HOOKS_IMPLEMENTATION_PLAN.md•29.5 kB
# Hooks Implementation Plan
**Version**: 2.0.0
**Status**: Design Phase
**Last Updated**: 2025-10-20
---
## Executive Summary
A comprehensive hooks system leveraging Claude Code's native PostToolUse/PreToolUse hooks to enable automatic memory management, pattern learning, and intelligent configuration updates. Designed for zero-impact performance (<5ms overhead) with intelligent significance filtering.
### Key Capabilities
- **5-Layer Pattern Detection**: Explicit, Implicit, Violations, Behavioral, Semantic
- **Automatic Learning**: Confidence-based preference promotion (3+ occurrences)
- **Skills Auto-Generation**: Dynamic skills file creation from learned patterns
- **CLAUDE.md Management**: Intelligent config updates with smart merging
- **Performance Optimized**: Async processing, significance filtering, <5ms impact
- **Error Resilient**: Always exit 0, fallback mode, automatic disable on failures
---
## Architecture Overview
```
┌──────────────────────────────────────────────────────────────┐
│ Claude Code/Desktop │
│ (Execution Layer) │
└─────────────────────┬────────────────────────────────────────┘
│
│ Native Hook Events
│
┌─────────────┼─────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│PreToolUse│ │PostToolUse│ │SessionEnd│
│ Hook │ │ Hook │ │ Hook │
└────┬─────┘ └────┬──────┘ └────┬─────┘
│ │ │
│ │ │
└─────────────┼──────────────┘
│
▼
┌───────────────────────┐
│ Hook Manager │
│ capture_hook.py │
│ • Event routing │
│ • Error handling │
│ • Timeout (5s) │
└──────────┬────────────┘
│
┌───────────┼───────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│Significance │ │Pattern │ │Context │
│Scorer │ │Extractor │ │Builder │
│ │ │ │ │ │
│Multi-factor │ │5-layer │ │Session │
│scoring │ │detection │ │tracking │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
│ │ │
│ │ │
└────────────────┼────────────────┘
│
▼
┌─────────────────┐
│ Intelligence │
│ Layer │
│ │
│ • Learning │
│ • Validation │
│ • Agent Track │
│ • Temporal │
└────────┬────────┘
│
▼
┌─────────────────────────┐
│ Storage Layer │
│ • SQLite (structured) │
│ • AgentDB (vectors) │
└─────────────────────────┘
│
▼
┌─────────────────────────┐
│ Auto-Update Layer │
│ • Skills files │
│ • CLAUDE.md │
│ • Config sync │
└─────────────────────────┘
```
---
## Hook Event Specifications
### 1. PreToolUse Hook
**Trigger**: Before tool execution
**Purpose**: Preparation, validation, resource allocation
```json
{
"event": "PreToolUse",
"timestamp": 1729485234567,
"tool": {
"name": "Read",
"arguments": {
"file_path": "/path/to/file.py"
}
},
"context": {
"session_id": "sess-1729485234567",
"project_path": "/Users/user/project",
"user_message": "Review this file for bugs",
"conversation_history": []
}
}
```
**Hook Actions**:
```python
async def pre_tool_use_hook(event: dict) -> dict:
"""
PreToolUse hook implementation
Returns:
dict: Modifications or metadata to inject
"""
# 1. Validate tool arguments
if not validate_tool_args(event['tool']):
return {'error': 'Invalid arguments', 'skip': True}
# 2. Check if learned preferences apply
preferences = await load_preferences(event['tool']['name'])
if preferences:
event['tool']['arguments'] = apply_preferences(
event['tool']['arguments'],
preferences
)
# 3. Prepare context
context = await build_context(event)
# 4. Log preparation
await log_event('pre_tool_use', event, context)
return {'modified_args': event['tool']['arguments'], 'context': context}
```
**Use Cases**:
- Auto-apply learned preferences (e.g., "use uv not pip")
- Validate command safety
- Prepare resources (e.g., create temp directories)
- Load relevant context from memory
---
### 2. PostToolUse Hook
**Trigger**: After tool execution
**Purpose**: Capture, learn, update
```json
{
"event": "PostToolUse",
"timestamp": 1729485235789,
"tool": {
"name": "Bash",
"arguments": {
"command": "uv pip install requests",
"description": "Install Python package"
}
},
"result": {
"success": true,
"output": "Successfully installed requests-2.31.0",
"execution_time_ms": 1234
},
"context": {
"session_id": "sess-1729485234567",
"project_path": "/Users/user/project",
"user_feedback": null,
"correction_detected": false
}
}
```
**Hook Actions**:
```python
