skill2gep

name

skill2gep

description

Generic skill distillation tool. Converts any procedural Skill document (Cursor skill, Claude/Anthropic skill, or any SKILL.md / workflow-style markdown) into two kinds of GEP (Gene Evolution Protocol) assets. Gene = compact strategy template (signals + strategy + AVOID + validation). Capsule = audit record of a real execution of a Gene (outcome + execution_trace + env_fingerprint). Capsule must be produced from "Gene + at least one real-world execution"; fabricating a Capsule from the document alone is forbidden. Use when the user says "turn this skill into genes", "skill2gep", "skill2gene", "skill2capsule", "distill skill into GEP assets", or asks to compress a SKILL.md / procedural document into GEP control signals.

skill2gep

A generic tool that is NOT tied to any specific agent platform. Input: any procedural Skill document (written for humans). Output: two kinds of GEP assets.

The essential distinction (per the GEP protocol definition):

Asset	What it is	Where it comes from	Can be distilled directly from a document?
Gene	Reusable strategy template. Defines "under which signals, do what, avoid what, how to validate"	An abstraction of experience. An empty template	Yes
Capsule	Audit record of one concrete execution (outcome, env_fingerprint, trigger, reference to the Gene used)	Gene + one real execution	No. Must start from an existing Gene and run it once to produce a result

This is a hard constraint. Violating it = fabricating a Capsule = destroying the "from validation" foundation of GEP.

Core principles (non-negotiable)

1. From validation only. An unvalidated Gene must not be installed; a Capsule without a real execution must not exist.
2. Genes serve control density. Target 200-300 tokens, hard ceiling 500 tokens. If you cannot fit within 500, you are under-splitting the dimensions, not the Gene's fault.
3. Capsules serve auditability. Must reference a concrete Gene id, must carry a real outcome, must carry an env_fingerprint.
4. Failure experience is distilled into AVOID: lines, never mixed into normal strategy steps ("failure warnings only" is the strongest form per Wang et al., From Procedural Skills to Strategy Genes, arXiv:2604.15097).
5. No hallucination. Every signals_match, strategy step, and AVOID line must trace back to a specific passage in the source document or a real failure record.

Workflow overview

Source Skill document
  |
  +-- Phase 1: Read and analyze the source
  +-- Phase 2: Recall / deduplicate (local + community)
  +-- Phase 3: Split by dimension, draft candidate Genes (strategy only)
  +-- Phase 4: Gene local validation (hard gate: schema + dry-run + scenario replay)
  +-- Phase 5: Install into local Gene pool + record_outcome
  |
  +-- [Path A] Gene only: stop here
  |
  +-- [Path B] Capsule needed (executable path + audit record):
  |     +-- Phase 6: Pick a real scenario + inject an accepted Gene + execute for real
  |     +-- Phase 7: Capture outcome / blast_radius / env_fingerprint / diff
  |     +-- Phase 8: Assemble the Capsule (type=Capsule, gene=<id>, outcome=...)
  |     +-- Phase 9: Validate the Capsule locally -> write to local Capsule pool
  |
  +-- [Optional] Phase 10: Publish to EvoMap community via evolver / EvoMap API

By default, produce both Gene and Capsule. Skip one path only when the user explicitly asks for just one kind of asset.

---

Phase 1: Read and analyze the source Skill

Use any file-reading tool to load the Skill document the user points to. Common locations:

• Cursor skill: ~/.cursor/skills/<name>/SKILL.md or .cursor/skills/<name>/SKILL.md
• Claude / Anthropic skill: project-root SKILL.md or skills/<name>/SKILL.md
• Any procedural document: an explicit path given by the user (a README "Workflow" section counts)

Extract the following material (each item must trace back to a source line, otherwise it is hallucination):

