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Agent skill

ML Adoption Playbook

by affaan-m in affaan-m/ECC

End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases.

MITAuto-check passedData & Analytics

Install ML Adoption Playbook

skills CLI
$ npx skills add affaan-m/ECC --skill ml-adoption-playbook -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC ml-adoption-playbook --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ml-adoption-playbook .claude/skills/ml-adoption-playbook && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ml-adoption-playbook
GitHub stars
277k
Used in
1 other repo
Token cost
~975 tokens
SKILL.md length
506 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases.

  • Works in 5 steps: Problem Framing & Feasibility → Data Readiness → Architectural Integration & Decoupling → …
  • Adding a machine learning capability to a codebase that has none
  • SKILL.md covers When to Activate, Phase 1: Problem Framing &…, Phase 2: Data Readiness and Phase 3: Architectural…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

ML Adoption Playbook is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 980 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Adding a machine learning capability to a codebase that has none
  • From problem framing through a baseline model

Example prompts

  • “/ml-adoption-playbook”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Problem Framing & Feasibility
  2. Data Readiness
  3. Architectural Integration & Decoupling
  4. Model Implementation & Training
  5. Handoff to MLOps

What it can do on your machine

Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

ML Adoption Playbook loads about 975 tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 506 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~975

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 506 words, ~975 tokens.

Download SKILL.mdSave it as .claude/skills/ml-adoption-playbook/SKILL.md (or your agent's skills folder).
name
ml-adoption-playbook
description
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.
origin
ECC

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.

  • Audit Data Sources: Identify where the training data lives. Is it a live database, a static CSV, or an API?
  • Data Contract: Establish a schema for the input data. What features are required? What happens if a feature is missing?
  • Leakage Prevention: Ensure the user's proposed data split does not accidentally leak future information into the training set (e.g., chronological splitting for time-series data).

Phase 3: Architectural Integration & Decoupling

Do not tightly couple model inference to core business logic.

  • API Boundary: Suggest placing the model behind an API endpoint (e.g., using fastapi-patterns or django-patterns) or a dedicated service class.
  • Fallback Mechanisms: Design a default state. If the model takes too long to respond or throws an error, the system must gracefully fall back to a hardcoded rule.
  • Feature Flags: Wrap the new ML inference call in a feature flag so it can be rolled out (or rolled back) safely.
Show full SKILL.md (190 more words)Show less

Phase 4: Model Implementation & Training

Structure the code for reproducibility and iteration.

  • Start Simple: Build a baseline model first (e.g., a simple scikit-learn Logistic Regression or a barebones PyTorch linear layer).
  • Reproducibility: Apply pytorch-patterns or similar best practices: fix random seeds, make code device-agnostic, and explicitly document tensor/array shapes.
  • Automated Evidence: Require tests for the data transforms and inference schema. Do not accept a model without an evaluation script comparing it against the baseline.

Phase 5: Handoff to MLOps

Once the baseline model is integrated, shift focus to continuous operations.

  • Refer to mle-workflow: Guide the user toward setting up experiment tracking, model registries, and drift detection.
  • CI/CD: Add the model evaluation step to the existing CI pipeline to ensure future commits do not degrade model performance.

Iterative Agent Workflow

When assisting a user via this playbook, agents should:

  1. Ask clarifying questions to complete Phase 1 before proposing architectures.
  2. Draft a data contract in Phase 2 for user approval.
  3. Write the decoupling interface (API/Service) in Phase 3 before writing the training loop.
  4. Deliver a reproducible script in Phase 4 that trains the model and saves the artifact.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ml-adoption-playbook of affaan-m/ECC.

Open the folder on GitHubat commit 2d515e4

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

ML Adoption Playbook next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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ML Adoption Playbook this skillaffaan-m/ECC277k1 repos~975Automated safety check: PassMIT
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Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT
Geomlitalo-goncalves/geoML109—~4.9kAutomated safety check: PassGPL-3.0
QuantMind Training Config Generatorqusong0627/QuantMind1.7k—~1.5kAutomated safety check: PassAGPL-3.0

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Categories

Questions about ML Adoption Playbook

What does ML Adoption Playbook do?

End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. ML Adoption Playbook is an agent skill from affaan-m/ECC. End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases.

When should I use ML Adoption Playbook?

ML Adoption Playbook fits situations like: adding a machine learning capability to a codebase that has none; from problem framing through a baseline model.

How do I install ML Adoption Playbook in Claude Code?

Run `npx skills add affaan-m/ECC --skill ml-adoption-playbook -a claude-code`. Or copy the skill folder (skills/ml-adoption-playbook in affaan-m/ECC) into .claude/skills/ml-adoption-playbook in your project. Claude Code loads it when a task matches its description.

How do I install ML Adoption Playbook in Codex?

Run `npx skills add affaan-m/ECC --skill ml-adoption-playbook -a codex`. Or copy the skill folder (skills/ml-adoption-playbook in affaan-m/ECC) into .agents/skills/ml-adoption-playbook in your project. Codex loads it when a task matches its description.

Can I use ML Adoption Playbook in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill ml-adoption-playbook -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml-adoption-playbook, .gemini/skills/ml-adoption-playbook, .github/skills/ml-adoption-playbook and .opencode/skills/ml-adoption-playbook in your project.

What does ML Adoption Playbook need to run?

SKILL.md names no scripts, command-line tools or credentials: ML Adoption Playbook is instructions for the agent only.

Does ML Adoption Playbook access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is ML Adoption Playbook safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does ML Adoption Playbook use?

ML Adoption Playbook is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does ML Adoption Playbook use?

About 975 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to ML Adoption Playbook?

Skills that share tags, products or a category with ML Adoption Playbook: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars) and Geoml (italo-goncalves/geoML, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains ML Adoption Playbook?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.