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 agents-inc/skills --skill ai-provider-openai-whispergit clone --depth 1 https://github.com/agents-inc/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/agents-inc/skills/ai-provider-openai-whisper)<a href="https://agentmods.dev/skills/agents-inc/skills/ai-provider-openai-whisper"><img src="https://agentmods.dev/badge/skills/agents-inc/skills/ai-provider-openai-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/agents-inc/skills/ai-provider-openai-whisper"><img src="https://agentmods.dev/badge/skills/agents-inc/skills/ai-provider-openai-whisper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.03623 |
| Opus 5 | $0.00018 | $0.01811 |
| Sonnet 5 | $0.00007 | $0.00725 |
| Haiku 4.5 | $0.00004 | $0.00362 |
Grade A, and why
ai-provider-openai-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 12d 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAI Whisper Patterns
Quick Guide: Use
client.audio.transcriptions.create()for speech-to-text andclient.audio.translations.create()for non-English audio to English text. Choosegpt-4o-transcribefor highest accuracy,gpt-4o-mini-transcribefor cost-efficiency,whisper-1for timestamps/SRT/VTT, orgpt-4o-transcribe-diarizefor speaker identification. Files must be under 25 MB -- chunk larger files. Usepromptto guide vocabulary and style. Streaming is available viastream: truefor progressive output ongpt-4o-transcribemodels.
<critical_requirements>
CRITICAL: Before Using This Skill
All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering,
import type, named constants)
(You MUST choose the correct model for the use case -- gpt-4o-transcribe for accuracy, whisper-1 for timestamps/SRT/VTT output, gpt-4o-transcribe-diarize for speaker labels)
(You MUST chunk audio files larger than 25 MB before sending to the API -- the API rejects files exceeding this limit)
(You MUST pass response_format: "verbose_json" when using timestamp_granularities -- timestamps only work with this format on whisper-1)
(You MUST set chunking_strategy: "auto" when using gpt-4o-transcribe-diarize with audio longer than 30 seconds -- the API requires it)
</critical_requirements>
Auto-detection: Whisper, whisper-1, gpt-4o-transcribe, gpt-4o-mini-transcribe, gpt-4o-transcribe-diarize, audio.transcriptions, audio.translations, transcription, speech-to-text, diarization, diarized_json, timestamp_granularities, verbose_json
When to use:
- Transcribing audio files (meetings, interviews, podcasts, voice notes) to text
- Translating non-English audio to English text
- Generating subtitles in SRT or VTT format from audio
- Getting word-level or segment-level timestamps for video editing
- Identifying speakers in multi-speaker audio (diarization)
- Streaming transcription results progressively as the model processes audio
What ships with it
2 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.
- 12d ago First seen · 327 lines · 36 tokens per session scan A c1e2e49de043
ai-provider-openai-whisper is a skill published in the GitHub repository agents-inc/skills (24 stars, last pushed 4d ago), licensed MIT. It adds 36 tokens to every session and 3,623 once invoked, about $0.0002 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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