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>
This commit is contained in:
admin
589
lib/prompt_techniques.py
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589
lib/prompt_techniques.py
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#!/usr/bin/env python3
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"""
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Advanced Prompt Augmentation Techniques
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Implements latest research on prompt engineering for different task types:
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1. Chain-of-Thought (CoT): Step-by-step reasoning
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2. Few-Shot Learning: Examples for task clarification
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3. Role-Based Prompting: Set specific persona/expertise
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4. System Prompts: Foundational constraints and guidelines
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5. Context Hierarchies: Prioritized context levels
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6. Task-Specific Patterns: Domain-optimized prompt structures
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7. Dynamic Difficulty: Adaptive prompting based on complexity
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References:
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- Wei et al. (2022): Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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- Brown et al. (2020): Language Models are Few-Shot Learners
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- Kojima et al. (2022): Large Language Models are Zero-Shot Reasoners
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- Reynolds & McDonell (2021): Prompt Programming for Large Language Models
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- Zhong et al. (2023): How Can We Know What Language Models Know?
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"""
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import json
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from typing import Dict, List, Optional, Any, Tuple
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from enum import Enum
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from dataclasses import dataclass, asdict
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from datetime import datetime
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class TaskType(Enum):
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"""Enumeration of task types with specific augmentation strategies"""
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ANALYSIS = "analysis"
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DEBUGGING = "debugging"
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IMPLEMENTATION = "implementation"
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RESEARCH = "research"
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REFACTORING = "refactoring"
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PLANNING = "planning"
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REVIEW = "review"
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OPTIMIZATION = "optimization"
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TESTING = "testing"
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DOCUMENTATION = "documentation"
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SECURITY = "security"
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class PromptStrategy(Enum):
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"""Prompt augmentation strategies"""
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CHAIN_OF_THOUGHT = "chain_of_thought"
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FEW_SHOT = "few_shot"
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ROLE_BASED = "role_based"
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SYSTEM_INSTRUCTION = "system_instruction"
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TREE_OF_THOUGHT = "tree_of_thought"
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SELF_CONSISTENCY = "self_consistency"
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UNCERTAINTY_AWARE = "uncertainty_aware"
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@dataclass
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class PromptContext:
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"""Structured prompt context with priority levels"""
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task_type: TaskType
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primary: Dict[str, str] # Highest priority - directly relevant
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secondary: Dict[str, str] # Important context
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tertiary: Dict[str, str] # Nice to have
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examples: List[Dict[str, str]] # Few-shot examples
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role: str = "Expert Analyst"
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complexity_level: int = 1 # 1-5 scale
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timestamp: str = None
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def __post_init__(self):
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if self.timestamp is None:
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self.timestamp = datetime.now().isoformat()
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class ChainOfThoughtEngine:
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"""Implements Chain-of-Thought prompting for complex reasoning"""
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@staticmethod
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def generate_cot_prompt(task: str, complexity: int = 1) -> str:
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"""
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Generate Chain-of-Thought prompt encouraging step-by-step reasoning.
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Args:
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task: The task to decompose
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complexity: 1-5 scale for reasoning depth
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"""
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depth = min(complexity, 5)
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num_steps = 3 + depth
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prompt = f"""Please solve this step-by-step:
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{task}
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**Your Reasoning Process:**
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Think through this problem systematically. Break it into {num_steps} logical steps:
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"""
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for i in range(1, num_steps + 1):
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prompt += f"Step {i}: [What is the {i}th logical component of this problem?]\n"
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prompt += """
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After completing each step, briefly verify your logic before moving to the next.
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Explicitly state any assumptions you're making.
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If you encounter conflicting information, address it directly.
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**Final Answer:**
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Summarize your complete solution, clearly showing how each step led to your conclusion."""
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return prompt
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@staticmethod
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def generate_subquestion_cot(task: str, context: str = "") -> str:
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"""
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Generate intermediate question-based Chain-of-Thought.
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Breaks complex problems into subquestions to improve reasoning.
