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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admin
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examples/prompt_engineering_demo.py
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310
examples/prompt_engineering_demo.py
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#!/usr/bin/env python3
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"""
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Prompt Engineering Demo Script
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Demonstrates the advanced prompt engineering techniques implemented in Luzia.
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Run with: python3 examples/prompt_engineering_demo.py
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"""
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import sys
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from pathlib import Path
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# Add lib to path
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sys.path.insert(0, str(Path(__file__).parent.parent / "lib"))
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from prompt_techniques import (
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TaskType, PromptStrategy, PromptEngineer,
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ChainOfThoughtEngine, FewShotExampleBuilder, RoleBasedPrompting,
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TaskSpecificPatterns
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)
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from prompt_integration import (
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PromptIntegrationEngine, ComplexityAdaptivePrompting,
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DomainSpecificAugmentor
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)
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def demo_chain_of_thought():
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"""Demonstrate Chain-of-Thought prompting"""
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print("\n" + "="*80)
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print("DEMO 1: Chain-of-Thought Prompting")
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print("="*80)
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task = "Implement a distributed caching layer for database queries with TTL-based invalidation"
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print(f"\nOriginal Task:\n{task}\n")
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print("CoT Prompt (Complexity 3):")
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print("-" * 40)
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cot_prompt = ChainOfThoughtEngine.generate_cot_prompt(task, complexity=3)
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print(cot_prompt)
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def demo_few_shot():
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"""Demonstrate Few-Shot Learning"""
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print("\n" + "="*80)
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print("DEMO 2: Few-Shot Learning")
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print("="*80)
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builder = FewShotExampleBuilder()
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examples = builder.build_examples_for_task(TaskType.IMPLEMENTATION, num_examples=2)
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print("\nFew-Shot Examples for IMPLEMENTATION tasks:")
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print("-" * 40)
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formatted = builder.format_examples_for_prompt(examples)
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print(formatted)
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def demo_role_based():
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"""Demonstrate Role-Based Prompting"""
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print("\n" + "="*80)
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print("DEMO 3: Role-Based Prompting")
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print("="*80)
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for task_type in [TaskType.DEBUGGING, TaskType.ANALYSIS, TaskType.IMPLEMENTATION]:
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print(f"\n{task_type.value.upper()}:")
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print("-" * 40)
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role_prompt = RoleBasedPrompting.get_role_prompt(task_type)
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print(role_prompt[:200] + "...")
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def demo_task_patterns():
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"""Demonstrate Task-Specific Patterns"""
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print("\n" + "="*80)
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print("DEMO 4: Task-Specific Patterns")
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print("="*80)
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patterns = TaskSpecificPatterns()
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print("\nAnalysis Pattern:")
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print("-" * 40)
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analysis = patterns.get_analysis_pattern(
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"Database Performance",
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["Query optimization", "Index efficiency", "Cache effectiveness"]
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)
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print(analysis[:300] + "...")
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print("\n\nDebugging Pattern:")
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print("-" * 40)
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debug = patterns.get_debugging_pattern(
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"Intermittent 503 errors under high load",
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"API Gateway"
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)
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print(debug[:300] + "...")
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def demo_complexity_detection():
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"""Demonstrate Complexity Estimation"""
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print("\n" + "="*80)
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print("DEMO 5: Complexity Adaptation")
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print("="*80)
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test_tasks = [
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("Fix typo in README", "Simple"),
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("Add logging to error handler", "Basic"),
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("Implement rate limiting for API", "Moderate"),
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("Refactor authentication system with concurrent access and security considerations", "Complex"),
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("Design and implement distributed transaction system with encryption, failover, and performance optimization", "Very Complex")
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]
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print("\nTask Complexity Detection:")
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print("-" * 40)
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for task, expected_level in test_tasks:
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complexity = ComplexityAdaptivePrompting.estimate_complexity(task, TaskType.IMPLEMENTATION)
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strategies = ComplexityAdaptivePrompting.get_prompting_strategies(complexity)
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print(f"\nTask: {task}")
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print(f"Expected: {expected_level} | Detected Complexity: {complexity}/5")
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print(f"Strategies: {len(strategies)} - {', '.join(s.value for s in strategies[:3])}")
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def demo_integration_engine():
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"""Demonstrate Full Integration Engine"""
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print("\n" + "="*80)
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print("DEMO 6: Full Prompt Integration Engine")
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print("="*80)
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# Initialize engine
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project_config = {
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"name": "luzia",
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"path": "/opt/server-agents/orchestrator",
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"focus": "Self-improving orchestrator for multi-project coordination"
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}
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engine = PromptIntegrationEngine(project_config)
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# Example 1: Simple implementation task
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task1 = "Add request rate limiting to the API endpoint"
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augmented1, metadata1 = engine.augment_for_task(
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task1,
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task_type=TaskType.IMPLEMENTATION,
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domain="backend"
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)
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print(f"\nTask 1: {task1}")
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print(f"Complexity: {metadata1['complexity']}/5")
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print(f"Strategies: {metadata1['strategies']}")
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print(f"Augmentation Ratio: {metadata1['final_token_estimate'] / len(task1.split()):.1f}x")
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print("\nAugmented Prompt (first 400 chars):")
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print("-" * 40)
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print(augmented1[:400] + "...\n")
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# Example 2: Complex debugging task
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task2 = """Debug intermittent race condition in async event handler that only manifests under high concurrent load.
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The issue causes occasional data corruption in shared state and we need to identify the synchronization issue and fix it."""
