cut-mistakes

# Cut Mistakes & Repeats (Pipeline Agent 2) Second step of the automated edit, run after **cut-silences**. It removes the things a human editor cuts on a second pass: stutters ("the the", "I- I-"), immediately repeated words, false starts (an abandoned phrase that restarts), and retakes (a line re-recorded — keep the clean take, drop the botched one). Deciding what counts as a mistake is **judgment, not a formula** — emphatic repetition ("never, never") and rhetorical doubling ("the first piece is, is this…") look identical to a stutter mechanically. So this agent is **review-gated by default**: it surfaces candidates with full context; the agent (and the user) decide; only approved cuts are rendered. ## When to use - "cut the mistakes", "remove stutters / repeats", "clean up the flubs", "keep the best take", "cut false starts" - As the second stage of the master edit workflow, on cut-silences' output. ## The three-part flow ### 1. Find candidates (mechanical) ```bash node .agents/skills/cut-mistakes/scripts/find-cut-candidates.mjs \ <silence-transcript.json> --out-dir video-projects/<slug>/assets ``` Input is normally Agent 1's `<stem>.silence-transcript.json`, so cuts land on the already-silenced timeline. Writes: - `<stem>.cut-candidates.json` — structured candidates (type, confidence, proposed `cut` range, `removes`/`keeps` text, context, recommendation) - `<stem>.cut-candidates.md` — readable proposal Candidate types: `stutter` (immediate word repeat), `retake` (duplicate/near-duplicate segment via Jaccard similarity), `false_start` (short abandoned phrase that restarts). ### 2. Review (the gate — REQUIRED) Read each candidate **in context** and decide keep-or-cut. The mechanical detector cannot tell intentional emphasis/rhetoric from a real flub, so do not trust `recommend` blindly — read `removes` + `context`. Common false positives: emphatic repetition ("it's never, never hands-off"), copula-then-question ("the piece is, is this…"), listing. Present the candidates to the user with a recommendation per item; collect approvals. Write the approved cuts to a file: ```json { "cuts": [ { "start": 100.93, "end": 101.06, "reason": "I- false start" }, ... ] } ``` (`start`/`end` in seconds on the input transcript/video timeline. You can widen a candidate's range — e.g. for a retake, cut from the botched take's start to the clean restart.) ### 3. Apply approved cuts ```bash node .agents/skills/cut-mistakes/scripts/apply-cuts.mjs \ <silence-transcript.json> --cuts approved-cuts.json \ --video video-projects/<slug>/assets/edited-silenced.mp4 \ --output video-projects/<slug>/assets/edited-clean.mp4 --apply ``` Writes the EDL, a re-timed `<stem>.mistakes-transcript.json` (feeds the motion-graphics agent + `validate-beat-sync.mjs`), a decisions log, and — with `--apply` — the cut video (ffmpeg trim+concat, A/V in sync). Drop `--apply` for a dry run. ## Reviewing the result Use the shared review tool to eyeball the cuts and spot-check boundaries: ```bash node scripts/build-edl-review.mjs <stem>.mistakes-edl.json \ --original video-projects/<slug>/assets/edited-silenced.mp4 \ --edited video-projects/<slug>/assets/edited-clean.mp4 \ --output video-projects/<slug>/assets/mistakes-review.html npx serve . -p 8080 -n ``` ## Notes - A very clean delivery may yield few or zero real cuts — that's a valid outcome; don't cut natural speech to hit a quota. - Cuts between two spoken words are hard joins. They're usually clean for stutters/false starts; for tighter audio a 20-30ms fade can be added later. - Hand the `mistakes-transcript.json` + the clean video to **Agent 3 (motion graphics / tiered cards)**.