Individual skill
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.
skills/ml-adoption-playbook/SKILL.md
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- ai-agents Repository topic
- anthropic Repository topic
- claude Repository topic
- claude-code Repository topic
- developer-tools Repository topic
- llm Repository topic
- mcp Repository topic
- productivity Repository topic
- engineering Derived category
- data Derived category
- education Derived category
- testing Derived category
- testing-browser Derived category
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# ML Adoption Playbook
This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional SWE and MLOps by structuring how ML should be researched, decoupled, trained, and integrated.
## When to Activate
- A user asks to "add ML" or "add an algorithm" to their existing codebase.
- Planning the integration of a new model (e.g., recommendation, classification, forecasting) into a non-ML application.
- Structuring a workflow for an agent to build, train, and deploy an ML component adaptively.
## Phase 1: Problem Framing & Feasibility
Before writing model code, establish the "why" and "how".
- **Heuristic Check:** Ask the user if a simple heuristic (e.g., regex, rule-based sorting) could solve the problem faster. If yes, start there.