ml-plan

Generate and validate machine learning implementation plans using official framework documentation.

192|18|Updated Mar 3, 2026
One-click install
npx skills add https://github.com/Leeroo-AI/superml --skill ml-plan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-plan
Source: https://github.com/Leeroo-AI/superml/tree/main/skills/ml-plan
Command: npx skills add https://github.com/Leeroo-AI/superml --skill ml-plan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill transforms high-level goals into detailed, validated, and runnable ML implementation plans, preventing costly mistakes and wasted GPU hours.

Core Features & Use Cases

  • Automated Plan Generation: Creates step-by-step ML project plans grounded in official documentation.
  • Validation & Gap-Filling: Reviews plans against documentation, identifies risks, and fetches missing details.
  • Use Case: When asked to "fine-tune a Llama 3 model for sentiment analysis on customer reviews," this Skill will generate a plan detailing the exact libraries, versions, commands, and configurations needed, citing official docs for each step.

Quick Start

Use the ml-plan skill to generate an implementation plan for fine-tuning a Llama 3 model for sentiment analysis.

Frequently Asked Questions about ml-plan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a validated machine learning implementation plan from a high-level goal?▼

To generate a validated machine learning implementation plan, you provide a high-level project goal and the system outputs a step-by-step plan grounded in official framework documentation, ensuring all configurations and commands are executable.

How does validating an ML plan against official documentation prevent wasted GPU hours?▼

Validating an ML plan against official documentation prevents wasted GPU hours by checking proposed steps for version-specific API compatibility and potential pitfalls, gap-filling missing configurations before execution begins.

Can I use official framework documentation to account for version-specific APIs in my MLOps plan?▼

Yes, you can use official framework documentation to account for version-specific APIs in your MLOps plan, fetching exact library requirements and configurations to ensure the implementation remains grounded and runnable.

What's the best way to plan fine-tuning a Llama 3 model for sentiment analysis?▼

The best way to plan fine-tuning a Llama 3 model for sentiment analysis is generating a detailed implementation plan that specifies exact libraries, versions, commands, and configurations, citing official docs for each step.

Does ML plan generation work with both local knowledge base and web-fetched documentation?▼

ML plan generation works with both KB-backed and web-fetched documentation modes, consulting official framework sources comprehensively to validate implementation steps and identify potential risks.

What are the limitations of automated ML engineering plan generation?▼

Automated ML engineering plan generation relies on available official framework documentation to validate steps and identify risks, meaning it cannot account for undocumented custom runtime environments or proprietary library behaviors.