document-learnings

Document Max for Live learnings in the canonical Max4Live-MCP knowledge repository.

Updated Aug 9, 2026
One-click install
npx skills add https://github.com/CooperNederhood/Ableton-Agent-App --skill document-learnings-coopernederhood
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: document-learnings
Source: https://github.com/CooperNederhood/Ableton-Agent-App/tree/main/.github/skills/document-learnings
Command: npx skills add https://github.com/CooperNederhood/Ableton-Agent-App --skill document-learnings-coopernederhood

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Max for Live development knowledge is easily lost after a patching session ends. This Skill captures what was learned about a Max for Live patch or topic and stores it in a structured, validated, and ingestible format inside the canonical Max4Live-MCP knowledge repository. ## Core Features & Use Cases - Structured Learning Documents: Creates a patch-learnings.md file with YAML frontmatter (type, key_objects, complexity, source) plus sections for description, implementation details, object roles, lessons learned, and anti-patterns. - Visual Documentation: Captures patching-view and presentation-view screenshots and copies the saved .amxd device file when available. - Validation and Ingestion: Runs the ingest_learning.py script with --validate before ingesting the learning into the max_knowledge tree. - Use Case: After building a Max for Live MIDI effect, use this Skill to record the signal flow, object roles, and gotchas so the knowledge is searchable in future sessions. ## Quick Start Document what we learned from this Max for Live patch and ingest it into the Max4Live-MCP knowledge repository.

Frequently Asked Questions about document-learnings

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I document Max for Live patch learnings?

Create a patch-learnings.md file in the max_knowledge/m4l-learnings directory with YAML frontmatter and sections for description, implementation details, object roles, lessons learned, and anti-patterns. Then validate and ingest it with the ingest_learning.py script.

What frontmatter fields are required for a Max for Live learning document?

The learning document must include YAML frontmatter with type, key_objects, complexity, and source fields. The body then follows with description, implementation details, an Object Roles table, lessons learned, and anti-patterns sections.

How do I validate and ingest a learning into the max_knowledge repository?

Run python3 ingest_learning.py with the learning file path and the --validate flag first, then run it again without the flag to ingest. Both commands use the script located in the Max4Live-MCP repository's scripts directory.

What screenshots are needed for Max for Live documentation?

Capture both a patching-view.jpg and a presentation-view.jpg in a patch-screenshots folder. Take screenshots last because they consume substantial context, and copy the saved .amxd file when available.

Where should Max for Live learnings be stored?

All output goes in the canonical checkout at $MAX4LIVE_MCP_ROOT/max_knowledge/m4l-learnings/<topic-or-patch-name>/. Never create a second max_knowledge tree inside the ableton-agent-app repository.