filler-word-processing

filler-word-processing is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 42 tokens per session (1,113 once invoked), scanned A, original, Apache-2.0.

A tool that converts timestamped labels for filler words such as “um,” “uh,” and “you know” into time ranges to cut from audio or video.

In plain words
What is it for?
Use it to turn filler-word annotations into edit lists or cut segments for cleaning recorded speech.
Why use it?
It removes the manual work of estimating how long each filler lasts when preparing an edit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to turn filler-word annotations into edit lists or cut segments…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/filler-word-processing
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,747 stars · on GitHub · skillsbench.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add benchflow-ai/skillsbench --skill filler-word-processing
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for filler-word-processing

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/filler-word-processing.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/filler-word-processing)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/filler-word-processing"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/filler-word-processing.svg" alt="Measured on agentmods" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,113 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01113
Opus 5 $0.00021 $0.00557
Sonnet 5 $0.00008 $0.00223
Haiku 4.5 $0.00004 $0.00111

Measured 7d ago against content hash 9a22c9b28331, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

filler-word-processing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

tasks-extra/video-filler-word-remover/environment/skills/filler-word-processing/SKILL.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Filler Word Processing

Annotation Format

Typical annotation JSON structure:

[
  {"word": "um", "timestamp": 12.5},
  {"word": "like", "timestamp": 25.3},
  {"word": "you know", "timestamp": 45.8}
]

Converting Annotations to Cut Segments

Each filler word annotation marks when the word starts. To remove it, use word-specific durations since different fillers have different lengths:

import json

# Word-specific durations (in seconds)
WORD_DURATIONS = {
    "uh": 0.3,
    "um": 0.4,
    "hum": 0.6,
    "hmm": 0.6,
    "mhm": 0.55,
    "like": 0.3,
    "yeah": 0.35,
    "so": 0.25,
    "well": 0.35,
    "okay": 0.4,
    "basically": 0.55,
    "you know": 0.55,
    "i mean": 0.5,
    "kind of": 0.5,
    "i guess": 0.5,
}
DEFAULT_DURATION = 0.4

def annotations_to_segments(annotations_file, buffer=0.05):
    """
    Convert filler word annotations to (start, end) cut segments.

    Args:
        annotations_file: Path to JSON annotations
        buffer: Small buffer before the word (seconds)

    Returns:
        List of (start, end) tuples representing segments to remove
    """
    with open(annotations_file) as f:
        annotations = json.load(f)

    segments = []
    for ann in annotations:
        word = ann.get('word', '').lower().strip()
        timestamp = ann['timestamp']
        # Use word-specific duration, fall back to default
        word_duration = WORD_DURATIONS.get(word, DEFAULT_DURATION)
        # Cut starts slightly before the word
        start = max(0, timestamp - buffer)
        # Cut ends after word duration
        end = timestamp + word_duration
        segments.append((start, end))

    return segments

Merging Overlapping Segments

When filler words are close together, merge their cut segments:

def merge_overlapping_segments(segments, min_gap=0.1):
    """
    Merge segments that overlap or are very close together.

    Args:
        segments: List of (start, end) tuples
        min_gap: Minimum gap to keep segments separate

    Returns:
        Merged list of segments
    """
    if not segments:
        return []

    # Sort by start time
    sorted_segs = sorted(segments)
    merged = [sorted_segs[0]]

    for start, end in sorted_segs[1:]:
        prev_start, prev_end = merged[-1]

        # If this segment overlaps or is very close to previous
        if start <= prev_end + min_gap:
            # Extend the previous segment
            merged[-1] = (prev_start, max(prev_end, end))
        else:
            merged.append((start, end))

    return merged

Read the full file on GitHub · 151 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 151 lines · 42 tokens per session scan A 9a22c9b28331

Subscribe to this mod's changes

filler-word-processing is a skill published in the GitHub repository benchflow-ai/skillsbench (1,747 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 1,113 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.