基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/arthur-debert/release --skill teach命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Drive a PR to ready-for-human-merge in any repo managed by `arthur-debert/release`. The loop is state-machine-driven: `release-core pr status` reports one lifecycle state plus the single next action; do that action, re-read, repeat — open as a draft, request the required reviews, wait in-turn, triage and resolve threads — and `release-core pr wait` flips draft→ready itself once the engine reaches READY, then stop (the human merges). Use when opening a PR, checking where a PR stands, waiting on or triaging review feedback, or driving a PR toward merge-readiness. Triggered by: `gh pr create`, 'check PR status', 'where does this PR stand', requesting a review, or processing review comments.
Escalate infrastructure friction (workflow failures, ruleset misbehaviors, broken policy templates, helper-script bugs) from a consumer repo to the `arthur-debert/release` repo. Files a GitHub issue at release with auto-collected reproduction context (repo, branch, PR, recent workflow run, workaround applied). Searches recent release issues first and comments on a matching one rather than filing a duplicate — the comment count is the signal of recurrence. Use ONLY for infra issues that the consumer cannot fix in place; do not use for code-quality nits, project-specific test failures, or anything fixable inside the consumer repo. Triggered by: 'this workflow fails in a way I can't fix here', 'the release-loop policy is misbehaving', 'an infra script errors', 'the cloud env is missing something it should provide'.
Measure whether automated PR reviews (Copilot / Gemini / CodeRabbit) actually earn their cost on a given repo, so review policy is set by evidence instead of gut. Runs a repo-agnostic pipeline over the repo's own merged-PR history: a cheap mechanical tier (extract → enrich → finalize → summarize, zero agents) answers latency / volume / convergence, and an optional LLM-judge tier (a `release` Workflow fan-out + aggregate → converge) scores each bot comment for value, hallucination, novelty and re-request convergence. Emits a machine-readable summary.json. Use when: 'audit our PR reviews', 'are the review bots worth it', 'should we keep CodeRabbit / Gemini', 'is re-request-on-push paying off', 'one reviewer or several?'.
| argument-hint | What would you like to learn about? |
| description | Teach the user a new skill or concept, within this workspace. |
| disable-model-invocation | true |
| metadata | {"github-path":"skills/in-progress/teach","github-ref":"refs/heads/main","github-repo":"https://github.com/mattpocock/skills","github-tree-sha":"2649a18218c39d578c4a5df7becd0fc96d356fbc"} |
| name | teach |
The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.
Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:
MISSION.md: A document capturing the reason the user is interested in the topic. This should be used to ground all teaching. Use the format in MISSION-FORMAT.md.GLOSSARY.md: A glossary of terminology related to the topic. All workspace files should adhere to this terminology. Use the format in GLOSSARY-FORMAT.md.RESOURCES.md: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in RESOURCES-FORMAT.md../learning-records/*.md: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled 0001-<dash-case-name>.md, where the number increments each time. Use the format in LEARNING-RECORD-FORMAT.md.To learn at a deep level, the user needs three things:
Before the RESOURCES.md is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.
Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.
Every teaching session should be tied into the mission - the reason that the user is interested in learning about the topic.
If the user is unclear about the mission, or the MISSION.md is not populated, your first job should be to question the user on why they want to learn this.
Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Exercises will feel too abstract. You will have no way of judging what the user should do next.
The user should always feel as if they are being challenged 'just enough'. The scope of the topic being taught should feel extremely tight, should be directly tied into their mission.
The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:
learning-recordsA user may tell you that they already know about that topic. If so, record it in their learning-records.
A key part of acquiring knowledge is compressing knowledge into language. Once a term is known and understood, it can be used and combined in new ways to make more complex terms easier to understand.
Building the glossary should be done once you feel confident that the user understands the term. Glossaries should use a strict format, and use as concise a definition as possible.
Knowledge and skills usually need to be taught as a 1-2 punch. You teach the knowledge first, then get the user to practice the skills via exercises.
Knowledge should first be gathered from trusted resources, then taught to the user via HTML explainers. These explainers should be beautiful, adhere to the glossary, and be saved to the local file system where they can be reviewed later.
You should make opening the HTML explainer as easy as possible for the user, ideally with a CLI command they can run.
Once the user has read the knowledge, allow them to ask questions about it. Answer their questions directly, and amend the explainer if needed (or produce another one).
At this point, you can amend the glossary if it appears clear they understand a term.
Skills should be taught through interactive exercises. There are several tools at your disposal:
Each exercise should be based on a feedback loop, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately.
Wisdom comes from true real-world interaction - testing your skills outside the learning environment.
When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a community.
A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.
You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.