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 iradoweck/antigravity-awesome-skills --skill audio-transcribergit clone --depth 1 https://github.com/iradoweck/antigravity-awesome-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/iradoweck/antigravity-awesome-skills/audio-transcriber)<a href="https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/audio-transcriber"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/audio-transcriber/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/iradoweck/antigravity-awesome-skills/audio-transcriber"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/audio-transcriber.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 | $0.00018 | $0.01011 |
| Opus 5.5 | $0.00007 | $0.00404 |
| Sonnet 5.5 | $0.00004 | $0.00202 |
| Haiku 4.5 | $0.00002 | $0.00101 |
Grade A, and why
audio-transcriber 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 1mo 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
100% identical to audio-transcriber — 0 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use
Invoke this skill when:
- User needs to transcribe audio/video files to text
- User wants meeting minutes automatically generated from recordings
- User requires speaker identification (diarization) in conversations
- User needs subtitles/captions (SRT, VTT formats)
- User wants executive summaries of long audio content
- User asks variations of "transcribe this audio", "convert audio to text", "generate meeting notes from recording"
- User has audio files in common formats (MP3, WAV, M4A, OGG, FLAC, WEBM)
Example Usage
Example 1: Basic Transcription
User Input:
copilot> transcribe audio to markdown: meeting-2026-02-02.mp3
Skill Output:
✅ Faster-Whisper detected (optimized)
✅ ffmpeg available (format conversion enabled)
📂 File: meeting-2026-02-02.mp3
📊 Size: 12.3 MB
⏱️ Duration: 00:45:32
🎙️ Processing...
[████████████████████] 100%
✅ Language detected: Portuguese (pt-BR)
👥 Speakers identified: 4
📝 Generating Markdown output...
✅ Transcription Complete!
📊 Results:
File: meeting-2026-02-02.md
Language: pt-BR
Duration: 00:45:32
Speakers: 4
Words: 6,842
Processing time: 127s
📝 Generated:
- meeting-2026-02-02.md (Markdown report)
🎯 Next steps:
1. Review meeting minutes and action items
2. Share report with participants
3. Track action items to completion
Example 3: Batch Processing
User Input:
copilot> transcreva estes áudios: recordings/*.mp3
Skill Output:
📦 Batch mode: 5 files found
1. team-standup.mp3
2. client-call.mp3
3. brainstorm-session.mp3
4. product-demo.mp3
5. retrospective.mp3
🎙️ Processing batch...
[1/5] team-standup.mp3 ✅ (2m 34s)
[2/5] client-call.mp3 ✅ (15m 12s)
[3/5] brainstorm-session.mp3 ✅ (8m 47s)
[4/5] product-demo.mp3 ✅ (22m 03s)
[5/5] retrospective.mp3 ✅ (11m 28s)
✅ Batch Complete!
📝 Generated 5 Markdown reports
⏱️ Total processing time: 6m 15s
What ships with it
7 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.
- 1mo ago Changed · -423 lines 29f4e8747c99
- 1mo ago First seen · 560 lines · 18 tokens per session scan A 855a317fa3ad
audio-transcriber is a skill published in the GitHub repository iradoweck/antigravity-awesome-skills (30 stars, last pushed 5d ago), licensed MIT. It adds 18 tokens to every session and 1,011 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to audio-transcriber, differing in 0 lines, and is treated as a copy.
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audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
audio-transcriber
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Get YouTube captions, timestamped quotations, SRT/VTT and translated transcripts for videos, channels and playlists through the harshmaur/youtube-transcript-scraper Apify Actor.
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Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.