Can it help with feature prioritization?

Yes, it uses structured frameworks like RICE (Reach, Impact, Confidence, Effort) and the Kano model to provide data-driven analysis of feature requests.

How does this skill differ from standard PM software?

This skill is context-aware and integrated with your codebase, meaning it can reference actual technical constraints, APIs, and debt when writing requirements or roadmaps.

Where are the PRDs and roadmaps stored?

All artifacts are generated as Markdown files within your Git repository, allowing them to be versioned, reviewed via PRs, and kept alongside the code they describe.

Does this skill replace a Product Manager?

No, it acts as a force multiplier for PMs and developers by handling documentation, synthesis, and initial analysis, while the final strategic decisions remain with the human user.

Product Management AI

Name: Product Management AI
Author: julianromli

byjulianromli

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Collaboration Tools

Assists with writing PRDs, analyzing feature requests, and synthesizing user research within your development workflow.

About

The Product Management AI skill streamlines core PM activities by integrating documentation and planning directly with your codebase. Unlike isolated point solutions, this skill provides context-aware assistance for drafting comprehensive PRDs, evaluating features using structured frameworks like RICE or ICE, and synthesizing research into actionable insights. By living alongside your code in version control, it ensures that product requirements, roadmaps, and decision logs remain technically grounded and accessible to the entire engineering team.

Key Features

12 GitHub stars
Version-controlled decision documentation for institutional memory
Code-integrated PRD generation referencing actual APIs and architecture
Structured feature prioritization using RICE, ICE, and Kano frameworks
User research synthesis to identify patterns and actionable insights
Outcome-focused roadmap planning with theme-based time horizons

Use Cases

Evaluating the business impact versus implementation effort for new feature requests
Translating raw user interview notes into structured requirements and success metrics
Drafting detailed technical PRDs that account for existing system constraints and dependencies

About

Key Features

12 GitHub stars
Version-controlled decision documentation for institutional memory
Code-integrated PRD generation referencing actual APIs and architecture
Structured feature prioritization using RICE, ICE, and Kano frameworks
User research synthesis to identify patterns and actionable insights
Outcome-focused roadmap planning with theme-based time horizons

Use Cases

Evaluating the business impact versus implementation effort for new feature requests
Translating raw user interview notes into structured requirements and success metrics
Drafting detailed technical PRDs that account for existing system constraints and dependencies