Create short commit messages using Conventional Commits, a format that labels the type of change. Emphasize intent, keep the subject concise, and include explanatory text when breaking changes, security fixes, data migrations, or reversions require context.
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Write a Conventional Commits message compressed to intent only. Use for "write a commit", "commit message", /commit or /caveman-commit.
安装前可以查看交给 AI 代理的指令内容。
Write commit messages terse and exact. Conventional Commits format. No fluff. Why over what.
Subject line:
<type>(<scope>): <imperative summary> — <scope> optionalfeat, fix, refactor, perf, docs, test, chore, build, ci, style, revertBody (only if needed):
- not *Closes #42, Refs #17What NEVER goes in:
Assisted-by/AI-attribution trailer, then add it as a trailerDiff: new endpoint for user profile with body explaining the why
feat(api): add GET /users/:id/profile
Mobile client needs profile data without the full user payload
to reduce LTE bandwidth on cold-launch screens.
Closes #128
Diff: breaking API change
feat(api)!: rename /v1/orders to /v1/checkout
BREAKING CHANGE: clients on /v1/orders must migrate to /v1/checkout
before 2026-06-01. Old route returns 410 after that date.
Always include body for: breaking changes, security fixes, data migrations, anything reverting a prior commit. Never compress these into subject-only — future debuggers need the context.
Only generates the commit message. Does not run git commit, does not stage files, does not amend. Output the message as a code block ready to paste. "stop caveman-commit" or "normal mode": revert to verbose commit style.
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Delegate code lookup, small edits, and diff reviews to three specialized subagents. Their compact, structured results help conserve the main agent's context, with guidance on when to delegate and when to keep work in the main conversation.
中文简介正在准备中。当前显示英语简介。
Make agent replies shorter and easier to scan by putting answers first and removing filler. Preserves technical details, exact commands, numbers, and essential conditions while keeping the user's chosen language.
中文简介正在准备中。当前显示英语简介。
Compress memory files and task lists into shorter wording to reduce input tokens. Keeps code and document structure intact, saves a readable backup separately, and sends the text to the configured model provider.
中文简介正在准备中。当前显示英语简介。
Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.
中文简介正在准备中。当前显示英语简介。
Review Caveman Cloud spending, latency, errors, and potential improvements using current reports and traces, or individual execution records. Produce findings tied to time windows and trace IDs, while keeping measured costs, estimated opportunities, and verified savings separate.
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Locate relevant code in an unfamiliar repository or after a direct search fails. This read-only explorer returns a short list of file paths, verified line ranges, and relevance notes for another agent to use.
中文简介正在准备中。当前显示英语简介。