skill2gep

A generic distillation tool that converts any procedural Skill document (Cursor Skill, Claude / Anthropic Skill, or any SKILL.md / workflow-style markdown) into two kinds of GEP (Genome Evolution Protocol) assets:

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

Skills are written for humans; GEP assets are written for model execution. skill2gep compresses a Skill into high-control-density Gene assets without losing the decision surface an agent needs at runtime, and it produces Capsules only from real executions -- fabricating a Capsule from the document alone is rejected by the validator.

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.

Quick start

Inside any Cursor-compatible agent session:

Apply the skill2gep skill to ~/.cursor/skills/<some_other_skill>

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

See SKILL.md for the full 10-phase workflow and all the hard rules.

Installation

Copy this directory into your Cursor skills folder:

git clone https://github.com/EvoMap/skill2gep.git ~/.cursor/skills/skill2gep

The two validator scripts are zero-dependency Node.js programs. Requires Node >= 18.

Why GEP, why not just keep the Skill?

Skills are fine prose. At execution time, agents pay for every token they read. GEP splits the experience into two addressable asset types:

| Asset | Serves | Derived from | |---|---|---| | Gene | Model execution | An abstracted strategy template | | Capsule | Audit and reproducibility | Gene + at least one real run |

A Gene is typically 200-300 tokens; the same experience as a full Skill is often several times larger. The difference is not information loss, it is the removal of narrative scaffolding the model does not need at runtime. Measured density ratios are reported in arXiv:2604.15097; the qualitative design is documented in the GEP protocol wiki.

Skills and Genes are not replacements for each other. The Skill keeps serving human readers; the Gene serves model execution. They coexist.

Validators

# Check a Gene candidate file (JSON array of Gene objects)
node scripts/validate_gene.js candidates.json

# Check a Capsule file; pass the Gene pool to enable dangling-reference
# and validation-coverage checks
node scripts/validate_capsule.js capsules.json \
  --genes-jsonl path/to/genes.jsonl

Both validators enforce:

• Required fields, correct type tag, id regex, category enum
• At least one AVOID: entry in every Gene (unless explicitly waived) and a real execution_trace with integer exit codes in every Capsule
• No private-path literals leaked (/home/<user>, internal repo names, etc.)
• Token ceilings (Gene <= 500 estimated tokens)
• Capsule forgery guard: outcome.status=success with zero blast radius and no execution trace is rejected
• Capsule validation coverage: every validation command declared by the referenced Gene must appear in execution_trace with a numeric exit code

Publishing to EvoMap

Two surfaces, two different paths:

| Surface | What to publish | Endpoint | |---|---|---| | Skill Store (human-facing, downloadable) | The full SKILL.md plus bundled files | POST https://evomap.ai/a2a/skill/store/publish (spec) | | GEP Hub (agent-facing evolution memory) | Individual Genes and Capsules | @evomap/evolver runtime + GEP MCP tools (spec) |

See SKILL.md Phase 10 for full payload shapes, moderation rules, and authentication.

License

MIT. See LICENSE.

References

• Paper: Wang, Ren, Zhang, From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution. arXiv:2604.15097
• GEP Protocol
• Skill Store
• API Access
• Evolver runtime
• GEP SDK (JS / TS)