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product-manager-toolkit

Run RICE prioritization and analyze customer interview transcripts with Python scripts.

Updated Dec 19, 2025
Search Tags:#product management#prioritization#PRD#product strategy#customer research#RICE framework
One-click install
npx skills add https://github.com/Spectaculous-Code/raamattu-nyt --skill product-manager-toolkit-spectaculous-code
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: product-manager-toolkit
Source: https://github.com/Spectaculous-Code/raamattu-nyt/tree/main/.claude/skills/product-manager-toolkit
Command: npx skills add https://github.com/Spectaculous-Code/raamattu-nyt --skill product-manager-toolkit-spectaculous-code

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, nltk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines product management workflows, from initial discovery and prioritization to documentation and go-to-market strategy, enabling more efficient and data-driven product decisions.

Core Features & Use Cases

  • Feature Prioritization: Use the RICE framework to objectively score and rank potential features.
  • Customer Insights: Analyze interview transcripts to uncover pain points, feature requests, and jobs-to-be-done.
  • Documentation: Provides templates for PRDs, Epics, and Feature Briefs.
  • Use Case: A product manager needs to decide which features to build next quarter. They can use the rice_prioritizer.py script to input feature details and get a ranked list, then use the customer_interview_analyzer.py script to synthesize feedback from recent user interviews.

Quick Start

Use the product manager toolkit to prioritize features listed in the 'new_features.csv' file.

Frequently Asked Questions about product-manager-toolkit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use the RICE framework to prioritize product features?▼

To prioritize features with the RICE framework, input your feature details into a CSV file and run the Python prioritization script to calculate scores and output a ranked list for objective product decision-making.

What is the best way to synthesize customer interview transcripts for product research?▼

The best way to analyze customer interview transcripts is using NLP to uncover pain points, feature requests, and jobs-to-be-done, generating actionable user insights for your product strategy.

How do I write a PRD using structured templates?▼

You can write a PRD using provided Markdown templates for PRDs, Epics, and Feature Briefs, enabling efficient requirement documentation and product discovery framework standardization.

Do I need Python and pandas to run RICE prioritization scripts?▼

Yes, you need Python with pandas and nltk installed to execute the RICE prioritizer and customer interview analyzer, as the toolkit leverages structured data input and NLP analysis for feature scoring.

Can I automate feature scoring from a CSV file for my product roadmap?▼

Yes, you can automate feature scoring by running the Python RICE prioritization script against a new_features.csv file, producing a ranked list to guide your next quarter product roadmap decisions.

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