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 lfurze/claude-skills --skill claude-code-video-transcriptiongit clone --depth 1 https://github.com/lfurze/claude-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/skills/lfurze/claude-skills/claude-code-video-transcription)<a href="https://agentmods.dev/skills/lfurze/claude-skills/claude-code-video-transcription"><img src="https://agentmods.dev/badge/skills/lfurze/claude-skills/claude-code-video-transcription/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/skills/lfurze/claude-skills/claude-code-video-transcription"><img src="https://agentmods.dev/badge/skills/lfurze/claude-skills/claude-code-video-transcription.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 | $0.00102 | $0.01145 |
| Opus 5 | $0.00051 | $0.00573 |
| Sonnet 5 | $0.00020 | $0.00229 |
| Haiku 4.5 | $0.00010 | $0.00114 |
Grade A, and why
video-analyse 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Analyse
Instructions
You are analysing a video file for the user. Follow these steps precisely.
Step 1: Find the video
If the user provided a path as an argument, use that. Otherwise, look for video files in the current directory:
ls -1 *.{mp4,mov,mkv,avi,webm,m4v} 2>/dev/null
If multiple videos are found, ask the user which one. If none are found, ask for a path.
Step 2: Check dependencies
Before running anything, verify the tools are available:
which ffmpeg && python3 -c "import whisper; print('whisper OK')"
If ffmpeg is missing: tell the user to run brew install ffmpeg (macOS) or apt install ffmpeg (Linux).
If whisper is missing: tell the user to run pip install openai-whisper.
Step 3: Determine frame rate
The script at ~/.claude/skills/video-analyse/analyse_video.py has auto mode built in. Unless the user specified a frame rate, use --auto which selects based on duration:
| Duration | FPM | Interval | Approx frames |
|---|---|---|---|
| < 5 min | 2 | 30s | ~10 |
| 5–15 min | 1 | 60s | ~15 |
| 15–45 min | 0.5 | 2 min | ~15–22 |
| 45–90 min | 0.33 | 3 min | ~15–30 |
| 90+ min | 0.2 | 5 min | ~18–24 |
If the user asked for a specific rate, pass --fpm <value> instead.
Step 4: Run the extraction pipeline
python3 ~/.claude/skills/video-analyse/analyse_video.py "<video_path>" --auto
This creates a {video-stem}-analysis/ directory containing:
summary.md— frame index and metadatatranscript.md— timestamped Whisper transcriptframes/— numbered JPG stills with timestamps
The script will take a while for long videos (transcription is the slow part). Let the user know it's running.
Step 5: Read all outputs
Once the script completes, read everything into context:
- Read
{video-stem}-analysis/summary.md - Read
{video-stem}-analysis/transcript.md - Read every frame image in
{video-stem}-analysis/frames/(use the Read tool on each.jpg— Claude can see images)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 127 lines · 102 tokens per session scan A 1a73d7e5db74
video-analyse is a skill published in the GitHub repository lfurze/claude-skills (23 stars, last pushed 6mo ago), licensed MIT. It adds 102 tokens to every session and 1,145 once invoked, about $0.0005 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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