async def post_tool_use_hook(event: dict) -> None:
"""
PostToolUse hook implementation
Main entry point for automatic learning
"""
try:
# 1. Significance scoring (0.0-1.0)
significance = await score_significance(event)
if significance < 0.3:
return # Skip insignificant events
# 2. Extract patterns (5-layer detection)
patterns = await extract_patterns(event, significance)
# 3. Store in database
await store_execution(event, significance, patterns)
# 4. Update learning models
if significance >= 0.6:
await update_learning(patterns)
# 5. Auto-update configs if high confidence
if patterns and patterns.confidence >= 0.9:
await auto_update_configs(patterns)
# 6. Notify MCP server (optional)
if significance >= 0.7:
await notify_mcp_server(event, patterns)
except Exception as e:
# Never fail - always exit 0
log_error(f"Hook error: {e}")
return
```
**Use Cases**:
- Capture significant tool executions
- Detect user corrections
- Learn workflow patterns
- Update CLAUDE.md automatically
- Track agent performance
---
### 3. SessionEnd Hook
**Trigger**: End of session
**Purpose**: Consolidation, summary, cleanup
```json
{
"event": "SessionEnd",
"timestamp": 1729489999999,
"session": {
"session_id": "sess-1729485234567",
"duration_ms": 4765432,
"tools_executed": 127,
"corrections_detected": 3,
"patterns_learned": 1,
"project_path": "/Users/user/project"
}
}
```
**Hook Actions**:
```python
async def session_end_hook(event: dict) -> None:
"""
SessionEnd hook implementation
Consolidate learning and update configs
"""
# 1. Generate session summary
summary = await generate_session_summary(event['session'])
# 2. Consolidate learned patterns
patterns = await consolidate_patterns(event['session']['session_id'])
# 3. Promote high-confidence patterns
for pattern in patterns:
if pattern.confidence >= 0.9 and pattern.occurrence_count >= 3:
await promote_to_preference(pattern)
await update_claudemd(pattern)
# 4. Generate skills files if new patterns
if len(patterns) > 0:
await generate_skills_files(patterns)
# 5. Cleanup temporary data
await cleanup_session(event['session']['session_id'])
# 6. Export metrics
await export_session_metrics(event['session'])
```
---
## Pattern Detection Layers
### Layer 1: Explicit Corrections (Highest Confidence)
**Detection**: User explicitly corrects Claude's output
```python
def detect_explicit_correction(event: dict) -> Optional[Pattern]:
"""
Detect explicit user corrections
Examples:
- "Use • bullets not -"
- "Use uv instead of pip"
- "Always run tests after code changes"
"""
user_message = event['context'].get('user_message', '')
# Patterns indicating corrections
correction_indicators = [
r'use\s+(\w+)\s+(?:not|instead of)\s+(\w+)',
r'(?:always|never)\s+(.+)',
r'prefer\s+(\w+)\s+over\s+(\w+)',
r'don\'?t\s+(.+)',
r'(?:should|must|need to)\s+(.+)',
]
for pattern in correction_indicators:
match = re.search(pattern, user_message, re.IGNORECASE)
if match:
return Pattern(
type='explicit_correction',
confidence=0.95,
content=user_message,
extracted_rule=match.group(0),
timestamp=event['timestamp']
)
return None
```
**Confidence**: 0.9-1.0
---
### Layer 2: Implicit Rejections (High Confidence)
**Detection**: User edits Claude's output within 2 minutes
```python
def detect_implicit_rejection(event: dict, history: list) -> Optional[Pattern]:
"""
Detect implicit rejections via user edits
Example:
- Claude writes code with "pip install"
- User edits to "uv pip install" within 2 minutes
"""
if event['tool']['name'] != 'Edit':
return None
# Find recent tool execution on same file
recent = find_recent_execution(
file_path=event['tool']['arguments']['file_path'],
window_ms=120000, # 2 minutes
history=history
)
if not recent:
return None
# Compare original vs edited
original = recent['result']['content']
edited = event['tool']['arguments']['new_string']
# Extract differences
diff = compute_diff(original, edited)
if len(diff) > 0:
return Pattern(
type='implicit_rejection',
confidence=0.85,
content=f"Changed '{diff.old}' to '{diff.new}'",
extracted_rule=f"Prefer '{diff.new}' over '{diff.old}'",
timestamp=event['timestamp']
)
return None
```
**Confidence**: 0.8-0.9
---
### Layer 3: Rule Violations (Medium Confidence)
**Detection**: Compare execution vs existing CLAUDE.md rules
```python
