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 belschak/video-watch --skill audio-transcribegit clone --depth 1 https://github.com/belschak/video-watchWrote 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/belschak/video-watch/audio-transcribe)<a href="https://agentmods.dev/skills/belschak/video-watch/audio-transcribe"><img src="https://agentmods.dev/badge/skills/belschak/video-watch/audio-transcribe/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/belschak/video-watch/audio-transcribe"><img src="https://agentmods.dev/badge/skills/belschak/video-watch/audio-transcribe.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 | $0.00144 | $0.00556 |
| Opus 5 | $0.00072 | $0.00278 |
| Sonnet 5 | $0.00029 | $0.00111 |
| Haiku 4.5 | $0.00014 | $0.00056 |
Grade A, and why
audio-transcribe 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 10d 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.
What it actually says
audio-transcribe
Local audio file -> Whisper transcript as .md. The script does everything itself: ffmpeg conversion (16 kHz mono opus), chunking above 20 MB, upload to the configured endpoint, retries, temp cleanup. It imports the proven pipeline of the video-watch skill instead of duplicating it, so both skills need to be installed side by side (they ship in the same repo).
Setup (once): same .env as video-watch (in the video-watch repo root or skill folder; both locations are read): WHISPER_API_URL, plus WHISPER_API_KEY for hosted APIs. See .env.example. ffmpeg must be on PATH.
Workflow
- Transcribe:
python3 <skill-path>/scripts/audio-transcribe.py "<path>" [--vocab "Term1, Term2"] [--out <folder>] [--json]--vocab: proper nouns and jargon likely to occur (derive from file name and context). Improves Whisper accuracy noticeably.- Several files in one call are fine;
--outmust then be a folder. --json: additionally write raw segments (finer timestamps) as .json, only for machine post-processing.
- Output:
<name>-transcript-YYYY-MM-DD.mdnext to the source file, paragraphs with[m:ss]timestamps. If the source sits in an awkward place (Downloads, Desktop), use--outto write into the matching project folder instead, never into a workspace root. - Answer follow-ups ("summarize", "what do I say about X") from the .md; never transcribe twice.
Cost depends on your endpoint; at Groq's whisper-large-v3 pricing (as of 2026-07) about $0.111 per audio hour. For YouTube or podcast URLs use the video-watch skill.
What ships with it
1 file 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.
- 10d ago First seen · 25 lines · 144 tokens per session scan A 9220132712d0
audio-transcribe is a skill published in the GitHub repository belschak/video-watch (1 stars, last pushed 1mo ago), licensed MIT. It adds 144 tokens to every session and 556 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
audio-transcriber-knowledge-graph
Native knowledge-graph ingestion of Whisper transcripts on the audio-transcriber MCP server — transcribe an audio/video file and push it into the epistemic-graph in one call: the raw audio as a shared :MediaAsset blob, the transcript text as a :Document, and each Whisper segment as a :TranscriptSegment node linked…
dialogue-transcriber
Transcribe audio or video of conversations — interviews, panel discussions, meetings, podcasts, YouTube videos — and identify who said what (speaker diarization) using the transcriber CLI. Use this skill whenever the user wants a transcript of multi-speaker audio, asks "who said what", wants speakers labeled or…
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
Audio Transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.