prioritize-features
This Claude Code skill evaluates and ranks feature backlogs to identify the top five priorities for development. Users should deploy it when making scope decisions, managing product roadmaps, or deciding which feature ideas to pursue next. The skill systematically assesses candidates across impact, effort, risk, and strategic alignment, optionally applying frameworks like Opportunity Score or ICE scoring to guide recommendations with clear trade-offs and rationale.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/prioritize-features && cp -r /tmp/prioritize-features/pm-product-discovery/skills/prioritize-features ~/.claude/skills/prioritize-featuresSKILL.md
## Prioritize Feature Backlog Evaluate and rank a backlog of feature ideas to identify the top 5 to pursue. ### Context You are helping prioritize features for **$ARGUMENTS**. If the user provides files (spreadsheets, backlogs, opportunity assessments), read and analyze them directly. ### Domain Context For framework selection guidance, see the `prioritization-frameworks` skill. Key recommendations: **Opportunity Score** (Dan Olsen, *The Lean Product Playbook*) is recommended for evaluating customer problems: Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1. High Importance + low Satisfaction = best opportunities. Prioritize **problems (opportunities)**, not solutions. **ICE** is recommended for quick scoring of initiatives: Impact (Opportunity Score × # Customers) × Confidence × Ease. **RICE** adds Reach as a separate factor for larger teams. ### Instructions The user will describe their product objective, desired outcomes, and provide feature ideas. Work through these steps: 1. **Understand priorities**: Confirm the product objective and success metrics. 2. **Evaluate each feature** against: - **Impact**: How much does it move the needle on desired outcomes? Consider Opportunity Score if customer data is available. - **Effort**: How much development, design, and coordination is required? - **Risk**: How much uncertainty exists? What assumptions need testing? - **Strategic alignment**: How well does it fit the product vision and current goals? 3. **Recommend the top 5 features** with: - Clear ranking (1-5) - Brief rationale for each selection - Key trade-offs considered - What was deprioritized and why 4. **Present as a prioritization table** if helpful. Think step by step. Save as markdown if the output is substantial. --- ### Further Reading - [Kano Model: How to Delight Your Customers Without Becoming a Feature Factory](https://www.productcompass.pm/p/kano-model-how-to-delight-your-customers) - [The Product Management Frameworks Compendium + Templates](https://www.productcompass.pm/p/the-product-frameworks-compendium) - [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.