meta-skill-optimizer
# 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
| 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:
```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
标签
skill
ai