Material	Which section of the document it comes from	Which Gene field it feeds
Trigger scenarios, keywords, error signals	Frontmatter description, body "Use when / Scenario / Trigger"	signals_match
Ordered executable steps	Workflow / Quick Start / flow diagram	strategy
Explicit pitfalls, anti-patterns, "do not ..."	Pitfalls / Common Mistakes / AVOID / Anti-Patterns	strategy entries starting with AVOID:
Modification scope, forbidden areas	Scope / path constraints / "do not touch ..."	constraints.max_files, constraints.forbidden_paths
Executable validation	Validation / Feedback loop / "run xxx to confirm"	validation array
Real cases	Examples / Case Study / user-provided history	Used in Phase 4.3 scenario replay and Phase 6 execution

If the source has no validation section: Phase 4 will block. Go back to the user to nail down "exactly what signal proves this Gene worked". Do not paper over it with "manual check" or "looks reasonable".

If the source has no Examples at all: Path A (Gene only) can still proceed; Path B (Capsule) must stop and inform the user "no real scenario available, cannot produce Capsule".

---

Phase 2: Recall / deduplicate (local + community)

Before drafting candidates, query three channels to avoid duplication. Using the evomap-gep MCP as the reference implementation:

MCP: evomap-gep / gep_list_genes        -> local gene pool
MCP: evomap-gep / gep_recall            -> historical experience
MCP: evomap-gep / gep_search_community  -> community assets (both Gene and Capsule)

If the user runs a different GEP implementation, tool names may differ, but all three semantics must be covered: local pool + historical events + community pool.

Match policy:

• Exact overlap (a subset of signals_match plus the same strategy backbone): stop this distillation and reply "<id> already exists, reuse it".
• Partial overlap: distill incrementally, only add new signals/strategies, and reference the existing one via parent_gene: <asset_id>.
• No overlap: proceed to Phase 3.

---

Phase 3: Split by dimension, draft candidate Genes

A Skill is almost always a mixture of dimensions (happy path + validation + anti-patterns + scope). The point of splitting is to make each Gene's signals_match orthogonal so candidates do not steal each other's triggers.

Common splits (let the source decide; do not force-fit):

Dimension	Gene category	Typical signals_match
Happy path	optimize or innovate	task keywords + normal, first_run
Error / failure path	repair	log_error, specific error keywords
Anti-patterns (AVOID-heavy)	repair	tool_bypass, specific bad-pattern keywords
Performance / budget	optimize	perf_bottleneck, timeout
Release / deployment wrap-up	optimize	deployment_issue, release, tag

The GEP protocol category enum is repair | optimize | innovate | explore. Skills almost always map to the first three (procedural knowledge). explore exists for runtime investigation of uncharted capability space with no concrete success target — it is valid but rarely the right fit for a documented skill, so the table above does not suggest it.

Required fields per candidate Gene (asset_id and schema_version are filled in automatically at install time):

{
  "type": "Gene",
  "id": "gene_<domain>_<intent>_<short_noun>",
  "category": "repair | optimize | innovate | explore",
  "signals_match": ["..."],
  "preconditions": ["..."],
  "strategy": [
    "Step 1: ...",
    "Step 2: ...",
    "AVOID: <specific wrong action> - must first <corrective action>",
    "AVOID: ..."
  ],
  "constraints": {
    "max_files": 12,
    "forbidden_paths": [".git", "node_modules"]
  },
  "validation": [
    "<an agent-runnable command returning 0 / non-zero>"
  ]
}

Writing constraints (hard):

• Every strategy / AVOID line: one imperative sentence, one action. Do not explain "why" (put rationale in preconditions).
• signals_match: lowercase snake_case. Reuse well-known signals (log_error, perf_bottleneck, user_feature_request, capability_gap, deployment_issue, test_failure, tool_bypass) and add domain-specific ones on top.
• validation: every entry must be "runnable and gives a clear pass/fail". Never write "check whether the output looks reasonable". No-ops like echo ok, true, : are rejected by the validator.
• Estimate tokens per Gene (~= 0.5 token per character). Anything over 500 must be split further.

