agentleFS
runxhqrunxhq/runx/skills/adopt-skill/SKILL.md

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

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
  1. Very specific:

    Use this for borrowed, pinned source.

    line 14
  2. Very specific:

    Only a passing native report is assembled into binding.json.

    line 32
  3. Very specific:

    Read upstream_skill, the bounded upstream SKILL.md supplied in prepared context.

    line 66

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

  1. Supply a local SKILL.md, pinned GitHub metadata, and registry metadata. Native fs.read contains 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.
  2. 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.
  3. Overlay forwards the unchanged upstream SKILL.md and the authored profile as one in-memory candidate. Native runx.skill.validate is the sole parser: it establishes the skill identity, validates the exact bytes, and runs the safe harness with isolated receipts and no operator credentials.
  4. Only a passing native report is assembled into binding.json. Native data.digest binds 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.

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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.

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