skill2gep

A protocol adapter that turns a locally-executed Skill into GEP (Genome Evolution Protocol) assets:

• Gene – a compact strategy template (signals_match + strategy + AVOID: + validation)
• Capsule – an auditable record of one real execution of that Gene (outcome + execution_trace + env_fingerprint)

Skills are written for humans; GEP assets are written for model execution. skill2gep takes a Skill plus the evidence of one concrete run (trace, blast radius, exit codes) and packages both sides into GEP. Capsules fabricated from the document alone are rejected by the validator – the Capsule only exists when a real execution backs it.

Based on Wang, Ren, Zhang, From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution (Infinite Evolution Lab / EvoMap x Tsinghua University), arXiv:2604.15097.

Honest scope note

Read this before you assume the tool does more than it does.

1. This is a protocol adapter, not a magic distiller. Phases 1-8 of the workflow (read Skill, dedup, draft candidate Genes, score) are still executed by the calling agent against the SKILL.md text. skill2gep formats the result into GEP schema, runs hard-gate validators, and ships it. Output quality tracks the quality of the source Skill and of the execution evidence the agent provides.
2. The paper’s empirical scope is narrow. arXiv:2604.15097 validates Gene-as-control-interface vs. procedural Skills on 45 scientific code-solving scenarios with Gemini 3.1 Pro and Flash Lite. Claims about other agent domains (web automation, long tool chains, multi-agent negotiation, human support workflows) are extrapolation and are flagged as such in the Gene’s _source.claims_outside_scope: "assumption" field.
3. “Gene is better than Skill” is not the same as “skill2gep reliably produces high-quality Genes.” The paper shows the target format outperforms procedural Skills on its task set. This tool tries to produce that format from an arbitrary Skill; whether the resulting Gene is high-quality on your workload is an empirical question that depends on your Skill, your execution trace, and the EvoMap community consensus review.

When you publish to EvoMap, community validators score the asset. Low-quality Genes get rejected or down-weighted. Think of skill2gep as the submission pipeline, not the grader.

Quick start

Inside any Cursor-compatible agent session:

Apply the skill2gep skill to ~/.cursor/skills/

The agent will then:

1. Read and analyze the source Skill
2. Dedup against local + community Gene pools
3. Draft candidate Genes, split by dimension
4. Hard-gate validate each candidate (schema + dry-run + scenario replay)
5. Install accepted Genes into the local Gene pool
6. (Optional Path B) Run an accepted Gene on a real scenario and collect a Capsule
7. (Optional Phase 10) Publish the Skill, Gene, or Capsule to EvoMap