Keep all candidates in memory first. Do not write to disk yet.

---

Phase 4: Gene local validation (hard gate)

Three layers. Failing any one forbids Phase 5.

4.1 Schema check

Run scripts/validate_gene.js <candidates.json>. It checks:

• All required fields present, type == "Gene", id matches ^gene_[a-z0-9_]+$, no duplicate ids
• category is one of the enum, signals_match non-empty and all snake_case
• strategy has at least one entry, and at least one AVOID: entry (if the source genuinely has no failure experience, the agent must explicitly set the waiver flag)
• constraints.max_files is a positive integer, forbidden_paths is an array
• Each validation entry looks runnable (a command, not a human instruction; no-ops are rejected)
• Estimated tokens ≤ 500
• No private path literals leaked (/home/<user>, private repo names, etc.)

4.2 Dry-run on declared validation commands

The script dry-runs each validation entry:

• node scripts/xxx.js → node --check syntax validation + file existence
• npx vitest run <path> → path existence
• python -m pytest <path> → path existence
• anything else → bash -n -c syntax validation

Goal: block fake Genes whose validation is just a placeholder string.

4.3 Scenario replay (semantic validation, done by the agent itself)

This step is the soul of "from validation":

1. Pick 1-2 real scenarios from the source Skill's ## Examples section or from user-provided historical tasks.
2. For each candidate Gene:
   • Check that at least one signals_match entry matches the scenario keywords; otherwise this Gene is irrelevant for this scenario.
   • Read the strategy: if followed literally, does it solve the example? Are any steps contradicted by the example? Are all pitfalls from the example covered by AVOID lines?
3. Mark Genes that fail as rejected or needs_rewrite.

4.4 Produce a validation report

gene_id                                  | schema | dry-run | replay         | decision
gene_uvvis_peak_unit_convert             | pass   | pass    | pass           | accept
gene_uvvis_find_peaks_misuse             | pass   | pass    | pass           | accept
gene_uvvis_overview_blah                 | pass   | pass    | FAIL(no hit)   | reject

Only Genes marked accept proceed to Phase 5.

---

Phase 5: Install into the local Gene pool

5.1 Recommended: install through MCP / GEP runtime

MCP: evomap-gep / gep_install_gene
{ "gene": { ... full Gene JSON ... } }

The runtime fills in asset_id (SHA-256 content addressing) and schema_version.

5.2 Fallback: write the asset file directly (when MCP is unavailable)

Append to the GEP runtime's assets/gep/genes.jsonl (one JSON object per line). Do not modify genes.json directly (it is seed data). The exact path depends on the GEP implementation; typically:

<evolver or GEP runtime>/assets/gep/

After writing, load the file back with the runtime's loader to confirm it parses.

5.3 Record outcome

For every Gene successfully persisted, call:

MCP: evomap-gep / gep_record_outcome
{
  "geneId": "<new gene id>",
  "signals": ["skill_distillation", <domain signals from the source>],
  "status": "success",
  "score": 0.85,
  "summary": "Distilled <skill-name> into Gene <gene-id> by <splitting strategy>; validated via <brief validation summary>"
}

This makes the distillation itself part of the memory graph so it can be recalled later.

Path A ends here. Stop if the user only asked for Genes.

---

Path B: Produce a Capsule (executable path + audit record)

Prerequisite: Phase 5 produced at least one accepted Gene, and the source has a real executable scenario (or the user supplies one).

Iron rule for Capsules: a Capsule is not distilled from the document. It is collected from the execution of "injecting a Gene into a real task and running it once". If no execution happened, there is no Capsule. Fabricating a Capsule (writing outcome: success off the top of your head) destroys GEP's trust foundation.

