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skill-release-gate

Evaluate an Agent Skill bundle for structural integrity, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity, and target-host portability before release.

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tessl review fix ./phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/outputs/skill-release-gate/SKILL.md
SKILL.md
Quality
Evals
Security

Skill release gate

Use this skill before publishing or distributing an Agent Skill directory bundle.

Workflow

  1. Resolve SKILL_ROOT to the absolute directory containing this installed SKILL.md. Do not assume the process cwd is the installed bundle.
  2. Resolve TARGET_ROOT from the original workspace working directory and resolve the user-supplied candidate as an absolute TARGET_BUNDLE.
  3. Read references/eval-contract.md from SKILL_ROOT.
  4. Inspect the positive and near-miss trigger cases in evals/cases.json under TARGET_BUNDLE.
  5. Inspect the shared baseline and with-skill assertions in evals/artifacts.json under TARGET_BUNDLE.
  6. Inspect the explicit script and safety results in evals/evidence.json under TARGET_BUNDLE.
  7. Inspect the declared runtime capabilities in assets/hosts.json under TARGET_BUNDLE and verify the target file hashes against its assets/manifest.json.
  8. For production, replace deterministic predictions, artifacts, evidence, and host capabilities with captured results; set all four captured modes; and bind every raw trigger observation, both artifacts, the complete evidence set, and the non-empty host matrix to non-empty sources and matching SHA-256 provenance digests. These local checks can set localEvidenceReady, but locally recomputable hashes do not prove capture.
  9. Obtain an external JSON attestation whose evidenceRoot matches the report, plus the SHA-256 of its exact bytes from a separate trusted policy or release channel. The attestation must be a regular file outside the target bundle.
  10. Before execution, show the exact resolved argv. The installed evaluator is scripts/evaluate_skill.py under SKILL_ROOT. For the shipped lesson fixture, build argv from python3, that absolute evaluator path, --fixture-demo, and the absolute TARGET_BUNDLE. For production, use the same installed script with --attestation, --trusted-attestation-sha256, and the absolute TARGET_BUNDLE, without --fixture-demo.
  11. Return checksPassed, fixturePassed, localEvidenceReady, trustAnchorValid, productionReady, and passed with the evidence root, evaluation modes, failed checks, precision, recall, every raw trigger observation, per-case repeated-run rates, artifact comparison, script and safety evidence, installed-tree verification, and portability matrix. Include the resolved script path, resolved target path, cwd, exact argv, and exit code. Mark unavailable observations unverified.

Output contract

Return the complete JSON evaluation report. Preserve every layer-specific check and its evidence so a passing aggregate cannot hide a routing, artifact, script, safety, installed-tree, or portability failure. fixturePassed reports a successful teaching fixture. localEvidenceReady reports only local digest integrity. passed is true only when productionReady also has a valid out-of-bundle trust anchor.

Failure behavior

If configuration is invalid, provenance is absent or mismatched, the trusted attestation is missing or invalid, a file hash differs, a required capability is absent, or any production gate fails, stop with a nonzero result and report the failed layer. The explicit --fixture-demo path may exit successfully only when fixturePassed is true, and it never makes a release claim. Never publish, install elsewhere, repair evidence, create the trust decision, or weaken a threshold automatically.

Do not publish a bundle merely because SKILL.md parses or one positive prompt activates. Do not label a package portable when a target drops required companion files or ignores required runtime extensions.

Repository
rohitg00/ai-engineering-from-scratch
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