AI & ML
Real file from a public repo · synced Sep 3, 2026
ml-adoption-playbook
End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.
Works with
Claude CodeCursorCodex CLIGitHub CopilotGemini CLI
Install in three steps
- 1
Download the ZIP
One file per agent, plus INSTALL.md with the exact paths below.
- 2
Claude Code: copy SKILL.md into your skills folder
.claude/skills/ml-adoption-playbook/SKILL.md # this project ~/.claude/skills/ml-adoption-playbook/SKILL.md # every project
- 3
Other agents: use the matching file
skill.mdc goes in Cursor’s .cursor/rules/. AGENTS.md works as GitHub Copilot instructions, a legacy .cursorrules, or a Codex / Gemini CLI instructions file.
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