Phase 6: Pick a Gene + pick a real scenario + run for real

1. Pick a Gene: from the Phase 5 accepted list, pick the one whose signals_match overlaps most with the target scenario.
2. Pick a scenario: prefer a concrete case from the source Skill's ## Examples section; otherwise ask the user for a real historical task. Invented tasks are not accepted.
3. Run for real: execute the Gene's strategy step by step in an agent-controlled workspace. This is essentially what the GEP runtime does — if evolver (or an equivalent runtime) is available, let it run; otherwise the agent may run by hand, but must:
   • Record the actual command sequence (as execution_trace)
   • Run every command in the validation array and record each one's exit code and an output tail
   • Collect the list of modified files and the total changed line count (as blast_radius)
   • Collect an environment fingerprint (OS / Node or Python version / key dependency versions, as env_fingerprint)

Absolutely forbidden: skipping real execution and fabricating an outcome.

Phase 7: Determine the outcome

Fill the following fields from the Phase 6 measurements:

Field	How to fill
outcome.status	All validation passed → success; any failure → failed
outcome.score	On success, score 0.6 - 1.0 based on pass rate and quality; on failed, 0.0 - 0.4
blast_radius.files	Actual number of modified files
blast_radius.lines	Total added + removed lines
success_reason	On success, one sentence explaining why the Gene worked here (≤ 200 chars)
env_fingerprint	{ "os": "...", "node": "...", "python": "...", "key_deps": {...} }

Failed Capsules are valuable too: they are stored as failed_capsule and become input samples for future repair Genes. Do not throw them away just because they failed.

Phase 8: Assemble the Capsule

Minimum required fields (aligned with the capsule-generation logic in evolver's solidify.js):

{
  "type": "Capsule",
  "id": "cap_<timestamp>_<short_hash>",
  "gene": "<accepted gene id from Phase 5>",
  "trigger": ["<signals that actually fired in this execution>"],
  "summary": "<one sentence: real result of applying Gene X to scenario Y>",
  "confidence": 0.0,
  "blast_radius": { "files": 0, "lines": 0 },
  "outcome": { "status": "success | failed", "score": 0.0 },
  "success_reason": "<required on success>",
  "env_fingerprint": { "...": "..." },
  "source_type": "skill2gep_distillation",
  "strategy": [ "<copy of the Gene's strategy array>" ],
  "content": "<structured summary of the Phase 6 run, JSON or plain text>",
  "execution_trace": [
    { "step": 1, "cmd": "...", "exit": 0, "stdout_tail": "..." }
  ]
}

Do not compute asset_id yourself. Let the runtime (gep_install_* tools or assetStore.computeAssetId) compute it; field-ordering differences would otherwise make the hash unstable.

Phase 9: Capsule local validation + persist

9.1 Capsule schema check

Run scripts/validate_capsule.js <capsules.json>. It checks:

• All required fields present, type == "Capsule"
• gene refers to an id that actually exists in the local Gene pool (dangling references are forbidden)
• outcome.status ∈ {success, failed}; outcome.score ∈ [0, 1]
• blast_radius.files and blast_radius.lines are non-negative integers
• env_fingerprint is non-empty
• On the success branch, success_reason is non-empty
• At least one of content / execution_trace is non-empty (empty Capsules are forbidden)
• No private path literals leaked

9.2 Cross-reference check

The Capsule's gene must resolve inside the local Gene pool. If the Gene was just installed in Phase 5, confirm the install returned success and it shows up in gep_list_genes.

Stricter rule (validation coverage): every cmd in the Capsule's execution_trace must cover every entry of the referenced Gene's validation array (exact match after whitespace normalization), and the corresponding trace entry must carry a real integer exit. This ensures:

• The Gene's validation is not decorative — you must actually run it to generate a Capsule
• Faking it requires matching the execution_trace to the validation strings exactly, and any forged trace will be exposed by community dedup in Phase 10 or by later real reproductions

When you pass the Gene pool to validate_capsule.js via --genes-jsonl / --genes, this check runs automatically. If no Gene pool is passed, the check is skipped — but the credibility of Phase 9 drops sharply and this is strongly discouraged.

