product-manager-toolkit

Prioritize features with RICE scoring and analyze customer interview transcripts using Python scripts.

1|Updated May 10, 2026
One-click install
npx skills add https://github.com/Tgoldi/claude-skills --skill product-manager-toolkit-tgoldi
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: product-manager-toolkit
Source: https://github.com/Tgoldi/claude-skills/tree/main/product-manager-toolkit
Command: npx skills add https://github.com/Tgoldi/claude-skills --skill product-manager-toolkit-tgoldi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Product managers struggle to objectively prioritize feature backlogs, synthesize customer interview findings, and produce consistent requirement documents. This toolkit automates RICE scoring, extracts insights from interview transcripts, and provides ready-to-use PRD templates. ## Core Features & Use Cases - RICE Prioritization: Score features by Reach, Impact, Confidence, and Effort, with portfolio analysis distinguishing quick wins from big bets and capacity-based quarterly roadmap generation. - Interview Analysis: Extract pain points, feature requests, jobs-to-be-done, sentiment, themes, and competitor mentions from customer interview transcripts. - PRD Templates: Four formats (Standard PRD, One-Page PRD, Agile Epic, Feature Brief) for different feature scopes and delivery timelines. - Use Case: A PM with 20 feature requests in a CSV runs the RICE prioritizer with a 15 person-month capacity to produce a ranked list and quarterly roadmap, then analyzes five interview transcripts to validate the top priorities. ## Quick Start Run the RICE prioritizer on my features CSV with a team capacity of 15 person-months and show the suggested quarterly roadmap.

Frequently Asked Questions about product-manager-toolkit

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

FAQPage Schema
How do I prioritize features with the RICE framework in Python?

Create a CSV with columns name, reach, impact, confidence, and effort, then run rice_prioritizer.py on it. The script calculates RICE scores as (Reach x Impact x Confidence) / Effort and outputs a ranked feature list with portfolio analysis.

How to analyze customer interview transcripts for pain points?

Run customer_interview_analyzer.py on a plain-text transcript file. It extracts pain points with severity levels, feature requests with priority, jobs-to-be-done patterns, sentiment scores, key themes, and competitor mentions, outputting text or JSON.

What CSV format does the RICE prioritizer require?

The CSV needs columns for name, reach (users per quarter), impact (massive/high/medium/low/minimal), confidence (high/medium/low), and effort (xl/l/m/s/xs person-months). Run the script with 'sample' as input to generate an example file.

Can the RICE tool generate a quarterly roadmap?

Yes, pass the --capacity flag with your team's person-months per quarter. The script packs prioritized features into quarters until capacity is exhausted, showing which features fit each quarter.

What are the limitations of the interview analyzer?

The analyzer uses keyword and regex pattern matching rather than machine learning, so it may miss nuanced phrasing or produce false positives. Results are capped at the top 10 pain points and requests, and sentiment is a simple signal-count ratio.