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.
- Kind
- Skill
- Works with
- Claude Code, Codex
- Source
- affaan-m/ECC @ d3b8a3e90890
- Path
- skills/ml-adoption-playbook/SKILL.md
- License
- MIT
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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. - **Metric Definition:** Define what business metric the ML model is trying to improve (e.g., click-through rate, reduced latency). - **Mistake Budget:** Define what a "bad" prediction looks like and how the system should handle it. ## Phase 2: Data Readiness ML is useless without clean, accessible data. …