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augmented2, metadata2 = engine.augment_for_task(
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task2,
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task_type=TaskType.DEBUGGING,
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domain="backend"
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)
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print(f"Task 2: {task2[:80]}...")
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print(f"Complexity: {metadata2['complexity']}/5")
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print(f"Strategies: {metadata2['strategies']}")
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print(f"Augmentation Ratio: {metadata2['final_token_estimate'] / len(task2.split()):.1f}x")
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# Example 3: Security analysis
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task3 = "Analyze security implications of the current token storage approach"
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augmented3, metadata3 = engine.augment_for_task(
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task3,
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task_type=TaskType.ANALYSIS,
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domain="crypto",
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context={
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"previous_results": {
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"current_approach": "JWT stored in localStorage",
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"threat_model": "Browser-based XSS attacks"
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},
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"blockers": ["Need to decide on alternative storage mechanism"]
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}
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)
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print(f"\n\nTask 3: {task3}")
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print(f"Complexity: {metadata3['complexity']}/5")
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print(f"Strategies: {metadata3['strategies']}")
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print(f"Augmentation Ratio: {metadata3['final_token_estimate'] / len(task3.split()):.1f}x")
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def demo_domain_contexts():
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"""Demonstrate Domain-Specific Contexts"""
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print("\n" + "="*80)
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print("DEMO 7: Domain-Specific Contexts")
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print("="*80)
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domains = ["backend", "frontend", "crypto", "devops", "research", "orchestration"]
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for domain in domains:
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context = DomainSpecificAugmentor.get_domain_context(domain)
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print(f"\n{domain.upper()}:")
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print(f" Focus: {context['focus']}")
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print(f" Priorities: {', '.join(context['priorities'][:2])}")
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print(f" Best Practices: {context['best_practices'][0]}")
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def demo_context_continuation():
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"""Demonstrate Task Continuation with Context"""
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print("\n" + "="*80)
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print("DEMO 8: Task Continuation with Previous Context")
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print("="*80)
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project_config = {
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"name": "luzia",
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"path": "/opt/server-agents/orchestrator",
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"focus": "Self-improving orchestrator"
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}
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engine = PromptIntegrationEngine(project_config)
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# Initial task
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initial_task = "Design a caching strategy for frequently accessed user profiles"
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print(f"\nInitial Task: {initial_task}")
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augmented_initial, metadata_initial = engine.augment_for_task(
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initial_task,
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task_type=TaskType.PLANNING,
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domain="backend"
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)
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print(f"Initial complexity: {metadata_initial['complexity']}")
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# Continuation with previous context
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context = {
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"previous_results": {
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"chosen_strategy": "Redis with TTL-based invalidation",
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"estimated_hit_rate": "85%",
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"cache_size": "~500MB per instance"
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},
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"state": {
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"implementation_status": "Completed caching layer",
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"current_focus": "Optimizing invalidation strategy"
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},
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"blockers": [
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"Need to handle cache stampede on popular profiles",
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"Invalidation latency causing stale data"
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]
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}
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continuation_task = "Continue: optimize cache invalidation to prevent stampede and reduce staleness"
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print(f"\nContinuation Task: {continuation_task}")
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augmented_cont, metadata_cont = engine.augment_for_task(
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continuation_task,
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task_type=TaskType.IMPLEMENTATION,
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domain="backend",
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context=context
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)
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print(f"Continuation complexity: {metadata_cont['complexity']}")
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print(f"Context included: {bool(context)}")
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print("\nAugmented Prompt includes:")
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print(" ✓ System Instructions")
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print(" ✓ Role-Based Prompting (Senior Engineer)")
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print(" ✓ Domain Context (Backend best practices)")
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print(" ✓ Task Continuation (Previous results, current state, blockers)")
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print(" ✓ Task-Specific Pattern (Implementation)")
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def main():
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"""Run all demonstrations"""
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print("\n" + "█"*80)
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print("█ LUZIA ADVANCED PROMPT ENGINEERING DEMONSTRATIONS")
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print("█"*80)
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demos = [
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("Chain-of-Thought Prompting", demo_chain_of_thought),
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("Few-Shot Learning", demo_few_shot),
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("Role-Based Prompting", demo_role_based),
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("Task-Specific Patterns", demo_task_patterns),
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("Complexity Adaptation", demo_complexity_detection),
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("Full Integration Engine", demo_integration_engine),
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("Domain-Specific Contexts", demo_domain_contexts),
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("Task Continuation", demo_context_continuation),
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]
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for i, (name, demo_func) in enumerate(demos, 1):
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try:
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demo_func()
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except Exception as e:
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print(f"\n[ERROR in {name}]: {e}")
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import traceback
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traceback.print_exc()
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print("\n" + "█"*80)
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print("█ DEMONSTRATIONS COMPLETE")
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print("█"*80)
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print("\nKey Takeaways:")
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print("1. Chain-of-Thought breaks complex reasoning into steps")
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print("2. Few-Shot examples improve understanding of task patterns")
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print("3. Role-based prompting sets appropriate expertise level")
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print("4. Complexity adaptation optimizes strategy selection")
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print("5. Domain-specific contexts apply relevant best practices")
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print("6. Task continuation preserves state across multi-step work")
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print("\nIntegrate into Luzia with:")
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print(" from prompt_integration import PromptIntegrationEngine")
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print(" engine = PromptIntegrationEngine(project_config)")
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print(" augmented, metadata = engine.augment_for_task(task, task_type, domain)")
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print()
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if __name__ == "__main__":
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main()
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