.13.0): ~130 tests
- **Target**: 1,750+ tests

---

## Configuration

### Monitoring Configuration

```yaml
# .clauxton/monitoring_config.yml
monitoring:
  enabled: true

  # File watching
  watch:
    ignore_patterns:
      - "*.pyc"
      - "*.pyo"
      - "__pycache__/**"
      - ".git/**"
      - "node_modules/**"
      - ".venv/**"
      - "venv/**"
      - "*.egg-info/**"
      - ".mypy_cache/**"
      - ".pytest_cache/**"
      - ".coverage"
      - "*.log"
      - "*.tmp"

    debounce_ms: 500  # Aggregate changes within 500ms

  # Suggestions
  suggestions:
    enabled: true
    min_confidence: 0.65  # Adjusts based on user acceptance rate
    max_per_context: 5
    notify_threshold: 3  # Notify after N file changes

  # Learning
  learning:
    enabled: true
    update_frequency: "immediate"  # or "hourly", "daily"
    min_interactions: 10  # Min interactions before personalizing

  # Context
  context:
    track_sessions: true
    track_time_patterns: true
    session_timeout_minutes: 15  # No activity = session end
```

### User Preferences (Learned)

```yaml
# .clauxton/user_model.yml (generated automatically)
preferences:
  # Category preferences (0.0 - 1.0)
  categories:
    architecture: 0.85
    pattern: 0.72
    decision: 0.68
    constraint: 0.45
    convention: 0.39

  # Temporal patterns
  time_patterns:
    preferred_hours:
      - "09:00-12:00"  # Morning planning
      - "14:00-18:00"  # Afternoon coding
    most_active_days:
      - "Tuesday"
      - "Wednesday"
      - "Thursday"

  # Interaction stats
  stats:
    total_suggestions: 127
    accepted: 93
    rejected: 18
    dismissed: 16
    acceptance_rate: 0.732

  # Adjusted confidence
  confidence_threshold: 0.63  # Lowered from 0.65 due to high acceptance

  # Suggestion timing preference
  timing:
    on_file_open: true
    on_session_start: true
    on_break_return: true
    during_deep_work: false  # Don't interrupt focused work
```

---

## Success Metrics

### Quantitative Metrics

**Adoption**:
- 🎯 Monitoring enabled: >80% of users
- 🎯 Daily active usage: >60% of users
- 🎯 Suggestions accepted: >70% acceptance rate

**Performance**:
- ⚡ File change latency: <10ms (p95)
- ⚡ Suggestion generation: <100ms (p95)
- ⚡ Background CPU: <5% average
- ⚡ Memory overhead: <50MB

**Quality**:
- 🎯 Suggestion relevance: >75% user-rated useful
- 🎯 KB coverage: +40% (more entries created)
- 🎯 Task completion: +30% velocity
- 🎯 False positives: <15% (low noise)

**Testing**:
- ✅ Test count: 1,750+ total (+113 new)
- ✅ Coverage: >85%
- ✅ All quality checks passing

---

### Qualitative Metrics

**User Experience**:
- ❤️ Users feel Clauxton "understands" their work
- ❤️ Suggestions feel timely and relevant
- ❤️ Not intrusive or annoying
- ❤️ Privacy-respecting (all local)

**Developer Productivity**:
- 🚀 Faster KB entry discovery
- 🚀 Better task prioritization
- 🚀 Reduced context switching
- 🚀 More time in flow state

**Feedback Themes** (target):
- "Clauxton knows what I need before I ask"
- "Suggestions are surprisingly relevant"
- "Helps me stay focused on the right work"
- "Love that all learning stays local"

---

## Risks & Mitigations

### Risk 1: Performance Impact
**Risk**: Background monitoring could slow down system
**Likelihood**: Medium
**Impact**: High (unusable if slow)

**Mitigation**:
- Aggressive debouncing (500ms)
- Ignore patterns for large directories
- Async event processing (non-blocking)
- Background thread with low priority
- Performance tests (CPU, memory, latency)
- Kill switch (disable monitoring if issues)

---

### Risk 2: Annoying Suggestions
**Risk**: Too many or irrelevant suggestions frustrate users
**Likelihood**: Medium
**Impact**: Medium (user disables feature)

**Mitigation**:
- High confidence threshold (0.65 default)
- Learning system to reduce noise over time
- User can adjust threshold or disable
- Limit suggestions (max 5 per context)
- Track rejection rate → auto-adjust
- Clear "dismiss forever" option

---

### Risk 3: Privacy Concerns
**Risk**: Users worried about data collection
**Likelihood**: Low
**Impact**: High (trust issue)

**Mitigation**:
- 100% local processing (no external calls)
- Transparent storage (`.clauxton/user_model.yml`)
- Easy reset (`get_user_preferences()` shows data)
- Documentation emphasizing privacy
- No telemetry, no analytics, no cloud

