ml-adoption-playbook
End-to-end methodology for AI agents and software engineers
by affaan-mFree
Description
ml-adoption-playbook is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It is a method for adding machine learning to an existing software project that was not built around it. It helps an agent or engineer move from a feature request to a working model, bridging ordinary software engineering and MLOps. The method begins with problem framing and feasibility, which asks whether a simple heuristic such as a rule or a regular expression could solve the problem first, defines the business metric the model should move, and sets a mistake budget for what a bad prediction looks like and how the system should handle it. Later phases cover data readiness, how the model is decoupled from the rest of the system, training and integration. It applies when a user asks to add an algorithm or machine learning to a codebase, plans a recommendation, classification or forecasting model, or wants an agent to build, train and deploy an ML component in stages. A skill is a SKILL.md file of instructions that an AI agent loads when a task calls for it. It is free and open source, and the skills.sh listing shows all three security audits passing.…
Highlights
- Problem framing and heuristic check first
- Business metric and mistake budget
- Data readiness phase
- Staged path from feature to model
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ml-adoption-playbook FAQ
What does the ml-adoption-playbook skill do?
It gives a staged method for adding machine learning to an existing non-ML codebase, starting from problem framing.
Should I always use a model?
No. The playbook asks first whether a simple heuristic, such as a rule-based sort, could solve the problem faster.
What is a mistake budget?
A definition of what a bad prediction looks like and how the system should handle it, set before any model code is written.
How do I install it?
Run: npx skills add https://github.com/affaan-m/ecc --skill ml-adoption-playbook
Is it free?
Yes. It is free and open source.
Are there security findings?
The skills.sh listing shows Gen Agent Trust Hub, Socket and Snyk all passing.
What are the best ml-adoption-playbook alternatives?
Popular alternatives to ml-adoption-playbook include mle-workflow, batch-grill-me and decision-mapping. See all ml-adoption-playbook alternatives