async def detect_rule_violation(event: dict) -> Optional[Pattern]:
"""
Detect violations of existing rules
Example:
- CLAUDE.md says "use uv for Python packages"
- Claude executes "pip install requests"
"""
# Load CLAUDE.md rules
rules = await load_claudemd_rules(event['context']['project_path'])
# Check if execution violates any rule
for rule in rules:
if rule.applies_to(event['tool']):
if not rule.satisfied_by(event['tool']['arguments']):
return Pattern(
type='rule_violation',
confidence=0.7,
content=f"Violated rule: {rule.description}",
extracted_rule=rule.description,
timestamp=event['timestamp'],
metadata={'rule_id': rule.id}
)
return None
```
**Confidence**: 0.6-0.8
---
### Layer 4: Behavioral Patterns (Medium Confidence)
**Detection**: Repeated sequences across sessions
```python
async def detect_behavioral_pattern(event: dict, history: list) -> Optional[Pattern]:
"""
Detect behavioral patterns via repetition
Example:
- User always runs "npm run test" after editing code
- Detected after 5+ occurrences
"""
# Build sequence fingerprint
sequence = build_sequence_fingerprint(event, history, window=5)
# Check pattern frequency
frequency = await query_pattern_frequency(sequence)
if frequency.occurrence_count >= 5:
return Pattern(
type='behavioral_pattern',
confidence=min(0.5 + (frequency.occurrence_count * 0.05), 0.8),
content=f"Repeated sequence: {sequence.description}",
extracted_rule=f"Auto-suggest: {sequence.next_action}",
timestamp=event['timestamp'],
metadata={'frequency': frequency.occurrence_count}
)
return None
```
**Confidence**: 0.5-0.8 (increases with repetition)
---
### Layer 5: Semantic Clustering (Lower Confidence)
**Detection**: Group semantically similar corrections via embeddings
```python
async def detect_semantic_cluster(event: dict) -> Optional[Pattern]:
"""
Detect semantic clusters via vector similarity
Example:
- Multiple corrections about bullet formatting
- Cluster: "formatting preferences for resumes"
"""
# Compute embedding for event
embedding = await compute_embedding(event['tool']['arguments'])
# Search for similar patterns
similar = await agentDB.retrieveWithReasoning(embedding, {
domain: 'corrections',
k: 10,
minSimilarity: 0.85,
})
if len(similar.memories) >= 3:
# Cluster detected
cluster = await synthesize_cluster(similar.memories)
return Pattern(
type='semantic_cluster',
confidence=0.6,
content=f"Cluster: {cluster.theme}",
extracted_rule=cluster.generalized_rule,
timestamp=event['timestamp'],
metadata={'cluster_size': len(similar.memories)}
)
return None
```
**Confidence**: 0.5-0.7
---
## Significance Scoring System
### Multi-Factor Scoring
```python
async def score_significance(event: dict) -> float:
"""
Calculate significance score (0.0-1.0)
Factors:
1. Tool type (Write/Edit > Read)
2. File importance (src/ > tests/ > docs/)
3. Execution result (error > success)
4. User feedback presence
5. Correction indicators
6. Project context
"""
score = 0.0
# 1. Tool type (0.0-0.3)
tool_scores = {
'Write': 0.3, 'Edit': 0.25, 'Bash': 0.2,
'Read': 0.1, 'Grep': 0.05, 'Glob': 0.05
}
score += tool_scores.get(event['tool']['name'], 0.1)
# 2. File importance (0.0-0.2)
if 'file_path' in event['tool']['arguments']:
path = event['tool']['arguments']['file_path']
if '/src/' in path or '/lib/' in path:
score += 0.2
elif '/tests/' in path:
score += 0.15
elif '/docs/' in path:
score += 0.1
# 3. Execution result (0.0-0.2)
if not event['result'].get('success', True):
score += 0.2
# 4. User feedback (0.0-0.2)
if event['context'].get('user_feedback'):
score += 0.2
# 5. Correction indicators (0.0-0.3)
if event['context'].get('correction_detected'):
score += 0.3
# 6. Project context (0.0-0.1)
if await is_active_project(event['context']['project_path']):
score += 0.1
return min(score, 1.0)
```
### Thresholds
- `score < 0.3`: Skip (insignificant)
- `0.3 ≤ score < 0.6`: Store only
- `0.6 ≤ score < 0.8`: Store + Extract patterns
- `score ≥ 0.8`: Store + Extract + Suggest updates
---
## Skills Auto-Generation
### Skills File Structure
```markdown
---
name: "Learned Preference: Use UV Package Manager"
description: "Automatically learned from 3+ corrections. User prefers 'uv' over 'pip' for Python package management."
