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 DJRHails/dotfiles --skill whispergit clone --depth 1 https://github.com/DJRHails/dotfilesWrote 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/djrhails/dotfiles/whisper)<a href="https://agentmods.dev/skills/djrhails/dotfiles/whisper"><img src="https://agentmods.dev/badge/skills/djrhails/dotfiles/whisper.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 | $0.00067 | $0.02035 |
| Opus 5 | $0.00034 | $0.01018 |
| Sonnet 5 | $0.00013 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade B, and why
whisper scanned grade B with 1 finding 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 4d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
# Ubuntu: sudo apt install ffmpeg This is a copy
91% identical to whisper — 3 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whisper - Robust Speech Recognition
OpenAI's multilingual speech recognition model.
When to use Whisper
Use when:
- Speech-to-text transcription (99 languages)
- Podcast/video transcription
- Meeting notes automation
- Translation to English
- Noisy audio transcription
- Multilingual audio processing
Metrics:
- 72,900+ GitHub stars
- 99 languages supported
- Trained on 680,000 hours of audio
- MIT License
Use alternatives instead:
- AssemblyAI: Managed API, speaker diarization
- Deepgram: Real-time streaming ASR
- Google Speech-to-Text: Cloud-based
Quick start
Installation
# Requires Python 3.8-3.11
pip install -U openai-whisper
# Requires ffmpeg
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
# Windows: choco install ffmpeg
Basic transcription
import whisper
# Load model
model = whisper.load_model("base")
# Transcribe
result = model.transcribe("audio.mp3")
# Print text
print(result["text"])
# Access segments
for segment in result["segments"]:
print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}")
Model sizes
# Available models
models = ["tiny", "base", "small", "medium", "large", "turbo"]
# Load specific model
model = whisper.load_model("turbo") # Fastest, good quality
| Model | Parameters | English-only | Multilingual | Speed | VRAM |
|---|---|---|---|---|---|
| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |
| base | 74M | ✓ | ✓ | ~16x | ~1 GB |
| small | 244M | ✓ | ✓ | ~6x | ~2 GB |
| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |
| large | 1550M | ✗ | ✓ | 1x | ~10 GB |
| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |
Recommendation: Use turbo for best speed/quality, base for prototyping
Transcription options
Language specification
# Auto-detect language
result = model.transcribe("audio.mp3")
# Specify language (faster)
result = model.transcribe("audio.mp3", language="en")
# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more
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.
- 4d ago First seen · 321 lines · 67 tokens per session scan B 060e2cc18ff6
whisper is a skill published in the GitHub repository DJRHails/dotfiles (2 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 2,035 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 91% identical to whisper, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
gemini-audio
Guide for implementing Google Gemini API audio capabilities - analyze audio with transcription, summarization, and understanding (up to 9.5 hours), plus generate speech with controllable TTS. Use when processing audio files, creating transcripts, analyzing speech/music/sounds, or generating natural speech from text.
todoist-cli
Manage Todoist tasks, projects, labels, filters, sections, comments, reminders, and workspaces via the td CLI. Use when the user wants to view, create, update, complete, or organize Todoist items, or mentions tasks, inbox, today, upcoming, projects, labels, or filters.
autopilot-batch
Fan out a batch of autopilot-queued issues to parallel background worktree subagents — each runs /autopilot at the build model from its 'model:' label — with a gating review at Opus 5 or above and never below the build (Opus reviews Sonnet and Opus builds, Fable reviews Fable builds).
autopilot
Carry a well-scoped GitHub issue through the full dev loop autonomously, stopping at a per-run tier boundary (PR-ready, or merge+deploy for small reversible changes).
qa-handoff
Generate a hands-on QA testing guide as a self-contained HTML page — for Rails apps or static (Hugo) sites. --publish uploads the HTML to the project's configured QA host.
walkthrough
Generate a hands-on browser walkthrough of a PR's user-facing changes to exercise before review; --publish posts the final version to the PR for QA.