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 h4vzz/awesome-ai-agent-skills --skill meeting-transcriptiongit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-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/h4vzz/awesome-ai-agent-skills/meeting-transcription)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/meeting-transcription"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/meeting-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/h4vzz/awesome-ai-agent-skills/meeting-transcription"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/meeting-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 | $0.00034 | $0.02051 |
| Opus 5 | $0.00017 | $0.01026 |
| Sonnet 5 | $0.00007 | $0.00410 |
| Haiku 4.5 | $0.00003 | $0.00205 |
Grade A, and why
meeting-transcription 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 13d 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.
This is a copy
98% identical to meeting-transcription — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
Meeting Transcription
This skill enables an AI agent to process meeting audio recordings into structured, actionable documents. The agent handles the full pipeline from raw audio input through speaker diarization, transcription, and intelligent summarization. The output includes a timestamped transcript with speaker labels, a concise summary of key discussion points, a list of decisions made, and clearly assigned action items with owners and deadlines.
Workflow
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Ingest and validate the audio input. Accept the meeting audio file and verify it is in a supported format: MP3, WAV, M4A, FLAC, OGG, or WebM. Check the file size, duration, and channel count (mono vs. stereo). If the audio is in a non-standard format, convert it to WAV 16kHz mono using FFmpeg or a similar preprocessing tool. Log the file metadata (duration, sample rate, codec) for downstream reference.
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Preprocess the audio for quality. Apply noise reduction to suppress background hum, keyboard clicks, and room echo. Normalize audio levels across the recording so that quiet speakers are boosted and loud segments are attenuated. If the recording has multiple channels (e.g., a stereo podcast), split channels where each maps to a known speaker. Flag sections with very low signal-to-noise ratio as potentially unreliable.
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Perform speaker diarization. Identify and label distinct speakers throughout the recording. Use voiceprint clustering to distinguish speakers even when they interrupt each other or speak in quick succession. Assign temporary labels (Speaker 1, Speaker 2, etc.) by default, and allow the user to provide a name mapping either before or after processing. Handle overlapping speech by attributing the segment to the dominant speaker and noting the overlap.
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Transcribe the audio to text. Run the preprocessed, diarized audio through a speech-to-text engine (e.g., Whisper, Deepgram, Google Speech-to-Text). Produce a word-level or segment-level transcript with timestamps. Apply punctuation restoration and capitalization correction. For multi-language meetings, detect language switches and transcribe each segment in its original language, optionally providing inline translations.
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.
- 13d ago First seen · 151 lines · 34 tokens per session scan A 86dde345baf3
meeting-transcription is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 2,051 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to meeting-transcription, differing in 2 lines, and is treated as a copy.
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