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Reference: Wei et al. (2022), Kojima et al. (2022)
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"""
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prompt = f"""Break down and answer this problem systematically:
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**Main Question:**
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{task}
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{f"**Context:**{chr(10)}{context}" if context else ""}
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**Approach:**
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1. Identify the key subquestions that must be answered to solve the main problem
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2. Answer each subquestion with clear reasoning
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3. Combine the answers into a comprehensive solution
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4. Verify your solution makes sense in context
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Please work through this methodically, showing your thinking at each stage."""
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return prompt
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class FewShotExampleBuilder:
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"""Constructs few-shot examples for better task understanding"""
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@staticmethod
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def build_examples_for_task(task_type: TaskType, num_examples: int = 3) -> List[Dict[str, str]]:
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"""Build task-specific few-shot examples"""
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examples_library = {
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TaskType.ANALYSIS: [
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{
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"input": "Analyze the performance bottleneck in this authentication flow",
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"approach": "1) Identify critical path, 2) Measure time per step, 3) Locate worst performer, 4) Suggest optimization",
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"output_structure": "Current bottleneck: [X]. Root cause: [Y]. Recommended fix: [Z]. Expected improvement: [percentage]%"
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},
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{
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"input": "Analyze security implications of storing user tokens in localStorage",
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"approach": "1) Enumerate attack vectors, 2) Assess likelihood and impact, 3) Compare to alternatives, 4) Make recommendation",
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"output_structure": "Risks: [list]. Severity: [high/medium/low]. Better approach: [X]. Implementation effort: [Y]"
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},
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{
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"input": "Analyze code complexity and maintainability of this module",
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"approach": "1) Calculate metrics (cyclomatic, cognitive), 2) Identify problematic patterns, 3) Assess testing difficulty, 4) Recommend refactoring",
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"output_structure": "Complexity score: [X]/10. Hot spots: [list]. Refactoring priority: [high/medium/low]"
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}
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],
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TaskType.DEBUGGING: [
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{
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"input": "Fix intermittent race condition in async handler",
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"approach": "1) Understand race condition mechanics, 2) Create minimal reproducible case, 3) Identify ordering issue, 4) Add synchronization, 5) Test thoroughly",
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"output_structure": "Root cause: [X]. Fix location: [file:line]. Change: [code diff]. Test strategy: [steps]"
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},
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{
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"input": "Debug memory leak in event listeners",
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"approach": "1) Profile memory usage, 2) Identify growth pattern, 3) Find leaked references, 4) Remove cleanup, 5) Verify fix",
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"output_structure": "Leak type: [X]. Source: [component]. Fix: [cleanup code]. Verification: [test approach]"
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},
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{
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"input": "Fix undefined behavior in concurrent map access",
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"approach": "1) Reproduce concurrency issue, 2) Find synchronization gap, 3) Add proper locking, 4) Test with concurrent load",
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"output_structure": "Issue: [X]. Cause: [Y]. Fix: [locking mechanism]. Verification: [concurrency test]"
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}
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],
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TaskType.IMPLEMENTATION: [
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{
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"input": "Implement rate limiting for API endpoint",
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"approach": "1) Define strategy (sliding window/token bucket), 2) Choose storage (in-memory/redis), 3) Implement core logic, 4) Add tests",
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"output_structure": "Strategy: [X]. Storage: [Y]. Key metrics tracked: [list]. Test coverage: [percentage]%"
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},
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{
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"input": "Add caching layer to database queries",
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"approach": "1) Identify hot queries, 2) Choose cache (redis/memcached), 3) Set TTL strategy, 4) Handle invalidation, 5) Monitor hit rate",
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"output_structure": "Cache strategy: [X]. Expected hit rate: [Y]%. Hit cost: [Z]ms. Invalidation: [method]"
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},
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{
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"input": "Implement graceful shutdown with in-flight request handling",
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"approach": "1) Define shutdown signal handling, 2) Stop accepting new requests, 3) Wait for in-flight, 4) Timeout and force quit",
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"output_structure": "Signal handling: [X]. Timeout: [Y]s. Graceful drain: [code]. Forced quit: [code]"
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}
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],
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TaskType.REFACTORING: [
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{
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"input": "Reduce cognitive complexity in 500-line function",
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"approach": "1) Map control flow, 2) Extract conditional branches, 3) Create helper methods, 4) Test each change, 5) Verify coverage",
