Initial commit

skills/fine-tune/README.md

@@ -0,0 +1,83 @@
+# LangGraph Fine-Tune Skill
+
+A comprehensive skill for iteratively optimizing prompts and processing logic in LangGraph applications based on evaluation criteria.
+
+## Overview
+
+The fine-tune skill helps you improve the performance of existing LangGraph applications through systematic prompt optimization without modifying the graph structure (nodes, edges configuration).
+
+## Key Features
+
+- **Iterative Optimization**: Data-driven improvement cycles with measurable results
+- **Graph Structure Preservation**: Only optimize prompts and node logic, not the graph architecture
+- **Statistical Evaluation**: Multiple runs with statistical analysis for reliable results
+- **MCP Integration**: Leverages Serena MCP for codebase analysis and target identification
+
+## When to Use
+
+- LLM output quality needs improvement
+- Response latency is too high
+- Cost optimization is required
+- Error rates need reduction
+- Prompt engineering improvements are expected to help
+
+## 4-Phase Workflow
+
+### Phase 1: Preparation and Analysis
+
+Understand optimization targets and current state.
+
+- Load objectives from `.langgraph-master/fine-tune.md`
+- Identify optimization targets using Serena MCP
+- Create prioritized optimization target list
+
+### Phase 2: Baseline Evaluation
+
+Quantitatively measure current performance.
+
+- Prepare evaluation environment (test cases, scripts)
+- Measure baseline (3-5 runs recommended)
+- Analyze results and identify problems
+
+### Phase 3: Iterative Improvement
+
+Data-driven incremental improvement cycle.
+
+- Prioritize improvement areas by impact
+- Implement prompt optimizations
+- Re-evaluate under same conditions
+- Compare results and decide next steps
+- Repeat until goals are achieved
+
+### Phase 4: Completion and Documentation
+
+Record achievements and provide recommendations.
+
+- Create final evaluation report
+- Commit code changes
+- Update documentation
+
+## Key Optimization Techniques
+
+| Technique                         | Expected Impact             |
+| --------------------------------- | --------------------------- |
+| Few-Shot Examples                 | Accuracy +10-20%            |
+| Structured Output Format          | Parsing errors -90%         |
+| Temperature/Max Tokens Adjustment | Cost -20-40%                |
+| Model Selection Optimization      | Cost -40-60%                |
+| Prompt Caching                    | Cost -50-90% (on cache hit) |
+
+## Best Practices
+
+1. **Start Small**: Begin with the most impactful node
+2. **Measurement-Driven**: Always quantify before and after improvements
+3. **Incremental Changes**: Validate one change at a time
+4. **Document Everything**: Record reasons and results for each change
+5. **Iterate**: Continue improving until goals are achieved
+
+## Important Constraints
+
+- **Preserve Graph Structure**: Do not add/remove nodes or edges
+- **Maintain Data Flow**: Do not change data flow between nodes
+- **Keep State Schema**: Maintain the existing state schema
+- **Evaluation Consistency**: Use same test cases and metrics throughout