Meta Skill Optimizer#

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#

bash

pip install numpy scipy json

Usage#

Initialize Optimizer#

python

from meta_optimizer import SkillOptimizer

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

Record Execution Result#

python

# 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#

python

# 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#

python

# 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#

MethodDescriptionrecord_success(...)Record successful executionrecord_failure(...)Record failed executionget_insights()Get learned insights

Prompt Optimization#

MethodDescriptionoptimize_prompt(...)Optimize prompt based on feedbackgenerate_examples(...)Generate few-shot examplesadapt_style(...)Adapt to user style

Tool Optimization#

MethodDescriptionsuggest_tools(...)Suggest best toolsoptimize_params(...)Optimize tool parametersdiscover_workflow(...)Discover effective workflows

Self-Diagnosis#

MethodDescriptionassess_capability(...)Assess capability for taskidentify_gaps()Identify knowledge gapscalibrate_confidence()Calibrate confidence levels

Evolution#

MethodDescriptiontrack_improvement()Track improvement over timeexport_knowledge()Export learned knowledgemerge_experiences()Merge from other optimizers

How It Works#

1. Feedback Loop#

text

Task → Execution → Result → Feedback → Learning → Improvement

2. Pattern Discovery#

text

Multiple Executions → Pattern Mining → Best Practices → Codification

3. Continuous Learning#

text

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:

json

{
  "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#

python

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

With Skills#

python

# 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