prd-ml

Generate structured Markdown PRDs for ML and MLOps initiatives.

Updated Dec 21, 2021
One-click install
npx skills add https://github.com/dobraga/dotfiles --skill prd-ml
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prd-ml
Source: https://github.com/dobraga/dotfiles/tree/main/.claude/skills/prd-ml
Command: npx skills add https://github.com/dobraga/dotfiles --skill prd-ml

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate comprehensive Product Requirements Documents for data science, machine learning, and MLOps initiatives to align teams and formalize project scope.

Core Features & Use Cases

  • Automates structure creation for ML PRDs including goals, background, data requirements, evaluation, user stories, risks, and non-goals.
  • Produces a ready-to-upload Markdown PRD at docs/tasks/prd-[feature-name].md.
  • Supports end-to-end lifecycle planning from data acquisition to deployment considerations.

Quick Start

Provide a feature description and I will generate a complete PRD draft saved under docs/tasks/prd-[feature-name].md.

Frequently Asked Questions about prd-ml

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

FAQPage Schema
How do I generate a machine learning PRD for model development and MLOps?

Generate a machine learning PRD by providing a feature description to produce a structured Markdown document covering data requirements, model specifications, evaluation metrics, and MLOps deployment considerations.

What sections should a data science product requirements document include?

A data science product requirements document should include sections for goals, background, data requirements, model and algorithm specifications, evaluation criteria, user stories, risks, non-goals, success metrics, and open questions.

Can I use this to plan data pipelines and ML deployment infrastructure?

Yes, you can plan data pipelines and ML deployment infrastructure by drafting PRDs that cover end-to-end lifecycle planning from data acquisition to deployment considerations for data science and MLOps initiatives.

What is the best way to structure an MLOps initiative document for team alignment?

The best way to structure an MLOps initiative document is to use a standardized PRD template with dedicated sections for data requirements, model algorithms, evaluation, and risks, outputting a ready-to-use Markdown file.

How do I save a drafted PRD for a data science project?

You save a drafted PRD for a data science project by outputting the final structured Markdown document to the file path docs/tasks/prd-[feature-name].md for version control and team access.