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.
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 benchflow-ai/skillsbench --skill filler-word-processinggit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/filler-word-processing)<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>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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- filler-word-processing — 100% identical, 0 lines differ
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.
- 7d ago First seen · 151 lines · 42 tokens per session scan A 9a22c9b28331
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.
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