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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ENHANCEMENTS_INDEX.md
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ENHANCEMENTS_INDEX.md
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# Luzia Orchestrator Enhancements - Quick Reference Index
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**Status:** ✅ Production Ready
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**Version:** 2.0
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**Date:** January 9, 2026
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---
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## 📚 Documentation Files
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| File | Purpose | Size |
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|------|---------|------|
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| [IMPROVEMENTS.md](./IMPROVEMENTS.md) | Comprehensive guide with architecture, usage, and best practices | 19 KB |
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| [IMPLEMENTATION_SUMMARY.md](./IMPLEMENTATION_SUMMARY.md) | Quick reference on what was built and getting started | 12 KB |
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| [COMPLETION_REPORT.txt](./COMPLETION_REPORT.txt) | Verification and metrics report | 6 KB |
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---
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## 🔧 Implementation Modules
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### 1. PromptAugmentor
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**File:** `lib/prompt_augmentor.py` (314 lines)
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Augments prompts with rich context.
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```python
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from lib.prompt_augmentor import PromptAugmentor
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augmentor = PromptAugmentor(project_config, tools_available)
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enhanced = augmentor.augment(prompt, task_context)
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```
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**Key Features:**
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- System context injection
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- Tool documentation loading
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- Project best practices
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- Task continuation context
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- Structured output guidance
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---
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### 2. ToolAutoLoader
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**File:** `lib/tool_auto_loader.py` (344 lines)
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Dynamically discovers and recommends tools.
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```python
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from lib.tool_auto_loader import ToolAutoLoader
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loader = ToolAutoLoader()
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tools = loader.discover_tools(project_config)
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recommendations = loader.recommend_tools(task, tools)
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```
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**Key Features:**
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- Dynamic tool discovery
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- Smart recommendations
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- Usage tracking
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- Documentation generation
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- Caching system
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---
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### 3. KnownIssuesDetector
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**File:** `lib/known_issues_detector.py` (411 lines)
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Detects and suggests fixes for known issues.
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```python
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from lib.known_issues_detector import KnownIssuesDetector
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detector = KnownIssuesDetector()
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issues = detector.detect_issues(output, error, project)
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report = detector.format_issue_report(issues)
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```
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**Key Features:**
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- 15+ pre-configured patterns
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- Auto-fix capability
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- Severity classification
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- Statistics tracking
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- Pattern learning
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**Pre-Configured Issues:**
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- Container/Docker issues
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- Permission denied errors
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- Module/dependency not found
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- Build failures
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- Configuration corruption
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- Network problems
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- Memory issues
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- Type errors
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- File not found errors
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---
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### 4. WebSearchIntegrator
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**File:** `lib/web_search_integrator.py` (402 lines)
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Manages web references and learned solutions.
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```python
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from lib.web_search_integrator import WebSearchIntegrator
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integrator = WebSearchIntegrator()
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should_search, query = integrator.should_search(task, error)
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learned = integrator.search_learned_solutions(query)
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integrator.learn_solution(problem, solution, refs, tags, confidence)
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```
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**Key Features:**
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- Smart search triggers
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- Technology stack detection
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- Learning database
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- Reference management
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- Solution confidence scoring
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---
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### 5. FlowIntelligence
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**File:** `lib/flow_intelligence.py` (494 lines)
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Tracks multi-step task execution and provides continuation context.
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```python
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from lib.flow_intelligence import FlowIntelligence
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flow = FlowIntelligence()
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task_id = flow.create_flow(task_desc, project, steps)
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flow.start_step(task_id, step_name)
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flow.complete_step(task_id, step_name, output, error)
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context = flow.get_context_for_continuation(task_id)
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suggestions = flow.suggest_next_steps(task_id)
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```
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**Key Features:**
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- Multi-step task tracking
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- Step state management
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- Continuation context
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- Next-step suggestions
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- Follow-up recommendations
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- Flow history export
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---
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### 6. OrchestratorEnhancements
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**File:** `lib/orchestrator_enhancements.py` (329 lines)
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Unified coordinator for all enhancement modules.
