Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AgriciDaniel/claude-musicnpx agentmods add skills/agricidaniel/claude-music/claude-music-enhanceWrote 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-enhance)<a href="https://agentmods.dev/skills/agricidaniel/claude-music/claude-music-enhance"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-music/claude-music-enhance/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-enhance"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-music/claude-music-enhance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.00723 |
| Opus 5.5 | $0.00023 | $0.00289 |
| Sonnet 5.5 | $0.00012 | $0.00145 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
claude-music-enhance 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
claude-music-enhance — Audio Post-Processing
Default path: loudness normalization (recommended first)
Loudness-normalize to the platform target in one ffmpeg call. This is the right answer 90% of the time — the generated audio is already clean; it just needs the level right.
# Two-pass loudnorm to -14 LUFS (Spotify/YouTube/Apple/TikTok all target this).
bash ~/.claude/skills/claude-music/scripts/music_export.sh spotify input.flac output.flac
See references/post-processing.md for the full platform × LUFS table and
the measure-then-apply two-pass recipe if you need manual control.
Escape hatch 1 — AI vocal denoise (only if audible artifacts)
Use only when you hear artifacts in vocals (hissing, AI mush, robot tones). FFmpeg normalization won't help; you need DeepFilterNet3.
source ~/.video-skill/bin/activate
python3 ~/.claude/skills/claude-video/scripts/audio_enhance.py denoise \
input.flac --output output_clean.flac
Requires claude-video skill + DeepFilterNet3 weights. <1 GB VRAM.
Escape hatch 2 — stem separation (only if you need surgical edits)
Use only when you need to fix vocals/drums/bass/other separately, then remix. Not needed for normal enhancement.
source ~/.video-skill/bin/activate
python3 ~/.claude/skills/claude-video/scripts/audio_enhance.py separate \
input.flac --model htdemucs_ft --output-dir ./stems/
Produces vocals.wav, drums.wav, bass.wav, other.wav. ~7 GB VRAM.
Decision tree
Is the generated audio too quiet or too loud?
→ use the default path (loudnorm)
Do you hear specific vocal artifacts?
→ escape hatch 1 (denoise) THEN default path (loudnorm)
Do you need to fix one stem (e.g., reduce drums, retune vocal) before final mix?
→ escape hatch 2 (separate) → edit stem → remix → default path (loudnorm)
Format conversion
For platform-specific export (codec/bitrate/sample-rate), use
claude-music-export — it wraps music_export.sh with the right flags per
target (Spotify FLAC, YouTube MP3 320k, TikTok M4A 256k, etc.).
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 · 82 lines · 58 tokens per session scan A 817ff863fd67
claude-music-enhance is a skill published in the GitHub repository AgriciDaniel/claude-music (86 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 723 once invoked, about $0.0002 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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