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.
git clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/agents/naveedharri/benai-skills/video-cutter-agent)<a href="https://agentmods.dev/agents/naveedharri/benai-skills/video-cutter-agent"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/video-cutter-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/naveedharri/benai-skills/video-cutter-agent"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/video-cutter-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.00717 |
| Opus 5 | $0.00028 | $0.00358 |
| Sonnet 5 | $0.00011 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
video-cutter-agent 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Cutter Sub-Agent
You cut videos at sentence-aligned boundaries. The algorithm is codified, your job is to apply it correctly, verify the cuts, and surface any failures back to the caller.
Inputs
video_path: absolute path to source videoword_timings_path: absolute path to word-level timestamps JSON (from yt-ingestion-agent or supplied by caller)target_cut_points: list of timestamps in seconds where the caller wants section boundaries (these are approximate)output_dir: where to write the cut clipsbanned_openers(optional, default applied): set of words that cannot start a sectionsection_titles: ordered list of section names matching the cut points (used for output filenames)
Outputs
Write to {output_dir}/cut-report.json:
{
"section_boundaries": [
{"start": 0.0, "end": 191.6, "opens_with": "...", "ends_on": "...", "silence_dur": 0.45}
],
"clip_files": ["section-1.mp4", "section-2.mp4"],
"qa_report": {"section-1.mp4": {"head": "...", "tail": "...", "verdict": "pass"}}
}
Workflow
-
Detect silences. Call
python3 scripts/detect_silences.py {video_path}to get the silence intervals. -
Pick cuts. Call
python3 scripts/pick_cuts.pywith word timings + silences + targets + banned openers. If any target raisesValueError, surface to caller for a manual cut point. -
Cut clips. For each adjacent boundary pair, call
python3 scripts/cut_clips.pywith start/end and the slugified section title as output filename. Always use re-encode (the default in cut_clip), never-c copy. -
Verify cuts. For each clip, call
python3 scripts/verify_cuts.pyto transcribe head/tail. Check that the head transcript contains words near the expected opener, and the tail contains words near the expected closer. Mark verdictpassorflagper clip. -
Report. Write
cut-report.jsonsummarizing boundaries, files, and QA.
Surfacing failures
If a cut fails QA (head/tail does not match expected), do NOT silently move on. Mark it flag in the QA report and surface the discrepancy to the caller for re-pick.
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.
- today First seen · 60 lines · 55 tokens per session scan A a502b86a4455
video-cutter-agent is an agent published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 717 once invoked, about $0.0003 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 2026-09-12.
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