meta-prompting

name	meta-prompting
description	Recursive prompt improvement with quality-driven iteration for Claude Code. Use when you need systematic, measurable improvement of LLM outputs through complexity routing, context extraction, and iterative refinement.

Meta-Prompting Framework

Recursive prompt improvement through quality-driven iteration with automatic complexity routing.

When to Use This Skill

• Task requires multiple refinements for quality
• Quality is critical (production code, important deliverables)
• Want systematic, measurable improvement
• First attempt was insufficient
• Building something complex that needs iterative approach
• Need to extract patterns and learnings from intermediate outputs

Core Components

1. Complexity Analysis

Determine optimal strategy by analyzing task complexity (0.0-1.0):

Level	Score	Characteristics	Strategy
SIMPLE	< 0.3	Single-step, clear I/O	direct_execution
MEDIUM	0.3-0.7	Multi-step, design decisions	multi_approach_synthesis
COMPLEX	> 0.7	Architectural, trade-offs	autonomous_evolution

2. Context Extraction

Extract learnings from each iteration:

• Patterns identified
• Constraints discovered
• What worked well
• What needs improvement

3. Quality Assessment

Score outputs (0.0-1.0) on:

• Correctness
• Completeness
• Error handling
• Documentation
• Test coverage

4. Iteration Loop

Task → Analyze Complexity → Select Strategy → Generate
         ↑                                        ↓
         └── Extract Context ← Assess Quality ←──┘
                                   (quality < threshold?)

Meta-Prompting Strategies

Simple Tasks (direct_execution)

You are {skill}.

Task: {task}

Execute with clear, step-by-step reasoning:
1. Understand the requirements
2. Implement the solution
3. Verify correctness

Provide complete, working code.

Medium Tasks (multi_approach_synthesis)

You are {skill} using meta-cognitive strategies.

Task: {task}

Approach:
1. Generate 2-3 different approaches
2. Evaluate strengths and weaknesses of each
3. Choose the optimal approach with justification
4. Implement the chosen solution
5. Include edge case handling and tests

{previous_context}

Complex Tasks (autonomous_evolution)

You are {skill} performing autonomous problem evolution.

Task: {task}

Strategy:
1. Generate 3+ architectural hypotheses
2. For each hypothesis, identify:
   - Strengths and use cases
   - Weaknesses and failure modes
   - Key tradeoffs
3. Test hypotheses against constraints
4. Synthesize optimal solution from best elements
5. Document decision rationale

{previous_context}

Previous iteration learnings:
{extracted_patterns}
{improvements_needed}

Usage

Via Slash Command

/meta-prompt "Write function to validate email addresses"
/meta-prompt "Create priority queue" --threshold=0.95
/meta-prompt "Design rate limiter" --iterations=5 --skill=architect

Via Python Engine

from meta_prompting_engine.llm_clients.claude import ClaudeClient
from meta_prompting_engine.core import MetaPromptingEngine

llm = ClaudeClient(api_key="...")
engine = MetaPromptingEngine(llm)

result = engine.execute_with_meta_prompting(
    skill="python-programmer",
    task="Create a function to validate email addresses",
    max_iterations=3,
    quality_threshold=0.90
)

print(f"Quality: {result.quality_score}")
print(f"Iterations: {result.iterations}")
print(result.output)

Configuration

Default settings:

meta_prompting:
  max_iterations: 3
  quality_threshold: 0.90
  auto_stop: true
  complexity_thresholds:
    simple: 0.3
    medium: 0.7

Real Test Results

Palindrome Checker: 2 iterations, 4,316 tokens, +21% quality improvement Find Maximum: 2 iterations, 3,998 tokens, +20% quality improvement

When NOT to Use

• Simple one-off tasks (just ask directly)
• Exploratory brainstorming
• Time-critical work (adds latency)
• Task is ambiguous (clarify first with /speckit.clarify)

Sub-Skills

Located in ~/.claude/skills/meta-prompting/:

• analyze-complexity/ - Determine optimal strategy
• assess-quality/ - Score output quality
• extract-context/ - Extract patterns from outputs
• meta-prompt-iterate/ - Full recursive workflow

Source

Framework: ~/.local/meta-prompting-framework Engine: ~/.local/meta-prompting-framework/meta_prompting_engine/core.py