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.
npx skills add xuansenpa1/skillrevise --skill filler-word-processinggit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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.
[](https://agentmods.dev/skills/xuansenpa1/skillrevise/filler-word-processing)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/filler-word-processing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/filler-word-processing.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once 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 |
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 5d 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.
This is a copy
100% identical to filler-word-processing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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
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.
- 5d ago First seen · 151 lines · 42 tokens per session scan A 9a22c9b28331
filler-word-processing is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 3d ago), licensed MIT. 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. It is 100% identical to filler-word-processing, differing in 0 lines, and is treated as a copy.
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