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 G1Joshi/Agent-Skills --skill whispergit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/whisper)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/whisper"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/whisper/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/g1joshi/agent-skills/whisper"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/whisper.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 | $0.00016 | $0.00281 |
| Opus 5 | $0.00008 | $0.00140 |
| Sonnet 5 | $0.00003 | $0.00056 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
whisper 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
Whisper
Whisper (OpenAI) is the industry standard for Speech-to-Text. It supports 99 languages and translation. V3 (large-v3) is the current state of the art.
When to Use
- Transcription: Creating subtitles for videos.
- Translation: Translating audio to English text.
- Local Privacy: Runs 100% locally (sensitive meetings).
Core Concepts
Models
tiny, base, small, medium, large, legacy, large-v3, large-v3-turbo.
Distil-Whisper
Smaller, faster versions of Whisper (6x speedup, 1% accuracy loss).
Best Practices (2025)
Do:
- Use
insanely-fast-whisper: A wrapper that uses Flash Attention to transcribing 2 hours of audio in 2 minutes. - Use API for streaming: OpenAI API supports streaming audio transcription.
Don't:
- Don't use
largefor realtime: It's too slow. Useturboordistilmodels.
References
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 · 40 lines · 16 tokens per session scan A 738c0a6f9f94
whisper is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 16 tokens to every session and 281 once invoked, about $0.0001 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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