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 howdeploy/kisa-stack --skill ai-music-and-audio-toolsgit clone --depth 1 https://github.com/howdeploy/kisa-stackWrote 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/howdeploy/kisa-stack/ai-music-and-audio-tools)<a href="https://agentmods.dev/skills/howdeploy/kisa-stack/ai-music-and-audio-tools"><img src="https://agentmods.dev/badge/skills/howdeploy/kisa-stack/ai-music-and-audio-tools/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/howdeploy/kisa-stack/ai-music-and-audio-tools"><img src="https://agentmods.dev/badge/skills/howdeploy/kisa-stack/ai-music-and-audio-tools.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 | $0.00033 | $0.00594 |
| Opus 5.5 | $0.00013 | $0.00238 |
| Sonnet 5.5 | $0.00007 | $0.00119 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
ai-music-and-audio-tools 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 1mo 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Music and Audio Tools
Overview
This umbrella covers music/audio creation and analysis workflows: lyric writing, Suno-style prompt engineering, HeartMuLa generation, AudioCraft/MusicGen-style local generation, and audio feature visualization such as mel/chroma/MFCC spectrograms.
When to Use
- The user asks for lyrics, song structure, rhyme/meter, parody, or musical prompt writing.
- The user asks to generate music or background audio with AI tools.
- The user asks about HeartMuLa, AudioCraft, MusicGen, or Suno-like prompts.
- The user asks to visualize or inspect audio features.
Songwriting and Prompting
- Start with genre, mood, tempo, vocalist/instrumentation, structure, and emotional arc.
- Use bracketed metatags only when the target generator supports them.
- Keep lyrics singable: meter, stresses, repetition, and contrast matter more than clever prose.
- For background music, avoid lyrical hooks unless requested.
Generation Tools
HeartMuLa
- Treat as a local generation stack with installation, Python-version, and dependency compatibility constraints.
- Verify hardware and patched dependencies before promising generation.
AudioCraft / MusicGen
- Check GPU/CPU feasibility and model size.
- Generate short previews first; longer outputs may need batching or stitching.
- Save outputs with clear filenames and report real paths.
Suno-style prompting
- Separate style/genre description from lyrics when the UI/API expects separate fields.
- Include arrangement cues such as intro, verse, chorus, bridge, drop, outro, and instrumentation.
Audio Feature Visualization
- Use spectrogram/feature tools for mel, chroma, MFCC, waveform, or analysis plots.
- Confirm sample rate and channels before interpreting output.
- Present generated images/audio paths, not just descriptions.
Common Pitfalls
- Writing text that looks poetic but cannot be sung.
- Ignoring generator-specific field limits or metatag syntax.
- Starting a heavy generation job without checking hardware/dependencies.
- Treating a failed/partial render as a completed audio file.
- Overstating what feature visualizations prove musically.
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.
- 1mo ago First seen · 71 lines · 33 tokens per session scan A fea62f4227b1
ai-music-and-audio-tools is a skill published in the GitHub repository howdeploy/kisa-stack (57 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 594 once invoked, about $0.0001 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 2026-08-30.
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