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skill-release-gate 精选

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

在 GitHub 打开

安装方式

cp -r phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/outputs/skill-release-gate ~/.claude/skills/skill-release-gate

把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/

整仓一次性获取:git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git

依赖与权限

  • 无需 API Key
  • 无需联网
  • 依赖外部命令:python3
  • 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。

SKILL.md 正文

来源 rohitg00/ai-engineering-from-scratch@56c886c · 许可 MIT · 内容未修改

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.

  1. Resolve TARGET_ROOT from the original workspace working directory and

resolve the user-supplied candidate as an absolute TARGET_BUNDLE.

  1. Read references/eval-contract.md from SKILL_ROOT.
  2. Inspect the positive and near-miss trigger cases in

evals/cases.json under TARGET_BUNDLE.

  1. Inspect the shared baseline and with-skill assertions in

evals/artifacts.json under TARGET_BUNDLE.

  1. Inspect the explicit script and safety results in

evals/evidence.json under TARGET_BUNDLE.

  1. Inspect the declared runtime capabilities in

assets/hosts.json under TARGET_BUNDLE and verify the target file hashes

against its assets/manifest.json.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

质量与来源

所属仓库
rohitg00/ai-engineering-from-scratch
仓库星标
6.8 万
最近更新
昨天
采集时间
今天
commit
56c886c8821f
内容指纹
sha256:b57d0f111224c29
文件路径
phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/outputs/skill-release-gate/SKILL.md

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