cot-lint
检查您的代码仓库中是否泄露了思维链——AI 助手在文档、注释和提交相关文本中留下的会话记录残留。随附可安装的 DeepSeek Harness 插件 cot-trim 技能。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add cot-lint@0.2.1Cot Lint 以 cot-lint 发布，当前版本为 0.2.1。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
English | 中文
Lint your repo for chain-of-thought leakage — the session-transcript residue AI assistants leave in docs, comments, and JSDoc.
Your coding agent writes great code and leaks its thinking everywhere around it:
- // This PR adds a retry loop (decision 7) so the diff stays reviewable.
- // The manager used to serialize writes itself; it no longer does after v1.
- // The cast is safe — it simply narrows the union. Probably fine for now.
+ // Retries transient provider failures up to 3 times with jittered backoff.
+ // The shared coordinator serializes writes per session.
+ // The cast narrows a union already validated at the loader boundary.
None of the left column is wrong about the code. It is wrong about its reader: it argues with a reviewer who has left, cites a design session nobody can open, and narrates a change instead of stating behavior.
Could a reader at HEAD — with no access to any session transcript, PR thread, or uncommitted draft — resolve every reference and verify every claim?
If no, that passage is chain-of-thought leakage. cot-lint finds it.
DeepSeek Harness — install the cot-trim fixing skill as a plugin:
dsh plugin add cot-lint
Any repo or CI — zero dependencies, Node ≥ 20:
npx cot-lint # scan the repo (Markdown and prose files)
npx cot-lint --json # machine-readable findings for CI or agents
npx cot-lint --ext ts,py # also scan source files line-by-line
npx cot-lint --hidden # descend into dot-directories such as .agents/
Exit codes: 0 clean · 1 findings · 2 usage error — drop it straight into CI.
| Class | Example |
|---|---|
| dead design-session citation | (decision 7), design §4.7, phase tokens W3/T4, 设计稿 |
| stack/PR vantage | "this PR adds…", "a later PR in this stack" |
| change narration / version stamps | "used to", "no longer", "the old X", "the v1 refactor", "today", 旧版/不再 |
| review choreography | "Rejected in review:", "the reviewer confirmed", 上一轮评审 |
| reviewer-addressed justification | "the cast is safe — it simply…" |
| control-flow narration | "first we X, then we Y", "as you can see" |
| hedge / planning residue | "probably fine for now", "should be enough" |
| authoring-language slip | untranslated working-language fragments in the other language |
English and Chinese batteries are both built in.
The keep-rules are half the tool. A zero-treatment linter that deletes RFC 9110 §10.1.5, a load-bearing TODO(alice):, or "the old connection drains before the new one accepts" (runtime lifecycle, not change history) does more damage than the leakage. So cot-lint mechanically exempts:
TODO/FIXME/XXX deferrals,§-references on lines that cite an external standard such as an RFC,cot-lint-ignore suppression — keep the reason next to it.Batteries over-match by design. Every finding is a candidate, not a verdict; the keep-rules and rewrite method decide what survives.
The repo ships cot-trim, an agent skill that pairs with the CLI: it runs cot-lint --json, judges every hit against the one test, enumerates the passage's propositions before deleting anything, and fixes owner-first (generated files via their source, model-visible strings via their owning snapshot).
Install it where your agent looks for skills:
dsh plugin add cot-lint (or github:YuanyuanMa03/cot-lint) — the cot-trim skill loads through the plugin's skill provider.skills/cot-trim/ into your skills directory (~/.claude/skills/, .agents/skills/, or wherever your agent looks).Style-slop detectors flag prose that sounds like AI (word choices, em-dash habits). cot-lint flags prose whose vantage is the authoring session — references and narration that only make sense if you were there. Human-written docs can leak (copy-pasted PR descriptions do); AI-written docs can be clean. Different failure class, different tool.
The taxonomy, keep-rules, and battery approach are distilled from the engineering standards of DeepSeek Harness (MIT) — specifically its prose-hygiene practice for agent-written repositories — generalized here to work with any repo and any coding agent. See their CONTRIBUTING.md for the project's stance on community ecosystem work.