Evolve Skill From Traces
Skill Verified ActiveEvolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.
To automate the creation and refinement of SKILL.md files by learning from observed agent execution traces, ensuring that documented procedures accurately reflect effective agent behavior.
Features
- Trajectory collection from agent execution logs
- Parallel multi-agent patch proposal for error and success analysis
- Conflict-free consolidation of edits via prevalence-weighting
- Validation against held-out traces for skill accuracy
- Automatic registration or version-bumping of evolved skills
Use Cases
- Evolving existing SKILL.md files when observed agent behavior differs
- Generating new SKILL.md files from scratch using recorded expert agent demonstrations
- Improving skill robustness by analyzing failure modes from execution traces
- Consolidating conflicting improvements proposed by multiple agents
Non-Goals
- Manually writing SKILL.md files from scratch without trace data
- Directly executing agent code; focuses on skill file generation
- Replacing human oversight entirely; provides a framework for automated evolution
Practical Utility
- info:Usage examplesThe SKILL.md provides example bash commands for trace partitioning and validation, but lacks end-to-end examples for the entire pipeline.
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
VerifiedTrust Signals
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