Lint your repo for chain-of-thought leakage — the session-transcript residue AI assistants leave in docs and comments.
Pending human review — the facts below come from the source repository.
Lint your repo for chain-of-thought leakage — the session-transcript residue AI assistants leave in docs and comments.
Users who want this capability in DSH; check the source and docs before installing.
dsh plugin --profile web add github:YuanyuanMa03/cot-lint No supported platforms were declared.
diff - // 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. ## The one test > 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. ## Quick start DeepSeek Harness — install the cot-trim fixing skill as a plugin: sh dsh plugin add cot-lint Any repo or CI — zero dependencies, Node ≥ 20: sh 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. ## What it detects | 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. ## What it deliberately does not flag 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: - issue references and marked TODO/FIXME/XXX deferrals, - §-references on lines that cite an external standard such as an RFC, - lines carrying a 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. ## Fixing, not just finding 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: - DeepSeek Harness: dsh plugin add cot-lint (or github:YuanyuanMa03/cot-lint) — the cot-trim skill loads through the plugin's skill provider. - Claude Code / generic agents: copy skills/cot-trim/ into your skills directory (~/.claude/skills/, .agents/skills/, or wherever your agent looks). ## How this differs from "AI slop" style linters 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. ## Origin 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. ## License MITNot by stars — picked by hand, one at a time.
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