Find recurring agent mistakes in a repository and prevent them through architecture, types, automated checks, or tests. Verify each safeguard against a real past mistake, and record how each rule is enforced in the agent instructions.
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Find the mistakes agents keep repeating in this repo and make each one impossible. Try architecture first, then types, then a lint whose error names the fix, then a test, and write docs last. Prove each check fails on a real past mistake. Repeat this each time the operator corrects you. Use for /correct.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
The operator keeps correcting agents in this repo for the same mistakes. Change the repo so the next agent can't make them.
Assume every contributor is an agent that sees only the files it opened, copies the nearest example, and takes the shortest path that compiles. Design the repo so a change that looks right from one file is right for the whole repo.
First, read recent commits, reverts, review comments, agent instruction files, and comments that explain workarounds. Group the mistakes into classes. A class counts once it has happened twice.
Then fix the most frequent classes now, one commit each. Prove each new check fails on a real past mistake. Run the same command locally and in CI. Exceptions go on the offending line with a reason, an expiry date, and a human's approval.
Last, keep a table in the agent instruction file that pairs each rule with what enforces it. When the operator corrects you, fix the mistake and add the rule. If the rule was already there and nothing enforces it, that's a repeat, so fix it at the highest level in the same change. Drop a rule once its mistake can't happen.
Reply: each class with its evidence, the level you picked, and why a higher level didn't work.
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Plan types, function inputs and outputs, and module boundaries before implementation. Compare structurally different designs from multiple AI models, document the chosen approach, and guide coding while tracking changes to the agreed design.
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Generate several candidate solutions in parallel, compare them against concrete criteria, and choose a base. Combine useful parts from other candidates, then verify the final artifact and document why each major choice was made.
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Turn recurring work habits and communication preferences into a personal agent skill. It combines evidence from workspace conversations with direct questions, then drafts or updates instructions for how agents should work with you.
中文简介正在准备中。当前显示英语简介。
Monitor an open pull request, fix failing automated checks and merge conflicts, and address straightforward review feedback. Recheck after updates, report the resulting status, and hand decisions requiring design judgment back to the user.
中文简介正在准备中。当前显示英语简介。
Checks whether performance measurements support a speedup, slowdown, or technology choice. It examines bottlenecks, production settings, errors, repeatability, and completed work, then guides reporting with run counts, ranges, and a clear verdict.
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Investigate what a code change could break beyond the edited files. Trace downstream effects, identify the key assumption behind its safety, and test that assumption with real code while clearly reporting unresolved risks.
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