---

### Risk 4: Complex User Behavior
**Risk**: Simple learning model can't capture complex preferences
**Likelihood**: Medium
**Impact**: Medium (poor suggestions)

**Mitigation**:
- Start with simple model (category frequency)
- Iterate based on user feedback
- Multiple factors (semantic, temporal, behavioral)
- Allow manual configuration override
- Phase 4 can add ML model if needed

---

### Risk 5: File System Permissions
**Risk**: watchdog fails on some systems (permissions, filesystem types)
**Likelihood**: Low
**Impact**: Medium (monitoring doesn't work)

**Mitigation**:
- Graceful degradation (polling fallback)
- Clear error messages
- Test on Linux, macOS, Windows
- Documentation for troubleshooting
- Manual suggestion trigger available

---

## Future Enhancements (Post v0.13.0)

**v0.14.0 (Interactive TUI)**:
- Proactive suggestions panel in TUI
- Real-time activity feed
- One-key accept/reject suggestions

**v0.15.0 (Web Dashboard)**:
- Suggestion analytics dashboard
- Acceptance rate over time
- Most useful suggestions

**v0.16.0 (Advanced AI)**:
- ML-based learning (beyond simple frequency)
- Multi-project learning
- Team-wide suggestion sharing

---

## Documentation Plan

### User Guides (4 new docs)

1. **`docs/proactive-intelligence.md`** (Comprehensive Guide)
   - Overview and benefits
   - Getting started (enable monitoring)
   - Understanding suggestions
   - Customizing behavior
   - Privacy and data storage
   - Troubleshooting

2. **`docs/configuration.md`** (Configuration Reference)
   - Monitoring config options
   - Suggestion threshold tuning
   - Ignore patterns
   - Learning settings
   - Example configurations

3. **`docs/user-model.md`** (Learning & Privacy)
   - How learning works
   - What data is collected (locally)
   - How to view preferences
   - How to reset model
   - Privacy guarantees

4. **Update `docs/mcp-integration.md`**
   - Add 10 new MCP tools
   - Usage examples with Claude Code
   - Best practices for proactive features

### README Updates

- Add v0.13.0 to feature list
- Update MCP tool count (32 → 42)
- Add "Proactive Intelligence" section
- Update installation instructions
- Update success metrics

### CHANGELOG

- v0.13.0 release notes
- Breaking changes (none expected)
- New features (10 MCP tools)
- Performance improvements
- Bug fixes

---

## Release Checklist

### Pre-Release (Dec 13-14)

- [ ] All tests passing (1,750+ tests)
- [ ] Coverage >85%
- [ ] mypy strict mode: no errors
- [ ] ruff linting: no errors
- [ ] Performance validation:
  - [ ] File monitoring: <10ms latency
  - [ ] Suggestions: <100ms generation
  - [ ] Background CPU: <5%
  - [ ] Memory: <50MB overhead
- [ ] Documentation complete:
  - [ ] `docs/proactive-intelligence.md`
  - [ ] `docs/configuration.md`
  - [ ] `docs/user-model.md`
  - [ ] `docs/mcp-integration.md` updated
  - [ ] README.md updated
  - [ ] CHANGELOG.md updated
- [ ] Manual testing:
  - [ ] Enable/disable monitoring
  - [ ] File changes → suggestions
  - [ ] Accept/reject → learning
  - [ ] Context switches
  - [ ] Long sessions

### Release (Dec 15)

- [ ] Version bump:
  - [ ] `clauxton/__version__.py`
  - [ ] `pyproject.toml`
- [ ] Build package: `python -m build`
- [ ] Validate: `twine check dist/*`
- [ ] Git tag: `git tag -a v0.13.0 -m "Release v0.13.0"`
- [ ] Push tag: `git push origin v0.13.0`
- [ ] PyPI upload: `twine upload dist/*`
- [ ] GitHub release:
  - [ ] Release notes from CHANGELOG
  - [ ] Highlight key features
  - [ ] Installation instructions
- [ ] Announcement:
  - [ ] Update README badges
  - [ ] Social media (if applicable)
  - [ ] Community notification

---

## Key Takeaways

**v0.13.0 Goals**:
1. ✅ Real-time file monitoring (watchdog)
2. ✅ Proactive, contextual suggestions
3. ✅ User behavior learning (100% local)
4. ✅ Enhanced context awareness
5. ✅ 10 new MCP tools (32 → 42)
6. ✅ Privacy-first, performance-conscious

**Value Proposition**:
> "Clauxton now anticipates your needs. It watches your work, learns your preferences, and proactively surfaces the right information at the right time—all while keeping your data 100% local."

**Next Phase**: v0.14.0 (Interactive TUI) - Visual interface for proactive features

---

**Status**: Ready to implement 🚀

**Questions or Concerns**: Review this plan and provide feedback before implementation begins.
