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 agentmods add skills/aris4u-dev/aris4u/transcribenpx skills add aris4u-dev/aris4u --skill transcribegit clone --depth 1 https://github.com/aris4u-dev/aris4uWrote 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/aris4u-dev/aris4u/transcribe)<a href="https://agentmods.dev/skills/aris4u-dev/aris4u/transcribe"><img src="https://agentmods.dev/badge/skills/aris4u-dev/aris4u/transcribe.svg" alt="Measured on agentmods" 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 | $0.00130 | $0.00856 |
| Opus 5 | $0.00065 | $0.00428 |
| Sonnet 5 | $0.00026 | $0.00171 |
| Haiku 4.5 | $0.00013 | $0.00086 |
Grade A, and why
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 5d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 5d ago First seen · 83 lines · 130 tokens per session scan A 643a056a7daa
transcribe is a skill published in the GitHub repository aris4u-dev/aris4u (0 stars, last pushed 1mo ago), with no licence file. It adds 130 tokens to every session and 856 once invoked, about $0.0006 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 2026-09-01.
Other skills, from other repositories
listen
Nested swiss-knife reference for local audio analysis — transcribe speech with Whisper, or extract musical features (tempo, key, dynamics, spectral profile) with librosa. Both run on the user's machine with no API key. Read this when the human asks you to transcribe a voice note, extract lyrics from singing, critique…
voice-chatterbox-tts
Use when free local TTS with voice cloning using Chatterbox. Zero API costs, word-level timing, whisper integration. Clone any voice with 10-60s reference audio. Use when generating narration, voiceovers, or custom AI voices.
openai-whisper
Local speech-to-text with the Whisper CLI (no API key).
transcribe-tool
Audio transcription tool. Converts audio files to text with Whisper and optional LLM post-processing. Use when: transcribing meetings, podcasts, or extracting text from recorded audio files.
audio-transcriber-transcription
Speech-to-text on the audio-transcriber MCP server — run Whisper (faster-whisper, falling back to openai-whisper) over a local audio/video file or a microphone recording, and export txt/srt/vtt/json captions. Use when the agent must transcribe or translate spoken audio, generate subtitle/caption files, or pick a…
faster-whisper
Local speech-to-text using faster-whisper. 4-6x faster than OpenAI Whisper with identical accuracy; GPU acceleration enables 20x realtime transcription. SRT/VTT/TTML/CSV subtitles, speaker diarization, URL/YouTube input, batch processing with ETA, transcript search, chapter detection, per-file language map.