bio-ai-product-manager

Drafts structured PRDs and user stories for biology-aware AI products.

Updated Apr 2, 2026

Search Tags:#biology #user-stories #domain-expertise #ai-product #discovery-framework #prd-drafting #api-dependencies

Ready to run

npx skills add https://github.com/jonkiky/ccdi-federation-ai --skill bio-ai-product-manager

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bio-ai-product-manager
Source: https://github.com/jonkiky/ccdi-federation-ai/tree/main/.agents/skills/bio-ai-product-manager
Command: npx skills add https://github.com/jonkiky/ccdi-federation-ai --skill bio-ai-product-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Biology and life-science teams often struggle to turn ideas into concrete AI product plans that align with domain needs, workflows, and data constraints.

Core Features & Use Cases

Brainstorm biology-aware AI product directions with clear links to real user workflows and regulatory considerations.
Translate the strongest direction into a PRD structure including goals, user problems, functional requirements, non-goals, API dependencies, risks, assumptions, and open questions.
Produce user stories, acceptance criteria, API gap analysis, and an open questions list to guide discovery and alignment.

Quick Start

Provide a rough product idea and biology context to generate a complete PRD draft, user stories, acceptance criteria, and API considerations.

Frequently Asked Questions about bio-ai-product-manager

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

FAQPage Schema

How do I draft a PRD for a biology-aware AI product?▼

To draft a PRD for a biology-aware AI product, provide a rough idea and biology context to generate a structured document including goals, functional requirements, user stories, and API dependencies.

What is included in an AI product discovery framework for life sciences?▼

An AI product discovery framework for life sciences includes brainstorming product directions, translating them into PRD structures, generating user stories, acceptance criteria, and conducting an API gap analysis.

How do I write user stories for life science applications with data constraints?▼

Writing user stories for life science applications involves defining clear links to real user workflows, regulatory considerations, and data constraints, which are then translated into actionable acceptance criteria.

Can I analyze API dependencies and risks for an AI-enabled biology application?▼

Yes, you can analyze API dependencies and risks for an AI-enabled biology application by translating product directions into a PRD structure that explicitly outlines API gaps, risks, and assumptions.

Does ideating a biology AI product require domain expertise to start?▼

Ideating a biology AI product requires providing basic biology context and a rough product idea to guide the discovery process, aligning outputs with domain needs and specific workflows.

What is the best way to align cross-functional teams on AI biology product requirements?▼

The best way to align cross-functional teams on AI biology product requirements is generating a PRD draft with explicit assumptions, open questions, and API considerations to guide discovery and alignment.