What sources are used for market research?

The skill utilizes WebSearch and MCP Reference tools to query industry reports from sources like Gartner and Statista, as well as recent news and competitor sites.

How does it handle large numbers of stories without hitting context limits?

It uses a sequential processing pattern, loading and analyzing one story at a time and clearing the detailed context before moving to the next, ensuring high token efficiency.

Does it integrate with project management tools like Linear?

Yes, it is designed to work with the Linear MCP to load Epics and Stories directly from your workspace for analysis.

Can I use this for technical stories or refactoring?

While it can be used, it is primarily designed for business-facing user stories. Purely technical infrastructure tasks often lack the market data required for an accurate RICE/Market score.

How does the skill calculate the RICE score?

It evaluates Reach (users affected), Impact (business value), Confidence (data reliability), and Effort (person-months) to generate a numerical score that determines the P0-P3 priority level.

Story Prioritizer & Market Researcher

Name: Story Prioritizer & Market Researcher
Author: levnikolaevich

bylevnikolaevich

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Productividad y Flujo de Trabajo

Prioritizes user stories using RICE scoring and real-world market research to identify high-impact features.

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The ln-230-story-prioritizer is a sophisticated workflow tool designed to bridge the gap between technical requirements and business value. By integrating RICE (Reach, Impact, Confidence, Effort) scoring with automated market research, it evaluates each user story against real-world data, including market size (TAM) and competitive density. This skill processes stories sequentially to maintain token efficiency, outputting a comprehensive prioritization table that helps teams focus on P0 and P1 tasks that offer the highest strategic ROI. It is ideal for product owners and lead developers who need a data-driven approach to sprint planning and stakeholder reporting.

Características Principales

Automated RICE scoring (Reach, Impact, Confidence, Effort) for every user story
Token-efficient sequential processing for large epics
Consolidated Markdown report generation with P0-P3 priority rankings
Competitive analysis with Blue Ocean vs. Red Ocean classification
38 GitHub stars
Real-time market size and TAM research via WebSearch and industry reports

Casos de Uso

Automating competitive research during the discovery phase of a new product epic
Optimizing sprint planning when engineering capacity is limited
Conducting data-driven ROI analysis for feature validation and stakeholder reviews

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Características Principales

Automated RICE scoring (Reach, Impact, Confidence, Effort) for every user story
Token-efficient sequential processing for large epics
Consolidated Markdown report generation with P0-P3 priority rankings
Competitive analysis with Blue Ocean vs. Red Ocean classification
38 GitHub stars
Real-time market size and TAM research via WebSearch and industry reports

Casos de Uso

Automating competitive research during the discovery phase of a new product epic
Optimizing sprint planning when engineering capacity is limited
Conducting data-driven ROI analysis for feature validation and stakeholder reviews