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"output_structure": "Original complexity: [X]. Target: [Y]. Extracted methods: [list]. Final complexity: [Z]"
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}
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],
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TaskType.TESTING: [
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{
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"input": "Write comprehensive tests for authentication module",
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"approach": "1) Identify happy path, 2) List edge cases, 3) Test error conditions, 4) Add integration tests, 5) Measure coverage",
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"output_structure": "Test count: [X]. Coverage: [Y]%. Critical paths: [Z]. Integration tests: [list]"
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}
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]
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}
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examples = examples_library.get(task_type, [])
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return examples[:num_examples]
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@staticmethod
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def format_examples_for_prompt(examples: List[Dict[str, str]]) -> str:
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"""Format examples into prompt text"""
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if not examples:
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return ""
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formatted = "\n**Examples of this task type:**\n\n"
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for i, example in enumerate(examples, 1):
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formatted += f"Example {i}:\n"
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formatted += f"- Input: {example.get('input', 'N/A')}\n"
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formatted += f"- Approach: {example.get('approach', 'N/A')}\n"
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formatted += f"- Output structure: {example.get('output_structure', 'N/A')}\n\n"
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return formatted
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class RoleBasedPrompting:
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"""Role-based prompting for expertise-specific responses"""
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ROLES = {
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TaskType.ANALYSIS: {
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"role": "Expert Systems Analyst",
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"expertise": "Systems performance, architecture, and optimization",
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"constraints": "Provide quantifiable metrics and data-driven insights"
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},
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TaskType.DEBUGGING: {
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"role": "Expert Debugger",
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"expertise": "Root cause analysis, system behavior, and edge cases",
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"constraints": "Always consider concurrency, timing, and resource issues"
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},
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TaskType.IMPLEMENTATION: {
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"role": "Senior Software Engineer",
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"expertise": "Production-quality code, maintainability, and scalability",
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"constraints": "Write defensive code with error handling and clear design"
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},
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TaskType.SECURITY: {
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"role": "Security Researcher",
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"expertise": "Threat modeling, vulnerability analysis, and secure design",
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"constraints": "Assume adversarial input and verify all assumptions"
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},
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TaskType.RESEARCH: {
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"role": "Research Scientist",
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"expertise": "Literature review, systematic analysis, and knowledge synthesis",
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"constraints": "Cite sources and distinguish between established facts and speculation"
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},
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TaskType.PLANNING: {
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"role": "Project Architect",
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"expertise": "System design, risk assessment, and strategic planning",
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"constraints": "Consider dependencies, timeline, and team constraints"
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},
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TaskType.REVIEW: {
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"role": "Code Reviewer",
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"expertise": "Code quality, best practices, and maintainability",
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"constraints": "Focus on correctness, readability, and adherence to standards"
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},
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TaskType.OPTIMIZATION: {
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"role": "Performance Engineer",
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"expertise": "Performance bottlenecks, optimization techniques, and profiling",
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"constraints": "Measure before and after, prioritize high-impact improvements"
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}
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}
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@staticmethod
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def get_role_prompt(task_type: TaskType) -> str:
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"""Generate role-based system prompt"""
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role_info = RoleBasedPrompting.ROLES.get(
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task_type,
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RoleBasedPrompting.ROLES[TaskType.ANALYSIS] # Default role
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)
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return f"""You are a {role_info['role']} with expertise in {role_info['expertise']}.
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Your responsibilities:
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- Provide expert-level analysis and solutions
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- Apply industry best practices consistently
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- Question assumptions and verify conclusions
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- Explain your reasoning clearly
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Key constraint: {role_info['constraints']}
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Maintain this expertise level throughout your response."""
