cut-mistakes
Agent 2 of the video editing pipeline. Finds and removes spoken mistakes — stutters, repeated words, false starts, and retakes (re-recorded lines) — from a talking-head recording. Use after cut-silences, when asked to cut mistakes, remove stutters / repeats / filler restarts, clean up flubs, or keep the best take. Works review-gated: it proposes every cut with context and a reason for approval, then renders only the approved cuts via ffmpeg. Requires a word-level transcript.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/nateherkai/herk-edit-kit --skill cut-mistakes- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads cut-mistakes automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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)
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, proposedcutrange,removes/keepstext, 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:
{ "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
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:
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).
FAQ
What does the cut-mistakes skill do?
Agent 2 of the video editing pipeline. Finds and removes spoken mistakes — stutters, repeated words, false starts, and retakes (re-recorded lines) — from a talking-head recording. Use after cut-silences, when asked to cut mistakes, remove stutters / repeats / filler restarts, clean up flubs, or keep the best take. Works review-gated: it proposes every cut with context and a reason for approval, then renders only the approved cuts via ffmpeg. Requires a word-level transcript.
How do I install cut-mistakes?
Run npx skills add https://github.com/nateherkai/herk-edit-kit --skill cut-mistakes in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cut-mistakes/ (personal) or .claude/skills/cut-mistakes/ (project).
Which AI agents can use cut-mistakes?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is cut-mistakes safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains cut-mistakes?
It is published by nateherkai in nateherkai/herk-edit-kit. It has 179 recorded installs.
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