ai-product-manager

Automate AI product management workflows from discovery to iteration.

6|Updated Mar 9, 2026
One-click install
npx skills add https://github.com/platootalp/agents --skill ai-product-manager-platootalp
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-product-manager
Source: https://github.com/platootalp/agents/tree/main/skills/ai-product-manager
Command: npx skills add https://github.com/platootalp/agents --skill ai-product-manager-platootalp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AI product teams often struggle with scattered research practices, inconsistent PRD templates, and slow iteration loops. This Skill provides a structured playbook to coordinate product discovery, requirements mining, PRD drafting, prototype design, and governance of human–AI collaboration, enabling faster, more reliable product outcomes.

Core Features & Use Cases

  • PRD drafting: generate structured product requirements documents aligned with organizational standards.
  • Requirements mining: extract user needs from interviews, analytics, and market data.
  • Prototype design: translate findings into testable prototypes and interaction flows.
  • Human–AI workflow governance: define role boundaries, evaluation metrics, and data feedback loops.
  • Data-driven iteration: establish dashboards, feedback loops, and metrics to guide ongoing improvements.
  • Use Case: Launch of a new AI feature with quick market assessment and a clear PRD plan.

Quick Start

Start a discovery sprint to generate a PRD, a prototype plan, and a governance blueprint for human–AI collaboration.

Frequently Asked Questions about ai-product-manager

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

FAQPage Schema
How do I draft a PRD for an AI product feature?

Drafting an AI product PRD requires structured templates that enforce requirements mining, evaluation metrics, and human-AI workflow boundaries to ensure reliable product outcomes from discovery to iteration.

What is the best way to manage human-AI collaboration workflows?

Managing human-AI collaboration involves defining clear role boundaries, establishing evaluation metrics, and creating data feedback loops. This Skill provides a governance blueprint to coordinate these interactions throughout the AI product life cycle.

How do I extract user requirements for AI products from market data?

Extracting user requirements for AI products applies structured research methods to analyze interviews, analytics, and market data. The playbook automates this requirements mining process to translate findings into testable prototypes and interaction flows.

Can I use this to establish data-driven iteration for an AI feature launch?

Yes, you can establish data-driven iteration by setting up dashboards, feedback loops, and metrics to guide ongoing improvements. It supports a complete discovery sprint from market assessment to governance blueprint for new AI feature launches.

Does this AI product management playbook handle prototype design?

Yes, the playbook handles prototype design by translating research findings and mined requirements into testable prototypes. It structures the interaction flows as part of the automated workflow from product discovery to iteration.