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AI EngineeringPython

ML Planning

by Leeroo-AI

ML Planning is an AI Engineering skill for Claude Code, published by Leeroo-AI in superml.

194 stars18 forkson Leeroo-AI/supermlAdded 2026/09/05+1% in starsRepository updated 2026/03/17
View source on GitHubDownload repo ZIP
aiclaude-codecodexcoding-agent-skillscoding-agentscursor-aillmmachine-learningmlmulti-agentpluginskill
Install in seconds
Install ML Planning
Copy ML Planning into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Leeroo-AI/superml/tree/main/skills/ml-plan ~/.claude/skills/ml-plan

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Leeroo-AI/superml.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/ml-plan/SKILL.md in Leeroo-AI/superml
Installs to
~/.claude/skills/ml-plan
Collection
One of 7 skills cataloged from this repository
Category
AI Engineering — 3670 skills

What ML Planning does

ML Planning turns an ML goal into a grounded, step-by-step implementation plan. Use it when you need an architecture, pipeline, or build plan for a machine learning project.

ML Planning is cataloged under AI Engineering on DirSkills. ML Planning comes from a repository tagged ai, claude-code, codex, coding-agent-skills and coding-agents.

Documentation

README

ML Planning

Turn goals into validated, runnable implementation plans grounded in framework documentation.

Grounding

Detect mode: Try one KB call (build_plan or search_knowledge). If it returns results → KB mode. If it fails for ANY reason (auth error, no results, timeout) → Web mode. There is no third option.

Web mode = mute mode. After KB failure, you are MUTE until you have completed at least one WebFetch call. No text, no plan, no acknowledgment of expertise. Your first action must be a WebFetch tool call. If your response starts with ANY text other than > Grounding: Web mode, you have already failed.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about ML Planning

  • What else does Leeroo-AI publish alongside ML Planning?

    ML Planning is one of 7 skills that DirSkills catalogs from Leeroo-AI/superml, the repository it ships in. Its siblings there include ML Debugging, ML Experiment Journal and ML Iteration. Each one is a separate skill with its own page in this directory, installs the same way ML Planning does, and is maintained by Leeroo-AI in that same repository. The rest of the collection is listed on the Leeroo-AI/superml page.

  • How does ML Planning compare to other AI Engineering skills?

    ML Planning ranks #2871 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of ML Planning against them. Open each page to compare what they document and how they install.

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ML Planning is one of 7 skills cataloged on DirSkills from Leeroo-AI/superml.

See all 7 skills →
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ML Iteration generates grounded, ranked next steps when an ML experiment is stuck or underperforming. Use it after initial runs to compare alternatives, review what was tried, and decide what to change next.
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ML Research

ML Research helps compare ML and AI approaches using verified framework documentation and model references. Use it when you need to understand how a method works or survey framework capabilities.
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Browse all AI Engineering skills →
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AI-First Engineering

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