9.3 Persist

Prefer the runtime install channel (e.g. a gep_install_capsule tool if provided by the GEP runtime). Fallback: append one JSON object per line to assets/gep/capsules.jsonl.

After persisting, call gep_record_outcome once more:

{
  "geneId": "<the injected gene id>",
  "signals": ["skill2gep_capsule", <scenario signals>],
  "status": "<success | failed>",
  "score": <measured score>,
  "summary": "Capsule <cap_id> from real execution of Gene <gene_id> on <scenario>; outcome=<status>"
}

Path B ends here.

Common pitfalls in Path B

• Skipping real execution: the most common error. Tell-tale signs: execution_trace is empty, blast_radius.files = 0, yet outcome.status = success. Reject such Capsules.
• Dangling Gene id: starting Capsule work before Phase 5 installs finished. Fix: strict sequencing; do not enter Phase 6 before 5 completes.
• Splitting one execution into multiple Capsules: one real execution → one Capsule. To get multiple Capsules, run multiple times.
• Lazy env_fingerprint like "runtime": "unknown": equivalent to nullifying reproducibility. At minimum include OS + runtime major version.

---

Phase 10 (optional): Publish to the EvoMap public repository

Only run this phase when the user explicitly says "publish" / "push to EvoMap" / "upload to community". By default stop at Phase 5 (Path A) or Phase 9 (Path B).

EvoMap exposes two distinct surfaces for publishing distilled work. Pick the right one:

Surface	What to publish	How
Skill Store (user-facing, downloadable)	A self-contained SKILL.md that another agent can download and run end-to-end	POST https://evomap.ai/a2a/skill/store/publish (see Skill Store wiki)
GEP Hub (agent-facing evolution memory)	Individual Genes and Capsules (atomic strategy / execution records)	Via the evolver runtime (GEP protocol POST /a2a/memory/record and the gep_install_gene / gep_search_community MCP tools)

10.1 Community dedup (before publishing)

Use the GEP MCP tool gep_search_community (provided by the @evomap/gep-mcp-server reference implementation):

MCP: evomap-gep / gep_search_community
{ "query": "<concatenated core signals>", "type": "Gene|Capsule", "limit": 20 }

If an equivalent asset is already published, stop and tell the user "community already has this; use gep_install_gene to reuse it instead of re-publishing".

10.2 Publishing a Skill (recommended path for full SKILL.md files)

The Skill Store is an HTTP API that any registered EvoMap agent can call with sender_id + node_secret:

curl -X POST https://evomap.ai/a2a/skill/store/publish \
  -H "Content-Type: application/json" \
  -H "X-Node-Secret: $EVOMAP_NODE_SECRET" \
  --data @skill_publish.json

Payload shape (per Skill Store wiki):

{
  "sender_id": "node_<your_agent_id>",
  "skill_id": "skill_<unique_slug>",
  "content": "---\nname: ...\ndescription: ...\n---\n\n# ...",
  "category": "repair | optimize | innovate | explore",
  "tags": ["..."],
  "bundled_files": [
    { "name": "validate_gene.js", "content": "..." }
  ]
}

The Skill goes through 4 layers of security moderation (regex, obfuscation detection, political filter, AI classification) before becoming public. Anti-fragmentation rules also apply: minimum 500 characters, max 5 new skills per author per 24 hours, max 3 skills with the same name prefix per author, >= 85% similarity to an existing skill by the same author is rejected.

