How do I install the Trim Filler skill?

Run npx skillmds@latest add jperrello/trim-filler in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Trim Filler safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Trim Filler?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Trim Filler free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Trim Filler?

jperrello (@jperrello) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Trim Filler by jperrello — Agent Skill

Name: Trim Filler
Author: jperrello

Trim Filler

Semantic dead-air / filler removal. Claude reads a clip-local word-timed transcript and marks filler words, trail-offs, false starts, repeated re-starts, and short digressive asides for removal. Emits keeps.json (ranges to keep) and transcript.trimmed.json (kept words with shifted timestamps). Pairs with cut-filler, which applies the cuts to the clip's video.

jperrello Updated Sep 17, 2026

File contents

trim-filler

Tightens a podcast clip by stripping low-value words, not just silence. Where tighten-pace cuts inter-word gaps > gap_max, trim-filler cuts whole spans of speech that don't carry the point — "uh", "um", "you know what I mean", false starts, and the brief tangents speakers fall into mid-sentence.

Example: "I opened a restaurant that was like uhm — I love restaurants haha — yeah me too anyways it sold burgers" → "I opened a restaurant that sold burgers".

Invoke

.claude/skills/trim-filler/trim-filler.sh <in_transcript> <out_keeps> <out_transcript> [pad=0.05]

in_transcript — clip-local word-timed transcript JSON (output of transcribe rebased to the clip)
out_keeps — written keeps.json containing the keep ranges and removal rationale
out_transcript — written clip-local transcript JSON with only kept words and shifted timestamps
pad — kept padding around each retained span (sec)

Output shape — keeps.json

{
  "source": "<original transcript path>",
  "keeps": [[0.0, 4.21], [5.83, 12.04], ...],
  "removed": [
    {"t0": 4.21, "t1": 5.83, "words": "uhm — I love restaurants haha — yeah me too anyways", "reason": "filler + digression"}
  ],
  "removed_total": 1.62
}

How

Build a numbered transcript (one word per line: <idx>\t<t0>\t<t1>\t<word>).
Ask Claude (claude -p) which index ranges to REMOVE because they are filler, trail-offs, false starts, or short asides that don't carry the speaker's point. Speech that delivers the actual content stays.
Parse Claude's JSON reply, build the complement (keeps), pad each kept span, merge overlaps, write both outputs.

Re-runs are idempotent via an mtime+pad signature in <out_keeps>.tfmeta.

Pairs with

cut-filler — consumes keeps.json and re-encodes the clip's video+audio.

Caveats

Operates per-clip after cut-clip + rebase. Don't run on the full source transcript — Claude context budget.
If Claude returns "no cuts", outputs are pass-through.

---name: trim-fillerdescription: Semantic dead-air / filler removal. Claude reads a clip-local word-timed transcript and marks filler words, trail-offs, false starts, repeated re-starts, and short digressive asides for removal. Emits keeps.json (ranges to keep) and transcript.trimmed.json (kept words with shifted timestamps). Pairs with cut-filler, which applies the cuts to the clip's video.---# trim-fillerTightens a podcast clip by stripping low-value words, not just silence. Where `tighten-pace` cuts inter-word gaps `> gap_max`, `trim-filler` cuts whole spans of speech that don't carry the point — "uh", "um", "you know what I mean", false starts, and the brief tangents speakers fall into mid-sentence.Example: `"I opened a restaurant that was like uhm — I love restaurants haha — yeah me too anyways it sold burgers"` → `"I opened a restaurant that sold burgers"`.## Invoke```.claude/skills/trim-filler/trim-filler.sh <in_transcript> <out_keeps> <out_transcript> [pad=0.05]```- `in_transcript` — clip-local word-timed transcript JSON (output of `transcribe` rebased to the clip)- `out_keeps` — written `keeps.json` containing the keep ranges and removal rationale- `out_transcript` — written clip-local transcript JSON with only kept words and shifted timestamps- `pad` — kept padding around each retained span (sec)## Output shape — keeps.json```json{ "source": "<original transcript path>", "keeps": [[0.0, 4.21], [5.83, 12.04], ...], "removed": [ {"t0": 4.21, "t1": 5.83, "words": "uhm — I love restaurants haha — yeah me too anyways", "reason": "filler + digression"} ], "removed_total": 1.62}```## How1. Build a numbered transcript (one word per line: `<idx>\t<t0>\t<t1>\t<word>`).2. Ask Claude (`claude -p`) which index ranges to REMOVE because they are filler, trail-offs, false starts, or short asides that don't carry the speaker's point. Speech that delivers the actual content stays.3. Parse Claude's JSON reply, build the complement (keeps), pad each kept span, merge overlaps, write both outputs.Re-runs are idempotent via an mtime+pad signature in `<out_keeps>.tfmeta`.## Pairs with- `cut-filler` — consumes `keeps.json` and re-encodes the clip's video+audio.## Caveats- Operates per-clip after `cut-clip + rebase`. Don't run on the full source transcript — Claude context budget.- If Claude returns "no cuts", outputs are pass-through.

Frequently asked questions