video-cutter-agent

video-cutter-agent is an agent for Claude Code from naveedharri/benai-skills. It costs 55 tokens per session (717 once invoked), scanned A, original, MIT.

Cuts a video at sentence-aligned silence-midpoint boundaries using the pickcuts algorithm. Takes target cut points, word timings, and a banned-opener list. Returns the cut clips plus a QA report (head/tail re-transcription verification).

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; mentions subagents.

Install with agentmods
npx agentmods add agents/naveedharri/benai-skills/video-cutter-agent
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.

Clone the repo
git clone --depth 1 https://github.com/naveedharri/benai-skills

Made for: Claude Code.

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 video-cutter-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/naveedharri/benai-skills/video-cutter-agent/github.svg)](https://agentmods.dev/agents/naveedharri/benai-skills/video-cutter-agent)
Your own site
<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.

agentmods 80×15 button for video-cutter-agent

Your own site · 80×15
<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>
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 717 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 unknown 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 $0.00055 $0.00717
Opus 5 $0.00028 $0.00358
Sonnet 5 $0.00011 $0.00143
Haiku 4.5 $0.00006 $0.00072

Measured today against content hash a502b86a4455, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

agents/video-cutter-agent.md · 60 lines

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 video
  • word_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 clips
  • banned_openers (optional, default applied): set of words that cannot start a section
  • section_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

  1. Detect silences. Call python3 scripts/detect_silences.py {video_path} to get the silence intervals.

  2. Pick cuts. Call python3 scripts/pick_cuts.py with word timings + silences + targets + banned openers. If any target raises ValueError, surface to caller for a manual cut point.

  3. Cut clips. For each adjacent boundary pair, call python3 scripts/cut_clips.py with start/end and the slugified section title as output filename. Always use re-encode (the default in cut_clip), never -c copy.

  4. Verify cuts. For each clip, call python3 scripts/verify_cuts.py to transcribe head/tail. Check that the head transcript contains words near the expected opener, and the tail contains words near the expected closer. Mark verdict pass or flag per clip.

  5. Report. Write cut-report.json summarizing 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.

Read the full file on GitHub · 60 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. today First seen · 60 lines · 55 tokens per session scan A a502b86a4455

Subscribe to this mod's changes

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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