Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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docs/SKILL_LEARNING_QUICKSTART.md
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docs/SKILL_LEARNING_QUICKSTART.md
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# Skill Learning System - Quick Start
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## TL;DR
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The skill learning system automatically learns from successful tasks and QA passes, storing learnings in the knowledge graph to improve future recommendations.
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**Enable it in one line:**
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```bash
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python3 lib/qa_validator.py --learn --sync --verbose
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```
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## How It Works
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1. **Task Completes** → QA validation passes
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2. **System Analyzes** → Extracts skills used, patterns, tools
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3. **Learning Created** → Stores in knowledge graph with metadata
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4. **Future Tasks** → System recommends relevant skills based on prompt
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## Basic Usage
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### Run QA with Learning Extraction
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```bash
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# Standard QA validation only
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python3 lib/qa_validator.py --sync --verbose
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# With learning extraction enabled
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python3 lib/qa_validator.py --learn --sync --verbose
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```
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### Extract Learnings from Completed Task
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```python
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from lib.skill_learning_engine import SkillLearningSystem
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system = SkillLearningSystem()
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task_data = {
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"task_id": "my_task",
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"prompt": "Refactor authentication module",
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"project": "overbits",
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"status": "success",
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"tools_used": ["Bash", "Read", "Edit"],
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"duration": 45.2,
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"result_summary": "Successfully refactored",
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"qa_passed": True,
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"timestamp": "2026-01-09T12:00:00"
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}
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qa_results = {
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"passed": True,
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"results": {"syntax": True, "routes": True},
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"summary": {"errors": 0}
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}
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result = system.process_task_completion(task_data, qa_results)
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print(f"Learning ID: {result['learning_id']}")
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```
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### Get Recommendations for a Task
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```python
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system = SkillLearningSystem()
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recommendations = system.get_recommendations(
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task_prompt="Debug authentication issue",
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project="overbits"
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)
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for rec in recommendations:
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print(f"{rec['skill']}: {rec['confidence']:.0%} confidence")
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```
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### View Learned Skills Profile
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```python
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profile = system.get_learning_summary()
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print(f"Total learnings: {profile['total_learnings']}")
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print(f"Top skills: {profile['top_skills']}")
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```
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## What Gets Learned
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The system extracts and learns:
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### Tool Usage
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- Which tools are used for which tasks
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- Tool frequency and patterns
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- Tool combinations that work well together
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### Decision Patterns
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- **Optimization**: Performance improvement approaches
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- **Debugging**: Error diagnosis and fixing strategies
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- **Testing**: Validation and verification techniques
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- **Refactoring**: Code improvement methods
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- **Documentation**: Documentation practices
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- **Integration**: System integration approaches
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- **Automation**: Automation and scheduling patterns
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### Project Knowledge
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- Which projects benefit from which approaches
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- Project-specific tool combinations
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- Project patterns and best practices
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### Quality Metrics
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- Success rates by tool combination
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- Task completion times
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- QA pass rates by category
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## Storage
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All learnings stored in the **research knowledge graph**:
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```
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/etc/luz-knowledge/research.db
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```
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Query learnings:
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```bash
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python3 lib/knowledge_graph.py search "optimization"
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python3 lib/knowledge_graph.py list research finding
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```
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## Examples
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### Example 1: Learn from Database Optimization
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```bash
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# Task completes successfully with QA passing
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python3 lib/qa_validator.py --learn --sync
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# System automatically:
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# - Identifies tools used: Bash, Read, Edit
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# - Recognizes pattern: optimization
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# - Stores learning about database optimization
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# - Creates relations between tools and pattern
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```
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### Example 2: Get Recommendations
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```python
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# Later, for similar task:
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recommendations = system.get_recommendations(
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"Optimize API endpoint performance",
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project="overbits"
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)
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# Might suggest:
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# - Use Bash for performance analysis
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# - Use Edit for code changes
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# - Watch for optimization patterns
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# - Similar to previous successful tasks
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```
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### Example 3: Build Team Knowledge
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Run multiple tasks with learning enabled:
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```bash
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# Day 1: Deploy task with --learn
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python3 lib/qa_validator.py --learn --sync
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# Day 2: Optimization task with --learn
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python3 lib/qa_validator.py --learn --sync
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# Day 3: Similar deployment task
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# System now has learnings from both previous tasks
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recommendations = system.get_recommendations("Deploy new version")
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```
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## Statistics and Monitoring
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View learning system statistics:
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```bash
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python3 lib/qa_learning_integration.py --stats
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```
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Output:
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```
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=== QA Learning Integration Statistics ===
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total_events: 42
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qa_passed: 40
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learnings_extracted: 38
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extraction_rate: 0.95
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last_event: 2026-01-09T12:00:00
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```
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## Testing
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Quick test of the system:
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```bash
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python3 lib/skill_learning_engine.py test
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```
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Full test suite:
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```bash
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python3 -m pytest tests/test_skill_learning.py -v
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```
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## Troubleshooting
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### "No learnings extracted"
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- Check that QA actually passed
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- Verify knowledge graph is accessible
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- Run with `--verbose` to see details
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### "Empty recommendations"
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- Need to complete tasks with `--learn` first
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- Task prompt must match learning keywords
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- Check knowledge graph has entries:
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```bash
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python3 lib/knowledge_graph.py list research finding
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```
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### "Permission denied"
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- Check `/etc/luz-knowledge/` permissions
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- Ensure user is in `ai-users` group
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- Check knowledge graph domain permissions
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## Next Steps
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1. **Start collecting learnings**: Run tasks with `--learn`
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2. **Monitor learnings**: Check statistics and knowledge graph
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3. **Use recommendations**: Integrate into task routing
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4. **Refine patterns**: Add custom extraction patterns as needed
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## Learn More
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- Full documentation: [SKILL_LEARNING_SYSTEM.md](./SKILL_LEARNING_SYSTEM.md)
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- Source code: `lib/skill_learning_engine.py`
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- Integration: `lib/qa_learning_integration.py`
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- Tests: `tests/test_skill_learning.py`
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