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class ContextHierarchy:
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"""Manages hierarchical context with priority-based injection"""
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def __init__(self):
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self.context_levels = {
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"critical": [], # Must always include
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"high": [], # Include unless very constrained
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"medium": [], # Include if space allows
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"low": [], # Include only with extra space
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}
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def add_context(self, level: str, context: str) -> None:
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"""Add context at specified priority level"""
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if level in self.context_levels:
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self.context_levels[level].append(context)
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def build_hierarchical_context(self, max_tokens: int = 2000) -> str:
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"""Build context respecting hierarchy and token budget"""
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context_str = ""
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token_count = 0
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target_tokens = int(max_tokens * 0.8) # Leave room for task
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# Always include critical
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for item in self.context_levels["critical"]:
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context_str += item + "\n\n"
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token_count += len(item.split())
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# Include high priority
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for item in self.context_levels["high"]:
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item_tokens = len(item.split())
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if token_count + item_tokens < target_tokens:
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context_str += item + "\n\n"
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token_count += item_tokens
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# Include medium if space permits
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for item in self.context_levels["medium"]:
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item_tokens = len(item.split())
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if token_count + item_tokens < target_tokens:
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context_str += item + "\n\n"
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token_count += item_tokens
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return context_str.strip()
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class TaskSpecificPatterns:
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"""Task-specific prompt patterns optimized for different domains"""
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@staticmethod
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def get_analysis_pattern(topic: str, focus_areas: List[str], depth: str = "comprehensive") -> str:
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"""Optimized pattern for analysis tasks"""
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return f"""# Analysis Task: {topic}
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## Objective
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Provide a {depth} analysis focusing on:
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{chr(10).join(f'- {area}' for area in focus_areas)}
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## Analysis Framework
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1. **Current State**: Describe what exists now
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2. **Key Metrics**: Quantify important aspects
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3. **Issues/Gaps**: Identify problems and gaps
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4. **Root Causes**: Explain why issues exist
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5. **Opportunities**: What could improve
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6. **Risk Assessment**: Potential downsides
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7. **Recommendations**: Specific, actionable next steps
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## Output Requirements
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- Use concrete data and examples
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- Prioritize findings by impact
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- Distinguish facts from interpretations
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- Provide confidence levels
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- Include supporting evidence"""
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@staticmethod
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def get_debugging_pattern(symptom: str, affected_component: str, severity: str = "high") -> str:
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"""Optimized pattern for debugging tasks"""
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return f"""# Debugging Task: {affected_component}
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## Symptom
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{symptom}
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## Severity: {severity}
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## Systematic Debugging Approach
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1. **Understand the Failure**: What goes wrong? When? Under what conditions?
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2. **Boundary Testing**: What works? What doesn't? Where's the boundary?
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3. **Hypothesis Formation**: What could cause this?
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4. **Evidence Gathering**: What would confirm/refute each hypothesis?
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5. **Root Cause Identification**: Which hypothesis is correct?
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6. **Solution Verification**: Test the fix thoroughly
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7. **Prevention**: How to prevent recurrence?
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## Investigation Priorities
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- Reproducibility: Can we reliably trigger the issue?
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- Isolation: What's the minimal failing case?
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- Impact Scope: What systems are affected?
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- Concurrency: Are timing/ordering factors involved?
