Claude AI Music Skills
I love music but never learned an instrument. AI became the creative outlet that was always out of reach. This project started as a way to go deep on Claude Code plugin architecture, agentic workflows, multi-model orchestration, and MCP tooling. Music was the domain because it was personal.
What it actually does: a Claude Code plugin that turns a conversation into a full album production pipeline. You describe what you want to make, and it handles concept development, lyrics, Suno prompts (an AI music generation platform), audio mastering, and release prep — with quality gates and source verification at every stage.
[!NOTE]
Active development happens on the develop branch — main only receives tested, stable releases. If you run into issues, open an issue or submit a PR.
Example Workflow
You: "Let's make an album about the 2016 Bangladesh Bank heist"
Claude: Creates album structure, runs 7-phase concept planning
You: "Start the research"
Claude: Dispatches legal, financial, and security researchers in parallel
Gathers DOJ filings, SWIFT documentation, malware analysis
Cross-verifies sources, flags claims that need human review
You: "Sources look good. Let's write track 1"
Claude: Drafts lyrics, checks prosody and rhyme schemes
Scans for pronunciation risks, suggests phonetic fixes
Builds Suno V5 style prompt with genre tags and vocal direction
You: "Track sounds great, here are the stems"
Claude: Imports stems from Suno, polishes per-stem
Masters to -14 LUFS for streaming
Generates promo video and social media copy
Concept to released album. You generate on Suno, everything else happens in the terminal.
Install
/plugin marketplace add bitwize-music-studio/claude-ai-music-skills
/plugin install bitwize-music@claude-ai-music-skills
Then run /bitwize-music:setup to detect your environment and install dependencies. Run /bitwize-music:configure to set your artist name and workspace paths.
Platform: Linux or macOS (Windows users: use WSL). Python 3.10+ for the MCP server and audio tools.
Architecture
This is where the engineering lives. The plugin is a case study in how far you can push Claude Code's plugin system.
Skill System (53 Skills)
Each skill is a self-contained markdown file with a YAML frontmatter that declares its model, description, and when it should activate. Skills range from simple clipboard operations to multi-step creative workflows. Claude routes to skills automatically based on context, or you invoke them directly with /bitwize-music:<name>.
The lyric-writer knows prosody rules, rhyme scheme analysis, and Suno's pronunciation quirks. The mastering-engineer knows loudness targets per platform and genre-specific EQ curves. The researcher coordinates parallel sub-agents across 10 domain specializations.
See docs/skills.md for the full reference.
Multi-Model Orchestration
Skills declare which Claude model they need. Creative work that directly impacts music quality runs on Opus. Coordination and reasoning tasks use Sonnet. Mechanical operations (imports, validation, clipboard) run on Haiku.
| Tier | Model | Skills | Rationale |
|---|
| Creative | Opus 4.8 | 7 | Lyrics, Suno prompts, album concepts, legal/verification research — output quality defines the music |
| Reasoning | Sonnet 4.6 | 30 | Research coordination, pronunciation analysis, most workflows |
| Mechanical | Haiku 4.5 | 16 | Imports, validation, clipboard, help — speed over creativity |
This project pushes Claude Code hard — multi-agent research, real-time audio analysis, sub-agent orchestration across model tiers. It works best on the Max plan. The standard Pro plan will hit rate limits during multi-track sessions.
See reference/model-strategy.md for per-skill rationale.
MCP Server (80+ Tools)
A Python MCP server exposes 80+ tools for instant state queries, audio analysis, lyrics processing, and database operations. The server is the plugin's nervous system — skills call MCP tools instead of reading files directly, which keeps responses fast and state consistent.
Key tool categories:
- State management — album/track lookups, session context, cache rebuild
- Lyrics analysis — syllable counting, readability scoring, rhyme detection, section validation, cross-track repetition
- Audio processing — mastering, stem analysis, QC checks, promo video generation
- Database — tweet/promo content management via PostgreSQL
Research System
