product-manager-toolkit

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

1|2|Updated Mar 5, 2026
One-click install
npx skills add https://github.com/zhizhunbao/textbook-rag --skill product-manager-toolkit-zhizhunbao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: product-manager-toolkit
Source: https://github.com/zhizhunbao/textbook-rag/tree/main/.agent/skills/dev-product_manager
Command: npx skills add https://github.com/zhizhunbao/textbook-rag --skill product-manager-toolkit-zhizhunbao

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Product managers struggle to objectively rank feature requests, synthesize customer interview feedback, and produce consistent requirement documents. This toolkit automates RICE-based prioritization, 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 from a CSV file, with portfolio analysis (quick wins vs big bets) and quarterly roadmap generation based on team capacity. - Interview Analysis: Parse customer interview transcripts to extract pain points with severity, feature requests with priority, jobs-to-be-done, sentiment scores, key themes, and competitor mentions. - PRD Templates: Choose from Standard PRD, One-Page PRD, Agile Epic, and Feature Brief formats for requirement documentation. - Use Case: Export your feature backlog to CSV, run the RICE prioritizer with your quarterly capacity, then analyze recent user interview transcripts to validate which pain points the top-ranked features address. ## Quick Start Ask the AI to run the RICE prioritizer on your features CSV with a quarterly capacity of 15 person-months and summarize the top priorities.

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, ranks features, and outputs portfolio analysis plus a quarterly roadmap based on your team capacity.

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: 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.

Does the interview analyzer support multiple transcripts at once?▼

The command-line interface analyzes one transcript per run, but the module includes an aggregate_interviews function that combines multiple analysis results to find common pain points, themes, and sentiment across interviews.

What are the limitations of keyword-based interview analysis?▼

The analyzer matches predefined keyword and regex patterns, so it can miss nuanced phrasing, sarcasm, or domain-specific language. It works best as a first-pass synthesis tool, with manual review of extracted quotes for accuracy.