confidence: 0.95
learned_from: [episode-123, episode-456, episode-789]
category: "python-tooling"
---
# Use UV Package Manager
## What This Skill Does
Ensures Python package operations use `uv` instead of `pip` based on learned user preferences.
## When to Apply
- Installing Python packages
- Managing virtual environments
- Updating dependencies
## Correct Usage
```bash
# ✅ Preferred
uv pip install requests
uv pip install -r requirements.txt
uv venv .venv
# ❌ Avoid
pip install requests
pip install -r requirements.txt
python -m venv .venv
```
## Context
Learned from user corrections:
1. 2025-10-15: "Use uv instead of pip" (Episode #123)
2. 2025-10-17: "Always use uv for package management" (Episode #456)
3. 2025-10-18: Changed `pip install` to `uv pip install` (Episode #789)
## Confidence: 95%
Based on 3 explicit corrections with consistent pattern.
---
**Auto-generated by**: MCP Standards Learning System
**Last updated**: 2025-10-20
```
### Auto-Generation Logic
```python
async def generate_skills_file(pattern: Pattern) -> None:
"""
Auto-generate skills file from learned pattern
Triggers:
- Confidence >= 0.9
- Occurrence count >= 3
- Pattern type: explicit_correction or implicit_rejection
"""
if pattern.confidence < 0.9 or pattern.occurrence_count < 3:
return
# Load template
template = load_template('skill_template.md')
# Fill in details
content = template.render(
name=pattern.generate_skill_name(),
description=pattern.description,
confidence=pattern.confidence,
learned_from=pattern.episode_ids,
category=pattern.category,
correct_usage=pattern.extract_correct_usage(),
incorrect_usage=pattern.extract_incorrect_usage(),
context=pattern.get_context_summary(),
last_updated=datetime.now().isoformat()
)
# Write to .claude/skills/
skill_path = f".claude/skills/learned-{pattern.id}/SKILL.md"
await write_file(skill_path, content)
# Notify user
await notify(f"✨ Generated new skill: {pattern.generate_skill_name()}")
```
---
## CLAUDE.md Auto-Update
### Smart Merge Strategy
```python
async def update_claudemd(pattern: Pattern) -> None:
"""
Intelligently update CLAUDE.md without conflicts
Strategy:
1. Detect CLAUDE.md hierarchy (global → parent → project)
2. Choose appropriate file (project-specific vs global)
3. Parse existing content (preserve structure)
4. Insert new rule in correct section
5. Create backup before writing
6. Validate after update
"""
# 1. Detect hierarchy
claudemd_files = await find_claudemd_hierarchy(pattern.project_path)
# Returns: [global, parent, project, nested]
# 2. Choose file
target_file = choose_claudemd_file(pattern, claudemd_files)
# 3. Parse existing
existing = await parse_claudemd(target_file)
# 4. Determine section
section = determine_section(pattern)
# e.g., "Tool Preferences", "Workflow Rules", "Code Style"
# 5. Insert rule
new_rule = format_rule(pattern)
updated = existing.insert_rule(section, new_rule)
# 6. Backup
await create_backup(target_file)
# 7. Write
await write_file(target_file, updated.to_markdown())
# 8. Validate
if not await validate_claudemd(target_file):
await rollback_from_backup(target_file)
raise ValidationError("CLAUDE.md validation failed")
# 9. Notify
await notify(f"✅ Updated {target_file} with new rule: {pattern.description}")
```
### Rule Formatting
```python
def format_rule(pattern: Pattern) -> str:
"""
Format pattern as CLAUDE.md rule
Example output:
### Python Package Management
**Preference**: Use `uv` instead of `pip`
**Reason**: User preference learned from 3 corrections
**Examples**:
```bash
# ✅ Correct
uv pip install requests
# ❌ Incorrect
pip install requests
```
**Confidence**: 95% (learned 2025-10-20)
---
"""
return f"""
