Cut Mistakes
Agent 2 of the video editing pipeline.
Install
npx skills add nateherkai/herk-edit-kit --skill cut-mistakes -g -a claude-code -y- Repository
- nateherkai/herk-edit-kit2.5k
- Category
- AI and agents
- Updated
- 21d ago
- License
- See repository
- Context cost
- Lightabout 924 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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.
