Product Discovery Frameworks in the PM Skills Plugin: A Complete Guide to ICE, RICE, OST, and More

The pm-product-discovery plugin implements seven product discovery frameworks including ICE, RICE, Opportunity Solution Tree, North Star, and Google HEART, each accessible through specific skill commands.

The phuryn/pm-skills repository contains a dedicated pm-product-discovery plugin that embeds battle-tested product management methodologies directly into its skill system. Each framework is codified in markdown skill files that guide users through structured decision-making processes. Understanding which product discovery frameworks are implemented within the PM Skills plugins enables product teams to leverage these methodologies for prioritization, discovery, and metrics tracking without leaving their workflow.

Prioritization Frameworks: ICE and RICE

The plugin implements two complementary scoring models for backlog prioritization and assumption testing.

ICE (Impact × Confidence × Ease) appears in both prioritize-features.md and prioritize-assumptions.md. This framework calculates a priority score by multiplying three factors: the potential impact of a feature or assumption, the confidence level in that impact, and the ease of implementation. It surfaces high-impact items that can be executed quickly.

RICE (Reach × Impact × Confidence ÷ Effort) extends ICE with a Reach factor, making it ideal for larger teams or products with diverse user segments. The formula divides the product of Reach, Impact, and Confidence by the required Effort. Both frameworks are located in pm-product-discovery/skills/prioritize-features/SKILL.md and pm-product-discovery/skills/prioritize-assumptions/SKILL.md.

Discovery Mapping: Opportunity Solution Tree

The Opportunity Solution Tree (OST) framework provides a visual mapping methodology for continuous discovery. Defined in pm-product-discovery/skills/opportunity-solution-tree/SKILL.md, OST creates a hierarchical structure connecting desired product outcomes to customer opportunities, possible solutions, and validation experiments. This ensures teams map solutions back to actual customer needs rather than building features in isolation.

Metrics Frameworks: North Star and Google HEART

For measurement and analytics, the plugin bundles two distinct metric frameworks within pm-product-discovery/skills/metrics-dashboard/SKILL.md.

The North Star Framework helps teams define a single, high-level metric that represents the core value delivered to customers. This metric aligns cross-functional teams around product-level success rather than vanity metrics.

The Google HEART Framework provides a user-centric measurement system covering Happiness, Engagement, Adoption, Retention, and Task Success. It offers a comprehensive view of user experience quality for product analytics dashboards.

Meta-Skills and Resource Libraries

Beyond individual frameworks, the plugin includes a Prioritization-frameworks skill that aggregates ICE, RICE, and other scoring approaches into reusable templates. Additionally, prioritize-features.md references a Product-Management Frameworks Compendium linking to a curated collection of additional methodologies and ready-to-use templates.

How to Invoke Product Discovery Frameworks

Each framework is accessible through specific command invocations documented in the commands/ folder. The commands automatically load the appropriate methodology from the corresponding skill files.

To prioritize a feature backlog using the RICE framework:

/triage-requests

This command prompts for a list of features, then applies the RICE formula (Reach × Impact × Confidence ÷ Effort) to generate a ranked priority table.

To build an Opportunity Solution Tree:

/discover

This launches the opportunity-solution-tree skill, walking through the outcome → opportunity → solution → experiment mapping sequence.

To design a metrics dashboard using the North Star or HEART frameworks:

/setup-metrics

This guides users to select a North Star metric and optionally incorporate HEART framework layers for comprehensive analytics.

Summary

• ICE and RICE prioritization frameworks are implemented in prioritize-features.md and prioritize-assumptions.md for scoring features and assumptions.
• Opportunity Solution Tree provides visual discovery mapping in opportunity-solution-tree.md.
• North Star and Google HEART frameworks support metrics definition in metrics-dashboard.md.
• Commands /triage-requests, /discover, and /setup-metrics invoke these frameworks directly.
• The plugin includes a meta-skill aggregating prioritization methods and links to an external frameworks compendium.

Frequently Asked Questions

How do I choose between ICE and RICE when prioritizing features?

Use ICE when you need quick, lightweight scoring for small teams or early-stage products where reach is uniform across all users. Use RICE when your product serves diverse user segments or when you need to account for how many users each feature will affect, as the Reach factor provides crucial differentiation in the calculation.

Can I use multiple frameworks simultaneously in the same project?

Yes. The pm-product-discovery plugin is designed for framework interoperability. You can use /discover to map opportunities with the Opportunity Solution Tree, then use /triage-requests to prioritize solutions using RICE scoring, and finally use /setup-metrics to track success via the North Star framework.

Where are the framework definitions stored in the repository?

Framework implementations reside in skill-specific markdown files under pm-product-discovery/skills/. ICE and RICE live in prioritize-features/SKILL.md and prioritize-assumptions/SKILL.md, OST in opportunity-solution-tree/SKILL.md, and metrics frameworks in metrics-dashboard/SKILL.md.

What is the Prioritization-frameworks skill mentioned in the source files?

This is a meta-skill that aggregates ICE, RICE, and other scoring templates into a single reference point. It provides reusable formulas and calculation methods that both prioritize-features and prioritize-assumptions skills can reference, ensuring consistency in scoring methodology across the plugin.

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