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 BlackBeltTechnology/pi-agent-dashboard --skill music-analysisgit clone --depth 1 https://github.com/BlackBeltTechnology/pi-agent-dashboardWrote 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/blackbelttechnology/pi-agent-dashboard/music-analysis)<a href="https://agentmods.dev/skills/blackbelttechnology/pi-agent-dashboard/music-analysis"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/music-analysis/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/blackbelttechnology/pi-agent-dashboard/music-analysis"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/music-analysis.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00090 | $0.01520 |
| Opus 5.5 | $0.00036 | $0.00608 |
| Sonnet 5.5 | $0.00018 | $0.00304 |
| Haiku 4.5 | $0.00009 | $0.00152 |
Grade A, and why
music-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
music-analysis
Turns a local audio file into <stem>_map.json + <stem>_analysis.png. The map
is the input of music-edit-to-length and beat-sync-video.
Step 0 — Python environment (once per project)
The scripts never install anything. Resolve the package root first:
PKG=<this skill dir>/../../.. (it holds lib/ and the requirements files).
# quick tier (librosa) — enough for everything except beat_this / stems / tags
uv venv -p python3.14 .venv-music
uv pip install --python .venv-music -r "$PKG/requirements-core.txt"
# deep tier — optional, multi-GB (torch, TensorFlow). Keep it in its OWN venv.
uv venv -p python3.14 .venv-mir
uv pip install --python .venv-mir -r "$PKG/requirements-mir.txt"
Verified platform: Python 3.14 on macOS arm64; every requirement is pinned exactly. A missing module makes a script exit 2 with one line naming the requirements file.
Step 1 — Source the audio (no-rip rule)
Analyse only a local file the user supplies. Never download or extract audio from a streaming service, and never suggest a tool that does. Acceptable sources:
- a purchased file (lossless or high-bitrate);
- a file from the artist, label or composer (stems welcome);
- the user's own recording of a session they are allowed to use.
From a video recording, extract the audio and check its level:
ffmpeg -i recording.mkv -vn -ac 2 -ar 44100 -c:a pcm_s16le track_rec.wav
ffmpeg -i track_rec.wav -af volumedetect -f null - 2>&1 | grep -E "max_volume|mean_volume"
Quiet recordings: when max_volume is below −12 dBFS, gain the source up so it
peaks at about −2 dBFS before analysis (e.g. a −21.9 dB peak → -af volume=19.9dB),
and note the applied gain next to the file (README row or filename) so the mix stage
knows the source was lifted.
Step 2 — Quick tier
.venv-music/bin/python "$PKG/.pi/skills/music-analysis/scripts/analyze_music.py" track.wav [--out-dir music/]
Step 3 — Deep tier (optional)
.venv-mir/bin/python "$PKG/.pi/skills/music-analysis/scripts/analyze_mir.py" track.wav [--map music/track_map.json] [--no-tags] [--no-stems]
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.
- 19d ago First seen · 109 lines · 90 tokens per session scan A 141d9290b75c
music-analysis is a skill published in the GitHub repository BlackBeltTechnology/pi-agent-dashboard (319 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 1,520 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-27.
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