adopt-skill
Skill97 starsChanged 32 days agoApache-2.0View on GitHub
Adopt a pinned third-party SKILL.md as an exact native Runx binding bundle with deterministic source digests, a bounded execution profile, isolated harness proof, and no publication claim. Use skill-lab for first-party Runx skills.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- amcclosky/skillstheir own version, first seen Apr 10, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- amcclosky/skills★ 1same name
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 repository94 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- llms.txtllms.txt
- runxSkill
- agencySkill
- answer-from-docsSkill
- attention-reviewSkill
- audit-receiptSkill
- brand-voiceSkill
- business-opsSkill
- chargeSkill
- chief-of-staffSkill
- cold-outreachSkill
- content-pipelineSkill
- contract-drafterSkill
- conversation-replySkill
- conversation-reviewSkill
- crm-cleanupSkill
- cve-auditSkill
- data-storeSkill
- data-subject-requestSkill
- deep-researchSkill
- diagnose-skill-runSkill
- dispute-respondSkill
- ecosystem-briefSkill
- extractSkill
- ghostwriteSkill
- github-pr-commentSkill
- github-pr-reviewSkill
- github-syncSkill
- google-analyticsSkill
- google-calendarSkill
- google-search-consoleSkill
- governed-outboundSkill
- helpdeskSkill
- incident-commanderSkill
- issue-intakeSkill
- issue-to-prSkill
- issue-triageSkill
- knowledge-routerSkill
- lead-enrichmentSkill
- lead-qualifySkill
- least-privilegeSkill
- ledgerSkill
- list-hygiene-judgeSkill
- marketplace-invokeSkill
- meeting-followupSkill
- mock-chargeSkill
- mock-paySkill
- mock-refundSkill
- moltbookSkill
- n8n-handoffSkill
- nitrosendSkill
- nws-weather-forecastSkill
- open-meteo-weather-forecastSkill
- operator-inboxSkill
- ops-deskSkill
- organic-growthSkill
- policy-authorSkill
- postmortem-makerSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 14
Use this for borrowed, pinned source.
- Very specific: line 32
Only a passing native report is assembled into binding.json.
- Very specific: line 66
Read upstream_skill, the bounded upstream SKILL.md supplied in prepared context.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Adopt Skill
Adopt a third-party skill without pretending that upstream prose is already a
Runx package. Adopt Skill binds one immutable upstream SKILL.md to a least-authority
execution profile under bindings/<owner>/<skill>/. The upstream manual remains
unchanged and readable; the binding adds only the executable contract and
provenance needed by Runx.
Use this for borrowed, pinned source. Use skill-lab when Runx owns the skill,
and use review-skill when the immediate job is assessment rather than binding.
Procedure
- Supply a local
SKILL.md, pinned GitHub metadata, and registry metadata. Nativefs.readcontains and digests the file; the Overlay domain module validates frontmatter, commit and blob pins, URLs, and the source-of-truth assertion, including the Git blob SHA that ordinary SHA-256 cannot express. - The builder reads the already bounded upstream manual from prepared context
and authors one exact
run.yaml: one runner with one agent step, exact inputs and outputs, least scopes, declared environment, credentials, allowed tools, and at least two mocked harness cases. Empty tool access is valid when the task needs no tools; it never means allow-all. - Overlay forwards the unchanged upstream
SKILL.mdand the authored profile as one in-memory candidate. Nativerunx.skill.validateis the sole parser: it establishes the skill identity, validates the exact bytes, and runs the safe harness with isolated receipts and no operator credentials. - Only a passing native report is assembled into
binding.json. Nativedata.digestbinds the exact profile and binding documents returned in the bundle. Apply them through the owning repository's normal authoring lane; publication and materialization remain separate operations.
The skill does not fetch an unpinned registry ref, edit the upstream skill, write repository files, publish a package, or claim provider verification. Missing source evidence, digest drift, invalid profile shape, or failed harness proof stops before a bundle is released. A failed run is safe to retry after correcting the source or profile because no workspace mutation has occurred.
Output
binding_bundle contains decision, binding_path, observed source evidence, exact files, native inspection and harness results, rationale, blockers, and a success_checkpoint. Only decision: ready contains files.
Inputs are skill_path, upstream, registry, optional objective, scope_intent, bounds, tags, and publication.
Caller-supplied bounds
scope_intent informs the profile draft; bounds constrains it. When the
caller supplies bounds (allowed_output_prefix, max_acts, or both), Overlay
validates the shape deterministically before any source read, the builder must
echo the exact bounds it applied as bounds_applied, and deterministic code
compares that echo field by field against the caller's declaration. A dropped,
widened, or invented bound rejects the candidate before native validation runs.
The accepted bounds are recorded in binding.json next to the harness evidence,
so the registry artifact carries the attenuation the caller actually declared,
not a prose intention. When no bounds are supplied, binding.json records
bounds: null and a draft that claims bounds anyway is rejected.
Agent task contracts
overlay-binding-profile
Read upstream_skill, the bounded upstream SKILL.md supplied in prepared
context. Return profile_draft with decision, profile_document,
rationale, and blockers. When the bounds input is present, also return
bounds_applied echoing every supplied bound exactly, and shape the profile so
the package honors them: keep write scopes inside allowed_output_prefix and
carry max_acts as a capped runner input where the wrapped skill takes
repeated acts. Never invent bounds the caller did not supply.
profile_document is the complete exact run.yaml
text. It must define one runner with one agent step, exact inputs and outputs,
declared environment and credentials, an explicit top-level tools: list naming
only the catalog tools the agent may call, and at least two mocked harness
cases. The cases must exercise the useful path and
a missing-input, refusal, or boundary path. Do not add shell, provider, network,
or filesystem execution stages. Do not author binding provenance or
publication claims; native validation owns syntax and identity, while Overlay
owns deterministic provenance assembly after that proof passes.
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.