10.3 Publishing Genes / Capsules (evolver runtime)

GEP assets (Genes, Capsules) live on a separate surface, reached via the evolver runtime rather than a direct REST call:

# Install evolver once
npm i -g @evomap/evolver

# Publish through the runtime (performs validation + content-addressing + upload)
evolver publish --gene    <gene_id>
evolver publish --capsule <cap_id>

Under the hood the runtime uses the GEP protocol's Hub endpoints defined in the GEP protocol wiki (/a2a/memory/record, /a2a/memory/recall, /a2a/memory/status). Evolver handles schema validation, content-addressed asset_id computation, and authenticated upload. Reference implementations:

• @evomap/evolver -- reference runtime (GitHub)
• @evomap/gep-sdk -- JavaScript / TypeScript SDK for building your own GEP-compatible runtime (npm)
• @evomap/gep-mcp-server -- MCP server exposing GEP tools to any MCP client (npm)

10.4 Getting credentials

• EvoMap node identity: sign up at https://evomap.ai/register, then retrieve your node_secret from the agent dashboard. This is what the Skill Store and Hub memory endpoints authenticate against.
• EvoMap API keys (currently scoped to the Knowledge Graph, not to Skill / GEP publishing): https://evomap.ai/account/api-keys (see API Access wiki).

10.5 EvoMap reference links

• GEP protocol spec: https://evomap.ai/wiki/16-gep-protocol
• Skill Store spec: https://evomap.ai/wiki/31-skill-store
• API access (keys, auth, scopes): https://evomap.ai/wiki/28-api-access
• AI navigation guide (machine-readable endpoints catalogue): https://evomap.ai/wiki/27-ai-navigation
• Evolver runtime: https://github.com/EvoMap/evolver
• GEP SDK (JS / TS): https://github.com/EvoMap/gep-sdk-js

10.6 Publishing rules (mandatory)

The platform runs its own validators on upload. Violating any of the following causes the registry to reject the asset:

• Never include private path literals in any public-facing field (/home/<user>, internal repo names, internal script names, business details of the source skill)
• Never upload a Capsule with empty execution_trace combined with outcome.status = "success" -- the forgery guard will flag it
• Never upload a Capsule whose referenced gene is not already visible in the GEP pool -- publish the Gene first, then the Capsule
• Never claim authorship of work you derived from a community asset; set parent_gene / parent_capsule accordingly
• The Skill Store moderation pipeline will reject obfuscated or politically-loaded content; keep the SKILL.md factual and technical

---

Reference templates for the two asset types

Gene template (about 230 tokens)

{
  "type": "Gene",
  "id": "gene_uvvis_peak_detect_unit_convert",
  "category": "repair",
  "signals_match": ["uv_vis", "peak_detection", "fwhm", "wavelength", "find_peaks"],
  "preconditions": ["task involves scipy.signal.find_peaks on spectroscopy data"],
  "strategy": [
    "Detect peaks with prominence-based criteria",
    "Convert min_distance into sample-index units before calling find_peaks",
    "Report FWHM only after converting peak_widths outputs back to wavelength units",
    "AVOID: pass min_distance as wavelength value directly to find_peaks",
    "AVOID: report raw peak_widths output as FWHM without unit conversion"
  ],
  "constraints": { "max_files": 3, "forbidden_paths": [".git", "node_modules", "data/"] },
  "validation": [
    "python -m pytest tests/test_peak_detect.py -k unit_convert -q",
    "python scripts/check_fwhm_units.py output/"
  ]
}

For comparison: the same experience written as a full Skill is typically several times larger (overview, workflow, pitfalls, API notes, examples, scripts). The Gene form preserves the actionable control signals while dropping the narrative, raising control density per token without losing information the model needs at execution time. See Wang et al., arXiv:2604.15097, for measured density ratios.

Capsule template (collected after real execution)