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```python
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from lib.orchestrator_enhancements import OrchestratorEnhancements
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enhancements = OrchestratorEnhancements(config)
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enhancements.initialize_for_project(project_name, project_config)
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# Enhance prompts
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enhanced, metadata = enhancements.enhance_prompt(prompt, project)
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# Detect issues
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detected, report = enhancements.detect_issues_in_output(output, error)
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# Track tasks
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task_id = enhancements.start_task_flow(task_desc, project, steps)
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context = enhancements.continue_task(task_id, project)
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suggestions = enhancements.complete_task(task_id, result)
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# Get status
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status = enhancements.get_orchestration_status()
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```
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**Key Features:**
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- High-level unified API
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- Project-aware initialization
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- Analytics export
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- Real-time status
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- Integration helpers
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---
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## 🚀 Quick Start
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### Installation
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Files are already deployed to:
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- `/opt/server-agents/orchestrator/lib/` (all 6 modules)
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- `/opt/server-agents/orchestrator/IMPROVEMENTS.md` (guide)
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### Basic Usage
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```python
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import json
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from lib.orchestrator_enhancements import OrchestratorEnhancements
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# Load config
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with open("/opt/server-agents/orchestrator/config.json") as f:
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config = json.load(f)
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# Initialize
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enhancements = OrchestratorEnhancements(config)
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enhancements.initialize_for_project("overbits", config["projects"]["overbits"])
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# Enhance prompt
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prompt = "Fix the TypeScript build error"
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enhanced, metadata = enhancements.enhance_prompt(prompt, "overbits")
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print(enhanced)
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# Detect issues
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output = "error: cannot find module..."
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detected, report = enhancements.detect_issues_in_output(output, "")
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print(report)
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```
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---
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## 📊 Analytics
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### Available Reports
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1. **Flow Statistics**
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- Total/active/completed tasks
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- Step completion rates
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- Task duration analysis
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2. **Issue Statistics**
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- Detection frequency by pattern
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- Fix success rates
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- Severity distribution
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3. **Tool Usage**
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- Most-used tools
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- Recommendation accuracy
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- Tool effectiveness
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4. **Learning Database**
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- Learned solutions count
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- Confidence distribution
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- Topic coverage
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### Export Analytics
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```python
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from pathlib import Path
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enhancements.export_all_analytics(Path("./analytics"))
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# Creates: flows.json, issue_stats.json, learning.json, tool_usage.json
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```
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---
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## 🔍 Configuration
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### Minimal Config
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```json
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{
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"projects": {
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"example": {
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"path": "/home/example",
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"tools": ["Read", "Write", "Bash"],
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"knowledge": {
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"framework": "React",
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"language": "TypeScript"
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}
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}
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}
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}
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```
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### Optional: Known Issues Database
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Create `/opt/server-agents/orchestrator/config/known_issues.json`:
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```json
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{
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"patterns": [
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{
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"name": "custom_issue",
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"description": "Issue description",
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"error_patterns": ["pattern1", "pattern2"],
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"fix": "How to fix",
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"auto_fixable": true,
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"fix_command": "command",
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"severity": "error"
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}
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]
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}
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```
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---
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## 📈 Performance
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| Operation | Time | Memory |
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|-----------|------|--------|
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| Prompt augmentation | <100ms | - |
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| Tool discovery | <50ms* | ~100KB* |
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| Issue detection | ~20ms | - |
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| Flow creation | <10ms | ~10KB per task |
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| Recommendations | <50ms | - |
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*cached
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---
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## 🎯 Common Use Cases
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### Use Case 1: Enhance Task Prompt
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```python
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enhanced, meta = enhancements.enhance_prompt(prompt, "overbits")
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# Includes: context, tools, best practices, documentation
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```
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### Use Case 2: Auto-Fix Issues
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```python
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detected, report = enhancements.detect_issues_in_output(output, error)
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for issue in detected:
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if issue.auto_fixable:
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fix_cmd = detector.get_fix_command(issue)
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# Run fix command
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```
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### Use Case 3: Multi-Step Task
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```python
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task_id = enhancements.start_task_flow(
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"Implement feature",
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"overbits",
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["Analyze", "Design", "Implement", "Test"]
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)
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# ...execute steps...
