feature-prioritization-assistant

This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/pmprompt/claude-plugin-product-management --skill feature-prioritization-assistant

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Installation Guide

How to use feature-prioritization-assistant on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add feature-prioritization-assistant
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/pmprompt/claude-plugin-product-management --skill feature-prioritization-assistant

Fetches feature-prioritization-assistant from pmprompt/claude-plugin-product-management and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/feature-prioritization-assistant

Restart Cursor to activate feature-prioritization-assistant. Access via /feature-prioritization-assistant in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Domain Context

This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.

Input Requirements

  • Context about your product, feature, or problem
  • Relevant data, research, or constraints (recommended but optional)
  • Clear articulation of what you're trying to achieve

Feature Prioritization Assistant

When to Use

  • Building your product roadmap
  • Need to choose between multiple feature ideas
  • Stakeholders are debating which features to build first
  • Want to make data-driven prioritization decisions
  • Need to justify prioritization decisions to leadership

What This Skill Does

Helps you systematically evaluate and prioritize features using the RICE framework (Reach, Impact, Confidence, Effort), providing scores and recommendations.

Instructions

Help me prioritize these features using the RICE framework. For each feature, help me estimate:

  1. Reach: How many users will this impact per month?
  2. Impact: How much will this impact each user? (Scale: 0.25=minimal, 0.5=low, 1=medium, 2=high, 3=massive)
  3. Confidence: How confident are we in our estimates? (Scale: 0-100%)
  4. Effort: How many person-months will this take to build?

Then calculate the RICE score: (Reach × Impact × Confidence) / Effort

Features to evaluate: [List your features with any context you have]

Best Practices

  • Gather data on current user behavior before estimating Reach
  • Base Impact on user research and pain point severity
  • Be honest about Confidence levels - lower confidence for assumptions
  • Include design, development, and testing time in Effort estimates
  • Revisit estimates after initial discovery work
  • Consider dependencies between features

Example

Input: 5 features (notifications, dark mode, API access, mobile app, analytics dashboard) Output: RICE scores calculated for each, ranked list with reasoning, recommendations on which to prioritize, and suggestions for validating assumptions on low-c...

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.426 reviews
  • X
    Xiao KhannaDec 24, 2024

    Solid pick for teams standardizing on skills: feature-prioritization-assistant is focused, and the summary matches what you get after install.

  • D
    Dhruvi JainDec 12, 2024

    Registry listing for feature-prioritization-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Y
    Yuki HaddadDec 12, 2024

    I recommend feature-prioritization-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Y
    Yuki YangNov 27, 2024

    Useful defaults in feature-prioritization-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • K
    Kiara SanchezNov 15, 2024

    Registry listing for feature-prioritization-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • O
    OshnikdeepNov 3, 2024

    Solid pick for teams standardizing on skills: feature-prioritization-assistant is focused, and the summary matches what you get after install.

  • G
    Ganesh MohaneOct 22, 2024

    I recommend feature-prioritization-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • S
    Sakura MalhotraOct 18, 2024

    Registry listing for feature-prioritization-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • P
    Pratham WareOct 6, 2024

    feature-prioritization-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • H
    Henry RaoOct 6, 2024

    Useful defaults in feature-prioritization-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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