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v1.0.0
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About this Skill

Ideal for AI Agents like Claude or AutoGPT needing advanced product management workflow automation, including research synthesis and feature prioritization. product-manager is a cross-platform system tray tool for quick AI assistant access, facilitating workflows such as user research, requirement documentation, and strategic communication.

Features

Assists with research synthesis, requirement documentation, and feature prioritization
Enables real-time AI interaction for stakeholder updates and presentations
Supports analyzing user interviews, surveys, or feedback for data-driven decisions
Facilitates interpreting product metrics and analytics for informed decision-making
Conducts competitive analysis for strategic product planning

# Core Topics

jk278 jk278
[0]
[0]
Updated: 3/6/2026

Quality Score

Top 5%
50
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add jk278/veld/product-manager

Agent Capability Analysis

The product-manager MCP Server by jk278 is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use product-manager, product-manager setup guide, what is product-manager.

Ideal Agent Persona

Ideal for AI Agents like Claude or AutoGPT needing advanced product management workflow automation, including research synthesis and feature prioritization.

Core Value

Empowers agents to streamline product development by synthesizing user research, documenting product requirements, and prioritizing features using data-driven insights from product metrics and analytics, all while facilitating strategic communication with stakeholders.

Capabilities Granted for product-manager MCP Server

Analyzing user feedback to inform product roadmaps
Automating the generation of PRDs and product requirement documents
Prioritizing features based on competitive analysis and market trends

! Prerequisites & Limits

  • Requires access to user feedback and market research data
  • May need integration with project management tools for seamless workflow
Project
SKILL.md
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SKILL.md
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Product Manager

Assists with core product management workflows including research synthesis, requirement documentation, feature prioritization, and strategic communication.

When to Use

  • Analyzing user interviews, surveys, or feedback
  • Writing or reviewing PRDs and product requirements
  • Prioritizing features or roadmap items
  • Preparing stakeholder updates or presentations
  • Interpreting product metrics and analytics
  • Conducting competitive analysis

Instructions

User Research Analysis

When analyzing user research:

  1. Read the provided transcripts, feedback, or survey data
  2. Identify the top 3-5 pain points with supporting quotes
  3. Group insights by themes (not by individual users)
  4. Prioritize by frequency AND impact
  5. Suggest 2-3 actionable opportunity areas
  6. Include specific quotes to support each finding

Feature Prioritization

When prioritizing features, use the RICE framework:

  • Reach: How many users impacted per time period?
  • Impact: Confidence score (0.25=minimal, 0.5=low, 1=medium, 2=high, 3=massive)
  • Confidence: Data quality (0-100%)
  • Effort: Person-months required
  • RICE Score = (Reach × Impact × Confidence) / Effort

Present results in a table with reasoning for each score.

PRD Writing

When creating or reviewing PRDs, ensure these sections:

  1. Problem Statement: Clear user problem with evidence
  2. Success Metrics: Quantifiable measures (not "improve UX")
  3. User Stories: Format: "As a [user], I want [action] so that [benefit]"
  4. Acceptance Criteria: Testable, specific conditions
  5. Edge Cases: Error states, boundary conditions, empty states
  6. Out of Scope: What we're explicitly NOT building

Flag any missing or unclear sections.

Stakeholder Communication

When drafting communications:

  1. Lead with impact/outcome, not features
  2. Match tone to audience (C-level: business impact; Eng: technical details)
  3. Use specific metrics, not vague terms like "better" or "improved"
  4. Include next steps with owners and timelines
  5. Be transparent about blockers/challenges

Metrics Analysis

When analyzing metrics:

  1. Calculate key ratios (DAU/MAU, retention cohorts, conversion rates)
  2. Identify trends (week-over-week, month-over-month)
  3. Flag anomalies requiring investigation
  4. Distinguish between correlation and causation
  5. Provide 2-3 actionable recommendations

Quick Reference

Problem Validation Checklist:

  • Problem clearly articulated?
  • Validated with real users (not assumptions)?
  • Frequent/painful enough to solve?
  • Users will pay/engage more if solved?
  • Technically feasible within constraints?

Go/No-Go Decision Criteria:

  • Strategic alignment with company vision?
  • Solves a validated user problem?
  • Moves key metrics meaningfully?
  • Team can build and maintain it?
  • Strengthens competitive differentiation?

Guidelines

  • Focus on outcomes (metrics moved) over outputs (features shipped)
  • Validate assumptions with data before building
  • Write testable, specific requirements (avoid "intuitive" or "easy to use")
  • Consider edge cases: errors, empty states, loading states
  • Question vanity metrics (prioritize engagement over page views)
  • Be explicit about trade-offs in prioritization decisions

Automatic Triggers:

  • User pastes interview transcripts or feedback
  • User asks to prioritize features or compare options
  • User mentions "PRD", "product requirements", or "user stories"
  • User shares metrics data or analytics
  • User requests stakeholder updates or presentations

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