菜单
Preserves positive or negative ML experiment evidence, reconciles report discrepancies, records the canonical decision and limitations, and prepares a concise handoff. Use when stopping an experiment, rejecting a prompt/model, or freezing a result before the next iteration.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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/bastos/skills --skill close-out-ml-experiment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
| name | close-out-ml-experiment |
| description | Preserves positive or negative ML experiment evidence, reconciles report discrepancies, records the canonical decision and limitations, and prepares a concise handoff. Use when stopping an experiment, rejecting a prompt/model, or freezing a result before the next iteration. |
End the experiment without erasing what it taught.
Stop active inference or training when requested, but preserve completed outputs. Do not regenerate, relabel, overwrite, move, or reinterpret raw evidence. Hash baseline and final artifacts and verify expected files before editing summaries.
Inventory:
Recalculate inexpensive totals from per-case artifacts. If a report omits a category or case, preserve its measured values and add a clearly named correction or addendum. Explain the discrepancy; do not rewrite history or rerun inference to make totals agree.
State:
Do not claim model, product, strategic, human, device, energy, or production acceptance from narrower evidence.
Write a concise reproducible report beside the experiment evidence. If the project uses an issue tracker, add one high-signal comment with decision, metrics, artifact locations/hashes, limitations, and next step. Keep broader qualification issues open when only one subexperiment ended. Commit only explicitly authorized, scoped source or documentation changes; never commit credentials, datasets, weights, checkpoints, adapters, or unrelated files.
Writes and revises clear, precise, accessible technical content for any medium. Use for manuals, wikis, README files, developer and user documentation, tutorials, references, specifications, RFCs, feature requests, issues, commit messages, code comments, review comments, pull request messages, release notes, runbooks, decision records, status updates, email, Slack, and other workplace or agent-facing communication.
Audits and designs lean, coherent context across system prompts, persistent instructions, repository guidance, skills, tool descriptions, memory, and references. Use for context engineering; creating, reviewing, simplifying, or reorganizing agent context; resolving conflicting, duplicated, bloated, stale, or misplaced guidance; designing progressive disclosure; or deciding where shared information should live.
Orchestrates and ships a milestone, release, issue set, or multi-workstream engineering goal through one manager and bounded parallel workers. Use when the user asks to manage a milestone, build a dependency graph, execute independent issues in parallel, coordinate threads, sessions, subagents, or worktrees, recover a stopped delivery, or invokes /gogopowermanager.