Music Analysis
v3.0.2Analyze music/audio files locally without external APIs. Extract tempo, pocket/groove feel, pulse stability, swing proxy, section/repetition structure, key c...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (local music/audio analysis) align with the included Python scripts and declared dependencies (librosa, numpy, ffmpeg/ffprobe). The code implements tempo, timbre, structure, instrument detection and optional lyric alignment via a local Whisper CLI/model which fits the stated purpose. Minor note: SKILL.md suggests using yt-dlp for fetching YouTube audio (network use) as an optional audio sourcing method; that is outside the 'no external APIs' claim but presented as an explicit optional workflow.
Instruction Scope
Runtime instructions and scripts operate on local audio files, run ffmpeg/ffprobe and (optionally) whisper-cli, and write analysis reports to disk if requested. The SKILL.md and scripts do not instruct reading unrelated system files, environment secrets, or posting data to remote endpoints. Whisper usage is optional and the code includes a fallback path if Whisper is missing.
Install Mechanism
There is no install spec and requirements.txt only lists librosa and numpy. The skill relies on system binaries (ffmpeg/ffprobe) and optionally a locally installed whisper-cli and model file; nothing in the repository pulls code from arbitrary URLs or writes external installers. This is a low-risk install posture.
Credentials
The skill declares no required environment variables or credentials. The code does reference concrete filesystem paths (a Homebrew whisper-cli path and a home-dir model path) but these are optional and not secrets. No credentials or sensitive environment access is requested.
Persistence & Privilege
The skill is user-invocable, not always-included, and does not modify other skills or agent-wide configuration. It runs as a normal local tool and does not request elevated or persistent privileges.
Assessment
This skill appears to do what it claims: offline analysis of audio files using librosa and local tools. Before installing or running: 1) ensure you trust and want any local binaries it calls (ffmpeg/ffprobe and optionally whisper-cli) because the scripts invoke them via subprocess; 2) the hardcoded whisper-cli path (/opt/homebrew/bin/whisper-cli) and the model path (~/.local/share/whisper-cpp/...) are mac- and home-directory-specific — if you don't have Whisper or the model, the code will skip it, but if you do, be aware Whisper models can be large; 3) the README suggests using yt-dlp to fetch YouTube audio — that will download data from the network if you follow that workflow; 4) no credentials are requested and there are no network callbacks in the code, but you should still review the included scripts yourself (they are present) before running them on sensitive data. If you want higher confidence, run the scripts in an isolated environment (temporary folder or container) and verify the absence/presence of whisper-cli and the model if you do not want transcription.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.