### {pattern.category.title()}
**Preference**: {pattern.extracted_rule}
**Reason**: {pattern.description}
**Examples**:
```{pattern.language or 'bash'}
# ✅ Correct
{pattern.correct_example}
# ❌ Incorrect
{pattern.incorrect_example}
```
**Confidence**: {int(pattern.confidence * 100)}% (learned {pattern.timestamp.date()})
---
"""
```
### File Selection Logic
```python
def choose_claudemd_file(pattern: Pattern, hierarchy: list) -> str:
"""
Choose appropriate CLAUDE.md file
Logic:
- Project-specific pattern → project CLAUDE.md
- Repeated across projects → parent/global CLAUDE.md
- Unclear → ask user once
"""
if pattern.project_specific:
return hierarchy['project']
# Check if pattern appears in other projects
other_projects = await find_pattern_in_other_projects(pattern)
if len(other_projects) >= 2:
# Pattern is universal
return hierarchy['global']
elif len(other_projects) == 1:
# Pattern in 2 projects → parent
return hierarchy['parent'] or hierarchy['global']
else:
# Single project
return hierarchy['project']
```
---
## Error Handling & Resilience
### Critical Requirements
1. **Always Exit 0**: Never fail Claude Code
2. **Async Processing**: Don't block execution
3. **Timeout**: 5 seconds max
4. **Fallback Mode**: Disable on repeated failures
5. **Logging**: All errors logged, never shown to user
### Implementation
```python
async def safe_hook_execution(hook_func, event: dict) -> dict:
"""
Safe wrapper for all hook functions
Guarantees:
- Never raises exceptions to caller
- Respects 5s timeout
- Auto-disables on repeated failures
- Always returns valid result
"""
try:
# 1. Check if hooks are enabled
if not await is_hooks_enabled():
return {'skipped': True, 'reason': 'hooks_disabled'}
# 2. Execute with timeout
result = await asyncio.wait_for(
hook_func(event),
timeout=5.0
)
# 3. Reset failure counter on success
await reset_failure_count()
return result
except asyncio.TimeoutError:
log_error("Hook timeout exceeded 5s")
await increment_failure_count()
return {'skipped': True, 'reason': 'timeout'}
except Exception as e:
log_error(f"Hook error: {e}")
await increment_failure_count()
# Auto-disable after 3 consecutive failures
if await get_failure_count() >= 3:
await disable_hooks()
log_error("Hooks auto-disabled due to repeated failures")
return {'skipped': True, 'reason': 'error'}
```
---
## Configuration
### Hooks Configuration File
**Location**: `~/.claude/hooks.json`
```json
{
"version": "1.0.0",
"enabled": true,
"hooks": {
"PreToolUse": [
{
"matcher": "*",
"hooks": [
{
"type": "command",
"command": "uv run --directory /path/to/mcp-standards python -m mcp_standards.hooks.capture_hook pre",
"timeout": 5000,
"async": true
}
]
}
],
"PostToolUse": [
{
"matcher": "*",
"hooks": [
{
"type": "command",
"command": "uv run --directory /path/to/mcp-standards python -m mcp_standards.hooks.capture_hook post",
"timeout": 5000,
"async": true
}
]
}
],
"SessionEnd": [
{
"matcher": "*",
"hooks": [
{
"type": "command",
"command": "uv run --directory /path/to/mcp-standards python -m mcp_standards.hooks.capture_hook session-end",
"timeout": 10000,
"async": true
}
]
}
]
},
"settings": {
"min_significance": 0.3,
"auto_update_claudemd": true,
"auto_generate_skills": true,
"notification_level": "important",
"max_failures": 3
}
}
```
### Environment Variables
```bash
# Enable/disable features
HOOKS_ENABLED=true
AUTO_LEARN=true
AUTO_UPDATE_CLAUDEMD=true
AUTO_GENERATE_SKILLS=true
# Performance tuning
SIGNIFICANCE_THRESHOLD=0.3
PATTERN_CONFIDENCE_THRESHOLD=0.9
BATCH_SIZE=100
# Paths
DB_PATH=.swarm/memory.db
AGENTDB_PATH=.agentdb/reasoningbank.db
SKILLS_DIR=.claude/skills
CLAUDEMD_PATH=CLAUDE.md
```
---
## Testing Strategy
### Unit Tests
```python