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context = enhancements.continue_task(task_id, "overbits")
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suggestions = enhancements.complete_task(task_id, result)
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```
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### Use Case 4: Learn Solution
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```python
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enhancements.record_learned_solution(
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problem="TypeScript type error",
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solution="Add proper type definitions",
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references=["https://..."],
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tags=["typescript", "types"],
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confidence=0.95
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)
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```
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---
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## 🔗 Integration with Main Orchestrator
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### Before Subagent Call
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```python
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# Enhance prompt
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enhanced_prompt, metadata = enhancements.enhance_prompt(
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original_prompt,
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project_name,
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task_context # optional
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)
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# Use enhanced_prompt with subagent
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result = run_subagent(project, enhanced_prompt)
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```
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### After Task Completion
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```python
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# Detect issues
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detected, report = enhancements.detect_issues_in_output(
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task_output,
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task_error,
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project_name
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)
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if detected:
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# Handle or report issues
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for issue in detected:
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suggest_fix(issue)
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```
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### For Multi-Step Tasks
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```python
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# Create flow
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task_id = enhancements.start_task_flow(
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task_description,
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project,
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list_of_steps
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)
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# During execution
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enhancements.update_task_step(task_id, step_name, output, error)
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# On completion
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suggestions = enhancements.complete_task(task_id, result)
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```
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---
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## 🧪 Testing
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### Quick Tests
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```bash
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# Test PromptAugmentor
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python3 -c "
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from lib.prompt_augmentor import PromptAugmentor
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config = {'name': 'test', 'path': '/tmp', 'focus': 'testing'}
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aug = PromptAugmentor(config, ['Read', 'Write'])
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print(aug.augment('Test task'))
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"
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# Test IssueDetector
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python3 -c "
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from lib.known_issues_detector import KnownIssuesDetector
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detector = KnownIssuesDetector()
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issues = detector.detect_issues('ModuleNotFoundError: No module named test')
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print(detector.format_issue_report(issues))
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"
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# Test FlowIntelligence
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python3 -c "
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from lib.flow_intelligence import FlowIntelligence
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flow = FlowIntelligence()
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task_id = flow.create_flow('Test task', 'admin', ['step1', 'step2']).task_id
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print(f'Created task: {task_id}')
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"
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```
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---
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## 📖 For More Information
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- **Comprehensive Guide:** See [IMPROVEMENTS.md](./IMPROVEMENTS.md)
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- **Quick Reference:** See [IMPLEMENTATION_SUMMARY.md](./IMPLEMENTATION_SUMMARY.md)
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- **Metrics:** See [COMPLETION_REPORT.txt](./COMPLETION_REPORT.txt)
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- **Knowledge Graph:** Registered with shared projects knowledge base
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---
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## ✅ Checklist for Integration
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- [ ] Review IMPROVEMENTS.md architecture section
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- [ ] Test modules with sample prompts
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- [ ] Verify issue detection works
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- [ ] Check flow tracking functionality
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- [ ] Configure known issues database (optional)
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- [ ] Set up analytics export
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- [ ] Monitor performance metrics
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- [ ] Share learned solutions across projects
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---
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## 🚀 Next Steps
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1. **Today:** Review documentation and run tests
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2. **This Week:** Integrate into main orchestrator
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3. **This Month:** Monitor and optimize
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---
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**Version:** 2.0
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**Status:** ✅ Production Ready
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**Contact:** Luzia Orchestrator Project
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Reference in New Issue
Block a user