skill-evolution
Skill0 starsChanged 3 months agoMITView on GitHub
Evidence-gated revision of another skill — hard-refuses unless the freshly derived gate authorizes a review, and lands only a blind-validated candidate.
12 other projects use a version of this.
Adopted by
Most recently taken up by
- Sirius-Ji/dsh-disciplinary-skills-for-greenhandstheir own version, first seen Nov 8, 2026
- coco-research/cocotheir own version, first seen Nov 1, 2026
- sqrDAO/sqrSKILLStheir own version, first seen Oct 19, 2026
- AidALL/ghost-alicetheir own version, first seen Oct 11, 2026
- sakuruamayday-ops/enterprise-project-application-assistanttheir own version, first seen Oct 9, 2026
- Heyvhuang/ship-fastertheir own version, first seen Oct 8, 2026
Used with
Projects with it also use skill-creator.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
SentrySkillskill-creator
Create new agent skills following the Agent Skills specification. Use when asked to "create a skill", "add a new skill", "write a skill", "make a skill", "build a skill", or scaffold a new skill with SKILL.md. Guides through requirements, writing, registration, and verification.
Also in 3 projects that use this
Other versions12 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 12
- NVIDIA/NeMo-Agent-Toolkit★ 2.7ksame name
- coco-research/coco★ 797same name
- Heyvhuang/ship-faster★ 337same name
- AidALL/ghost-alice★ 15same name
- juemin4-source/writing-pro-max★ 7same name
- Carlo1911/skill-evolution★ 6same name
- echoleesong/claude-skills-plugin★ 4same name
- sakuruamayday-ops/enterprise-project-application-assistant★ 2same name
- JameMy0001/antigravity-skills-hub★ 1same name
- Sirius-Ji/dsh-disciplinary-skills-for-greenhands★ 1same name
2 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository28 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- codebase-designSkill
- code-reviewSkill
- commission-method-gap-researchSkill
- diagnosing-bugsSkill
- domain-modelingSkill
- grillingSkill
- grill-with-docsSkill
- handoffSkill
- implementSkill
- improve-codebase-architectureSkill
- method-gap-research-statusSkill
- prototypeSkill
- refactor-skillSkill
- research-briefSkill
- researchSkill
- setup-matt-pocock-skillsSkill
- skill-evidence-captureSkill
- skill-evolution-statusSkill
- tddSkill
- teachSkill
- to-issuesSkill
- to-prdSkill
- triageSkill
- wait-whatSkill
- wayfinderSkill
- writing-great-skillsSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 8 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 47
Read references/authorized-review.md and execute it end-to-end, starting by claiming the review.
“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill Evolution
Revise a target skill only when accumulated prospective evidence has opened a mechanical authorization gate, review confirms the target owns the recurring failure, and fresh blind comparative trials prove an isolated candidate materially better without unacceptable regression. This skill is deliberately unavailable for preventive checking, aesthetic cleanup, reassurance, or reaction to a newly noticed imperfection: it is event-authorized, never schedule-authorized, and a hard refusal on a closed gate is a successful, complete run. Invoking it is authorization to land a validated candidate — no second "implement now" prompt — and equally authorization for nothing at all when the gate or validation says no.
Arguments: the path of the target skill directory (required). Everything else is derived mechanically from its evidence store, reports/skill-evidence/<skill-key>/.
Hard boundaries
- Only the freshly derived gate authorizes work. No rationale substitutes: not reassurance, a noticed possible improvement, staleness, size or ugliness, one non-severe incident, many clean uses, a new model or tool version without observed incompatibility, old
skill-audithistory, a prior premature refusal, explicit user insistence, or an "obvious tiny" change. - Until the preflight passes: never read the target semantically, inspect it or repository conventions for improvement opportunities, propose or suggest anything, append any incident, or edit anything. Repeated invocations do not advance the gate, create evidence, or shorten the cooldown.
- Fresh session means a different top-level session. Never satisfy the cooldown through a child agent, subagent, continuation, or context reset inside the same top-level session.
- Self-targeting is forbidden. If this skill's own evidence gate ever opens, route the bounded evidence packet to an independent skill-authoring workflow; judging the live skill via a copied candidate of itself is still self-review.
- Evidence is immutable: every event write goes through the compiled command; never hand-edit
events.jsonlorgate-status.json, and never delete or rewrite earlier events.
Workflow
1. Run the mechanical preflight
From the repository root:
cargo run --locked -p skill-evidence -- skills evolution preflight \
--target <skill-path> --recorded-at <RFC3339-clock> \
--now-epoch-milliseconds <clock-ms> --session-id <top-level-session-id> \
--lock-owner <caller-owned-lock-id>One run is the whole check: the compiled command validates event-stream integrity, regenerates gate-status.json, hashes the live target, and evaluates every authorization term — authorized workflow, eligible or quarantined-eligible state, hash match, no active review, fresh-session/cooldown, non-self target. Clock, session, and lock identities are explicit caller inputs; the command never reads the ambient clock or generates an identity.
Done when the compiled command printed either a refusal block or an authorized evidence packet.
2. On refusal: relay and stop
Relay the compiled command's refusal verbatim — gate state, failed condition, terminal outcome — and end the run with no analysis, no suggestions, and no softening ("we could still look at…"). Safe refusal is success.
Done when the refusal was relayed unchanged and nothing else was done to or about the target.
3. On authorization: run the review
Read references/authorized-review.md and execute it end-to-end, starting by claiming the review.
Done when exactly one close disposition was recorded through the compiled command, the review report names its truthful terminal outcome under reports/skill-evidence/<skill-key>/reviews/, and the user-facing completion states whether the live skill changed.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

