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.
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/
https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/56c886c8821fc488b6f9608895c983d18f0d1ea3/phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/outputs/skill-release-gate/SKILL.md下载后另存为 ~/.claude/skills/skill-release-gate/SKILL.md。链接锁定采集时的 commit,内容与本站展示一致。
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
Skill release gate
Use this skill before publishing or distributing an Agent Skill directory bundle.
Workflow
- Resolve
SKILL_ROOTto the absolute directory containing this installed
SKILL.md. Do not assume the process cwd is the installed bundle.
- Resolve
TARGET_ROOTfrom the original workspace working directory and
resolve the user-supplied candidate as an absolute TARGET_BUNDLE.
- Read
references/eval-contract.mdfromSKILL_ROOT. - Inspect the positive and near-miss trigger cases in
evals/cases.json under TARGET_BUNDLE.
- Inspect the shared baseline and with-skill assertions in
evals/artifacts.json under TARGET_BUNDLE.
- Inspect the explicit script and safety results in
evals/evidence.json under TARGET_BUNDLE.
- Inspect the declared runtime capabilities in
assets/hosts.json under TARGET_BUNDLE and verify the target file hashes
against its assets/manifest.json.
- 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.
- Obtain an external JSON attestation whose
evidenceRootmatches 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.
- 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.
- 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