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## Output Requirements
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- Root cause with high confidence
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- Minimal reproducible test case
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- Proposed fix with rationale
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- Verification strategy
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- Regression prevention measures"""
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@staticmethod
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def get_implementation_pattern(feature: str, requirements: List[str],
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constraints: List[str] = None) -> str:
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"""Optimized pattern for implementation tasks"""
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constraints = constraints or []
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return f"""# Implementation Task: {feature}
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## Requirements
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{chr(10).join(f'- {req}' for req in requirements)}
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{f"## Constraints{chr(10)}{chr(10).join(f'- {c}' for c in constraints)}" if constraints else ""}
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## Implementation Strategy
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1. **Design Phase**: Architecture, interfaces, design patterns
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2. **Implementation Phase**: Code, error handling, documentation
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3. **Testing Phase**: Unit, integration, edge case testing
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4. **Integration Phase**: How it fits with existing code
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5. **Deployment Phase**: Rollout strategy and monitoring
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## Code Quality Requirements
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- Error handling for all failure modes
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- Clear, self-documenting code
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- No external dependencies without justification
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||||
- Performance within acceptable bounds
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- Security reviewed for input validation
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## Testing Requirements
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- Unit test coverage for core logic
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- Edge case and error path testing
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- Integration tests with dependent systems
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- Performance/load testing if applicable
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## Output Deliverables
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1. Detailed implementation plan
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2. Complete code implementation
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3. Comprehensive test suite
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4. Documentation updates
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5. Deployment considerations and rollout plan"""
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@staticmethod
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def get_planning_pattern(objective: str, scope: str, constraints: List[str] = None) -> str:
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"""Optimized pattern for planning tasks"""
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constraints = constraints or []
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return f"""# Planning Task: {objective}
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## Scope
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||||
{scope}
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{f"## Constraints{chr(10)}{chr(10).join(f'- {c}' for c in constraints)}" if constraints else ""}
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## Planning Framework
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1. **Goal Clarity**: What are we trying to achieve?
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2. **Success Criteria**: How will we know we succeeded?
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3. **Resource Analysis**: What's needed (people, tools, time)?
|
||||
4. **Dependency Mapping**: What must happen in order?
|
||||
5. **Risk Assessment**: What could go wrong?
|
||||
6. **Contingency Planning**: How to handle risks?
|
||||
7. **Communication Plan**: How to keep stakeholders informed?
|
||||
|
||||
## Output Requirements
|
||||
- Clear, prioritized action items
|
||||
- Realistic milestones and dependencies
|
||||
- Risk assessment with mitigation strategies
|
||||
- Resource and timeline estimates
|
||||
- Success metrics and validation approach"""
|
||||
|
||||
|
||||
class PromptEngineer:
|
||||
"""Main orchestrator for advanced prompt engineering"""
|
||||
|
||||
def __init__(self):
|
||||
self.cot_engine = ChainOfThoughtEngine()
|
||||
self.few_shot = FewShotExampleBuilder()
|
||||
self.role_prompter = RoleBasedPrompting()
|
||||
self.patterns = TaskSpecificPatterns()
|
||||
|
||||
def engineer_prompt(self,
|
||||
task: str,
|
||||
task_type: TaskType,
|
||||
strategies: List[PromptStrategy] = None,
|
||||
context: Optional[PromptContext] = None,
|
||||
max_prompt_length: int = 3000) -> Tuple[str, Dict[str, Any]]:
|
||||
"""
|
||||
Engineer an optimized prompt combining multiple strategies.
|
||||
|
||||
Returns:
|
||||
(engineered_prompt, metadata)
|
||||
"""
|
||||
strategies = strategies or [
|
||||
PromptStrategy.SYSTEM_INSTRUCTION,
|
||||
PromptStrategy.CHAIN_OF_THOUGHT,
|
||||
PromptStrategy.FEW_SHOT,
|
||||
PromptStrategy.ROLE_BASED
|
||||
]
|
||||
|
||||
sections = []
|
||||
metadata = {
|
||||
"task_type": task_type.value,
|
||||
"strategies_used": [s.value for s in strategies],
|
||||
"estimated_tokens": 0
|
||||
}
|
||||
|
||||
# 1. System instruction
|
||||
if PromptStrategy.SYSTEM_INSTRUCTION in strategies:
|
||||
system_prompt = f"""You are an expert at solving {task_type.value} problems.
|
||||
Apply best practices, think step-by-step, and provide clear explanations."""
