- name
- meta-skill-optimizer
- description
- 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.
- tags
- version
- 1.0.0
- author
- chenq
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
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
- Record Everything: More data = better learning
- Categorize Failures: Understand failure types
- Update Regularly: Keep knowledge current
- Merge Insights: Combine learnings from multiple sources
Future Capabilities
- Cross-skill learning
- Automatic skill creation
- Self-debugging
- Automated testing
