Meta Skill Optimizer

Self-improving AI skill optimizer that learns from feedback, auto-tunes prompts, optimizes tool usage patterns, and evolves based on success/failure analysis. Enables AI to continuously enhance its own capabilities.

Self-improving AI capability that enables continuous skill enhancement.

Features

1. Feedback Learning

• Success Analysis: Learn from successful executions
• Failure Analysis: Understand and prevent failures
• Pattern Recognition: Identify recurring patterns
• Preference Learning: Adapt to user preferences

2. Prompt Optimization

• Auto-Tuning: Optimize prompts based on outcomes
• Chain-of-Thought: Improve reasoning chains
• Example Selection: Dynamic few-shot example selection
• Style Adaptation: Match user communication style

3. Tool Usage Optimization

• Tool Selection: Choose best tools for tasks
• Parameter Tuning: Optimize tool parameters
• Workflow Patterns: Discover effective workflows
• Error Recovery: Learn from tool errors

4. Self-Diagnosis

• Capability Assessment: Know what it can/can't do
• Knowledge Gaps: Identify missing knowledge
• Confidence Calibration: Accurate confidence levels
• Limitation Awareness: Know when to ask for help

5. Continuous Evolution

• Version Tracking: Track skill improvements
• A/B Testing: Compare approach effectiveness
• Best Practices: Extract and codify learnings
• Knowledge Base: Build searchable knowledge

Installation

pip install numpy scipy json

Usage

Initialize Optimizer

from meta_optimizer import SkillOptimizer

optimizer = SkillOptimizer(
    skill_name="data_analysis",
    learning_rate=0.1
)

Record Execution Result

# Record successful execution
optimizer.record_success(
    task="analyze sales data",
    approach="used pandas groupby",
    context={"data_size": "10MB", "complexity": "high"},
    outcome={"success": True, "quality": "high"}
)

# Record failure
optimizer.record_failure(
    task="predict stock price",
    approach="used linear regression",
    error="insufficient features",
    lesson="need more technical indicators"
)

Get Optimized Approach

# Get best approach for task
best_approach = optimizer.get_best_approach(
    task_type="data_analysis",
    context={"data_size": "1GB"}
)

print(best_approach)
# {'method': 'chunked_processing', 'tools': ['pandas', 'dask']}

Optimize Prompt

# Optimize prompt based on results
optimized_prompt = optimizer.optimize_prompt(
    original_prompt="Analyze this data",
    outcome="too vague",
    feedback="be more specific about analysis type"
)

print(optimized_prompt)
# "Analyze this time-series data using trend detection and seasonality analysis"

API Reference

Feedback Learning

Method	Description
record_success(...)	Record successful execution
record_failure(...)	Record failed execution
get_insights()	Get learned insights

Prompt Optimization

Method	Description
optimize_prompt(...)	Optimize prompt based on feedback
generate_examples(...)	Generate few-shot examples
adapt_style(...)	Adapt to user style

Tool Optimization

Method	Description
suggest_tools(...)	Suggest best tools
optimize_params(...)	Optimize tool parameters
discover_workflow(...)	Discover effective workflows

Self-Diagnosis

Method	Description
assess_capability(...)	Assess capability for task
identify_gaps()	Identify knowledge gaps
calibrate_confidence()	Calibrate confidence levels

Evolution

Method	Description
track_improvement()	Track improvement over time
export_knowledge()	Export learned knowledge
merge_experiences()	Merge from other optimizers

How It Works

1. Feedback Loop

Task → Execution → Result → Feedback → Learning → Improvement

2. Pattern Discovery

Multiple Executions → Pattern Mining → Best Practices → Codification

3. Continuous Learning

New Task → Similar Past Tasks → Learned Lessons → Optimized Approach

Use Cases

• Prompt Engineering: Continuously improve prompts
• Tool Selection: Better tool recommendations
• Error Prevention: Learn from past mistakes
• User Adaptation: Match user preferences
• Capability Growth: Expand what AI can do

Knowledge Base

The optimizer builds a knowledge base:

{
  "patterns": {
    "data_analysis": {
      "small_data": "pandas sufficient",
      "large_data": "use dask or chunking",
      "time_series": "check stationarity first"
    }
  },
  "prompts": {
    "effective": ["specific", "contextual", "actionable"],
    "ineffective": ["vague", "ambiguous", "overly broad"]
  },
  "tools": {
    "coding": ["cursor", "claude-code"],
    "research": ["tavily", "browser"]
  }
}

Integration

With OpenClaw

# Auto-record all executions
@hookimpl
def after_execution(result, context):
    optimizer.record_execution(context, result)

With Skills

# Optimize skill behavior
skill = MySkill()
optimized_skill = optimizer.optimize_skill(skill)

Best Practices

1. Record Everything: More data = better learning
2. Categorize Failures: Understand failure types
3. Update Regularly: Keep knowledge current
4. Merge Insights: Combine learnings from multiple sources

Future Capabilities

• Cross-skill learning
• Automatic skill creation
• Self-debugging
• Automated testing