|
||||
sections.append(("## System Instructions", system_prompt))
|
||||
|
||||
# 2. Role-based prompt
|
||||
if PromptStrategy.ROLE_BASED in strategies:
|
||||
role_prompt = self.role_prompter.get_role_prompt(task_type)
|
||||
sections.append(("## Your Role & Expertise", role_prompt))
|
||||
|
||||
# 3. Task-specific pattern
|
||||
task_pattern = self._get_task_pattern(task, task_type)
|
||||
if task_pattern:
|
||||
sections.append(("## Task Structure", task_pattern))
|
||||
|
||||
# 4. Few-shot examples
|
||||
if PromptStrategy.FEW_SHOT in strategies:
|
||||
examples = self.few_shot.build_examples_for_task(task_type, num_examples=2)
|
||||
if examples:
|
||||
examples_text = self.few_shot.format_examples_for_prompt(examples)
|
||||
sections.append(("## Learning from Examples", examples_text))
|
||||
|
||||
# 5. Chain-of-thought prompting
|
||||
if PromptStrategy.CHAIN_OF_THOUGHT in strategies:
|
||||
complexity = context.complexity_level if context else 1
|
||||
cot_prompt = self.cot_engine.generate_cot_prompt(task, complexity)
|
||||
sections.append(("## Reasoning Process", cot_prompt))
|
||||
|
||||
# 6. The actual task
|
||||
sections.append(("## Your Task", f"Execute: {task}"))
|
||||
|
||||
# Build final prompt
|
||||
final_prompt = "\n\n".join(f"{title}\n{content}" for title, content in sections)
|
||||
|
||||
# Calculate metadata
|
||||
metadata["estimated_tokens"] = len(final_prompt.split())
|
||||
|
||||
return final_prompt, metadata
|
||||
|
||||
def _get_task_pattern(self, task: str, task_type: TaskType) -> Optional[str]:
|
||||
"""Get task-specific pattern based on task type"""
|
||||
patterns = {
|
||||
TaskType.ANALYSIS: lambda: self.patterns.get_analysis_pattern(
|
||||
"Analysis", ["Key findings", "Implications", "Recommendations"]
|
||||
),
|
||||
TaskType.DEBUGGING: lambda: self.patterns.get_debugging_pattern(
|
||||
task, "System", "High"
|
||||
),
|
||||
TaskType.IMPLEMENTATION: lambda: self.patterns.get_implementation_pattern(
|
||||
task, ["Functional requirements", "Non-functional requirements"]
|
||||
),
|
||||
TaskType.PLANNING: lambda: self.patterns.get_planning_pattern(
|
||||
task, "Comprehensive planning"
|
||||
),
|
||||
}
|
||||
|
||||
pattern_func = patterns.get(task_type)
|
||||
return pattern_func() if pattern_func else None
|
||||
|
||||
def suggest_strategies(self, task_type: TaskType, complexity: int = 1) -> List[PromptStrategy]:
|
||||
"""Suggest strategies based on task type and complexity"""
|
||||
base_strategies = [
|
||||
PromptStrategy.SYSTEM_INSTRUCTION,
|
||||
PromptStrategy.ROLE_BASED,
|
||||
]
|
||||
|
||||
if complexity >= 2:
|
||||
base_strategies.append(PromptStrategy.CHAIN_OF_THOUGHT)
|
||||
if complexity >= 3:
|
||||
base_strategies.append(PromptStrategy.FEW_SHOT)
|
||||
if complexity >= 4:
|
||||
base_strategies.append(PromptStrategy.TREE_OF_THOUGHT)
|
||||
|
||||
return base_strategies
|
||||
|
||||
|
||||
# Export for use in other modules
|
||||
__all__ = [
|
||||
'TaskType',
|
||||
'PromptStrategy',
|
||||
'PromptContext',
|
||||
'ChainOfThoughtEngine',
|
||||
'FewShotExampleBuilder',
|
||||
'RoleBasedPrompting',
|
||||
'ContextHierarchy',
|
||||
'TaskSpecificPatterns',
|
||||
'PromptEngineer',
|
||||
]
|
||||
Reference in New Issue
Block a user