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 AgriciDaniel/claude-music --skill claude-music-randomgit clone --depth 1 https://github.com/AgriciDaniel/claude-musicWrote 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/agricidaniel/claude-music/claude-music-random)<a href="https://agentmods.dev/skills/agricidaniel/claude-music/claude-music-random"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-music/claude-music-random/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/agricidaniel/claude-music/claude-music-random"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-music/claude-music-random.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 9 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00035 | $0.00703 |
| Opus 5 | $0.00017 | $0.00351 |
| Sonnet 5 | $0.00007 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
claude-music-random 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 12d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
claude-music-random — Quick Random Generation
Worked example (input → command → output)
User: "surprise me"
Claude picks: Lo-fi (from the pool below), composes a caption.
Command:
bash ~/.claude/skills/claude-music/scripts/music_engine.sh \
--quality draft \
generate \
--caption "lo-fi hip-hop, chill, atmospheric, vinyl crackle, mellow piano" \
--instrumental --duration 60
Expected JSON on stdout (batch=4 from draft preset):
{
"success": true,
"task_type": "text2music",
"model": "acestep-v15-turbo",
"outputs": [
{"path": ".../text2music_20260416_121530_01.flac", "seed": 81, "index": 1},
{"path": ".../text2music_20260416_121530_02.flac", "seed": 84012, "index": 2},
{"path": ".../text2music_20260416_121530_03.flac", "seed": 44190, "index": 3},
{"path": ".../text2music_20260416_121530_04.flac", "seed": 2031, "index": 4}
],
"timing": {"generation_sec": 14.8},
"count": 4
}
Then present all four paths and ask which to iterate on.
Workflow
- Pick a random genre from the list below
- Craft a caption using genre-appropriate tags
- Generate with draft quality (4 variants, ~15s)
Genre Pool
Pick randomly from: Pop, Rock, Jazz, Lo-fi, Electronic, Hip-hop, R&B, Ambient, Folk, Synthwave, Bossa Nova, Afrobeat, Funk, Soul, Blues, Country, Reggae, Classical Piano, Cinematic, Chillwave, House, DnB, Trap, Phonk
Quick Generate
# Instrumental random (fastest)
bash ~/.claude/skills/claude-music/scripts/music_engine.sh generate \
--caption "<genre tags here>" \
--instrumental --quality draft --duration 60
# With vocals (Claude writes a quick verse + chorus)
bash ~/.claude/skills/claude-music/scripts/music_engine.sh generate \
--caption "<genre tags>" \
--lyrics "[Verse] <quick lyrics> [Chorus] <catchy hook>" \
--quality draft --duration 60
After Generation
Present all 4 variants with paths and suggest: "Want me to refine any of these? I can repaint sections, change the style, or generate more in this direction."
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.
- 12d ago First seen · 81 lines · 35 tokens per session scan A d1a9e63cc3a6
claude-music-random is a skill published in the GitHub repository AgriciDaniel/claude-music (51 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 703 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
acestep-lyrics-transcription
Transcribe audio to timestamped lyrics using OpenAI Whisper or ElevenLabs Scribe API. Outputs LRC, SRT, or JSON with word-level timestamps. Use when users want to transcribe songs, generate LRC files, or extract lyrics with timestamps from audio.
acestep-songwriting
Music songwriting guide for ACE-Step. Provides professional knowledge on writing captions, lyrics, choosing BPM/key/duration, and structuring songs. Use this skill when users want to create, write, or plan a song before generating it with ACE-Step.
acestep-thumbnail
Generate song cover/thumbnail images using Gemini API. Creates artistic images suitable for music video backgrounds. Use when users want to generate album art, song covers, thumbnails, or background images for MVs.
acestep-docs
ACE-Step documentation and troubleshooting. Use when users ask about installing ACE-Step, GPU configuration, model download, Gradio UI usage, API integration, or troubleshooting issues like VRAM problems, CUDA errors, or model loading failures.
acestep
Use ACE-Step API to generate music, edit songs, and remix music. Supports text-to-music, lyrics generation, audio continuation, and audio repainting. Use this skill when users mention generating music, creating songs, music production, remix, or audio continuation.
acestep-simplemv
Render music videos from audio files and lyrics using Remotion. Accepts audio + LRC/JSON lyrics + title to produce MP4 videos with waveform visualization and synced lyrics display. Use when users mention MV generation, music video rendering, creating video from audio/lyrics, or visualizing songs.