Case 03438

Skill knownasnaffy/prompthound/dataset/case_03438

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_03438

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Transcribe audio files to text using OpenAI Whisper. Supports speech-to-text with auto language detection, multiple output formats (txt, srt, vtt, json), batch processing, and model selection (tiny to large). Use when transcribing audio recordings, podcasts, voice messages, lectures, meetings, or any audio/video file to text. Handles mp3, wav, m4a, ogg, flac, webm, opus, aac formats.

SKILL.md

2.0 KB, 416 tokens by cl100k_base, as published. Nobody here has run it

Cleanup Review

python scripts/_cache_clear.py is an optional cleanup helper for the Whisper Transcribe skill. It may recursively remove /tmp/skill_build/*, /tmp/skill_build/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

Whisper Transcribe

Transcribe audio with scripts/transcribe.sh:

# Basic (auto-detect language, base model)
scripts/transcribe.sh recording.mp3

# German, small model, SRT subtitles
scripts/transcribe.sh --model small --language de --format srt lecture.wav

# Batch process, all formats
scripts/transcribe.sh --format all --output-dir ./transcripts/ *.mp3

# Word-level timestamps
scripts/transcribe.sh --timestamps interview.m4a

Models

Model	RAM	Speed	Accuracy	Best for
tiny	~1GB	⚡⚡⚡	★★	Quick drafts, known language
base	~1GB	⚡⚡	★★★	General use (default)
small	~2GB	⚡	★★★★	Good accuracy
medium	~5GB	🐢	★★★★★	High accuracy
large	~10GB	🐌	★★★★★	Best accuracy (slow on Pi)

Output Formats

txt — Plain text transcript
srt — SubRip subtitles (for video)
vtt — WebVTT subtitles
json — Detailed JSON with timestamps and confidence
all — Generate all formats at once

Requirements

whisper CLI (pip install openai-whisper)
ffmpeg (for audio decoding)
First run downloads the model (~150MB for base)

What ships with it: 2 files

3.5 KB alongside SKILL.md, 2 of them executable

scripts/

_cache_clear.pyruns499 B
transcribe.shruns3.0 KB