# test_hooks.py
async def test_significance_scoring():
"""Test significance scoring accuracy"""
event = create_test_event(tool='Write', file_path='/src/main.py')
score = await score_significance(event)
assert 0.5 <= score <= 0.6 # Write to src/ should score high
async def test_explicit_correction_detection():
"""Test explicit correction detection"""
event = create_test_event(
user_message="Use uv instead of pip",
tool='Bash',
command='pip install requests'
)
pattern = await detect_explicit_correction(event)
assert pattern is not None
assert pattern.confidence >= 0.9
assert 'uv' in pattern.extracted_rule
async def test_hooks_resilience():
"""Test hooks never fail Claude"""
event = create_malformed_event()
result = await safe_hook_execution(post_tool_use_hook, event)
assert result['skipped'] == True
# Should never raise exception
```
### Integration Tests
```python
# test_integration.py
async def test_full_learning_workflow():
"""Test end-to-end learning workflow"""
# 1. Execute tool
event1 = simulate_tool_execution('pip install requests')
await post_tool_use_hook(event1)
# 2. User corrects
event2 = simulate_user_correction("Use uv instead of pip")
await post_tool_use_hook(event2)
# 3. Repeat correction
event3 = simulate_tool_execution('pip install numpy')
event4 = simulate_user_correction("Use uv instead of pip")
await post_tool_use_hook(event3)
await post_tool_use_hook(event4)
# 4. Third correction triggers learning
event5 = simulate_tool_execution('pip install pandas')
event6 = simulate_user_correction("Always use uv")
await post_tool_use_hook(event5)
await post_tool_use_hook(event6)
# 5. Verify pattern learned
patterns = await get_learned_patterns()
assert len(patterns) == 1
assert patterns[0].confidence >= 0.9
# 6. Verify CLAUDE.md updated
claudemd = await read_file('CLAUDE.md')
assert 'uv' in claudemd
# 7. Verify skills file generated
assert await file_exists('.claude/skills/learned-uv-preference/SKILL.md')
```
---
## Performance Benchmarks
### Expected Overhead
| Operation | Overhead | Target |
|-----------|----------|--------|
| PreToolUse hook | <2ms | <5ms |
| PostToolUse hook | <3ms | <5ms |
| SessionEnd hook | <100ms | <1s |
| Pattern extraction | <50ms | <100ms |
| CLAUDE.md update | <200ms | <500ms |
### Optimization Strategies
1. **Async Processing**: All heavy work in background
2. **Batching**: Group database operations
3. **Caching**: Cache CLAUDE.md parsing
4. **Lazy Loading**: Load AgentDB only when needed
5. **Incremental Updates**: Don't recompute everything
---
## Migration Plan
### Phase 1: Basic Hooks (Week 1)
- [ ] Implement capture_hook.py entry point
- [ ] Setup significance_scorer.py
- [ ] Create pattern_extractor.py
- [ ] Configure hooks.json
- [ ] Test with Claude Code
### Phase 2: Intelligence (Week 2)
- [ ] Implement 5-layer detection
- [ ] Add pattern learning logic
- [ ] Setup confidence scoring
- [ ] Create pattern database tables
### Phase 3: Auto-Update (Week 3)
- [ ] Implement CLAUDE.md manager
- [ ] Create skills auto-generation
- [ ] Add smart merge logic
- [ ] Setup backup system
### Phase 4: Polish (Week 4)
- [ ] Add comprehensive tests
- [ ] Performance optimization
- [ ] Error handling hardening
- [ ] Documentation and examples
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## Next Steps
1. Review hooks implementation plan with hive mind
2. Implement proof-of-concept hook
3. Test with real Claude Code executions
4. Iterate based on performance data
5. Create detailed API specifications
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**Designed by**: Memory Architecture Agent
**Coordinated via**: Hive Mind Collective
**Implementation**: `.mcp_standards/hooks/`