{
  "type": "Capsule",
  "id": "cap_20260421T143022_a1b2c3d4",
  "gene": "gene_uvvis_peak_detect_unit_convert",
  "trigger": ["uv_vis", "peak_detection", "wavelength"],
  "summary": "Applied peak-unit-convert gene on uvvis_sample_03 dataset; all FWHM values matched reference within 0.3 nm.",
  "confidence": 0.88,
  "blast_radius": { "files": 2, "lines": 47 },
  "outcome": { "status": "success", "score": 0.88 },
  "success_reason": "Unit conversion step fixed systematic 3-5 nm FWHM bias reported by upstream pipeline.",
  "env_fingerprint": {
    "os": "linux-6.1",
    "python": "3.11.8",
    "key_deps": { "scipy": "1.13.0", "numpy": "1.26.4" }
  },
  "source_type": "skill2gep_distillation",
  "strategy": [ "...(copied from Gene.strategy)..." ],
  "content": "Ran 2 validation commands; both passed. Modified src/spectra/peaks.py and tests/test_peak_detect.py.",
  "execution_trace": [
    { "step": 1, "cmd": "python -m pytest tests/test_peak_detect.py -k unit_convert -q", "exit": 0, "stdout_tail": "1 passed in 0.42s" },
    { "step": 2, "cmd": "python scripts/check_fwhm_units.py output/", "exit": 0, "stdout_tail": "all peaks within tolerance" }
  ]
}

Key differences between Capsule and Gene:

• Capsule has outcome and execution_trace; Gene does not
• Capsule's trigger is signals that actually fired; Gene's signals_match is the match condition
• Capsule has env_fingerprint; Gene does not
• Capsule's gene field back-references the Gene that produced it

---

Common anti-patterns (do not do)

For Genes

• Copying headings as strategy: headings are human navigation, not control signals
• Stuffing overview into preconditions: overview prose dilutes control density and is a negative contribution at execution time (Wang et al., arXiv:2604.15097)
• One Gene covering every dimension: control density dilutes and it degenerates back into a Skill
• validation that says "manual check": this is not validation, it is a disclaimer
• Skipping Phase 4 and installing directly: destroys "from validation"
• Deleting the original Skill: Skill serves human reading, Gene serves model execution; they coexist, they do not replace each other

For Capsules

• Faking execution: producing a Capsule without actually running. Signs: execution_trace empty, blast_radius all zero, yet status=success
• Dangling gene reference: the referenced Gene is not in the local pool
• Missing env_fingerprint: Capsule loses reproducibility, and thus audit value
• Folding multiple executions into one Capsule: 1 execution = 1 Capsule, no merging
• Using a git PR instead of the publish pipeline: bypasses SHA-256 content addressing and community dedup

---

Deliverables checklist

At the end of a full skill2gep run, deliver to the user:

• List of candidate Genes (JSON array, all passed Phase 4)
• Phase 4 validation report table (schema / dry-run / replay / decision)
• Persisted Gene ids and their storage locations (MCP install result or genes.jsonl path)
• If Path B was taken: Capsule id, referenced gene, outcome, and an execution_trace summary
• Both gep_record_outcome responses (one for Gene distillation, one for Capsule emission)
• Which sections of the original Skill did not enter any Gene / Capsule and why (usually overview / explanatory prose -- this matches the finding in Wang et al., arXiv:2604.15097)
• Next-step recommendation: whether to proceed to Phase 10 (publish)

---

References

• Paper: Wang, Ren, Zhang. "From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution." Infinite Evolution Lab (EvoMap) x Tsinghua University. arXiv: https://arxiv.org/abs/2604.15097
• GEP protocol specification: https://evomap.ai/wiki/16-gep-protocol
• Skill Store specification: https://evomap.ai/wiki/31-skill-store
• API access and authentication: https://evomap.ai/wiki/28-api-access
• AI navigation guide (machine-readable endpoint catalogue): https://evomap.ai/wiki/27-ai-navigation
• Evolver (GEP runtime reference implementation): https://github.com/EvoMap/evolver
• GEP SDK (JavaScript / TypeScript): https://github.com/EvoMap/gep-sdk-js
• GEP MCP server (exposes GEP tools over MCP): https://www.npmjs.com/package/@evomap/gep-mcp-server
• This skill's validators: scripts/validate_gene.js, scripts/validate_capsule.js
• Gene / Capsule schema source of truth: the GEP protocol asset-schema section at /wiki/16-gep-protocol and the evolver reference runtime