Streamline Product Management with AI-Powered Frameworks

Product Manager Toolkit bundles tools and frameworks covering the full product management workflow, from discovery to delivery. Two Python scripts do the heavy lifting: rice_prioritizer.py scores a feature CSV (name, reach, impact, confidence, effort) using the RICE formula - Score = (Reach x Impact x Confidence) / Effort, with impact weighted massive/high/medium/low/minimal (3x down to 0.25x) and confidence weighted high/medium/low (100%/80%/50%) - and adds portfolio balance analysis (quick wins vs. big bets), quarterly roadmap generation, and team capacity planning, with text/JSON/CSV output; customer_interview_analyzer.py runs NLP-based analysis on an interview transcript to extract pain points with severity, feature requests, jobs-to-be-done patterns, sentiment, themes, competitor mentions, and key quotes.

Around those scripts sit three core workflows. Feature prioritization gathers requests from customers, sales, tech debt, and strategy, scores them with RICE, reviews the resulting portfolio against strategy, and generates a capacity-planned roadmap. Customer discovery runs semi-structured, problem-focused interviews, analyzes them with the interview script, synthesizes findings into opportunity areas, then validates solution hypotheses against prototypes. PRD development picks from four templates in references/prd_templates.md - a comprehensive 11-section Standard PRD for major features, a concise One-Page PRD for smaller ones, a sprint-based Agile Epic template, and a lightweight, hypothesis-driven Feature Brief for pre-PRD exploration - structures each as Problem to Solution to Success Metrics with an explicit out-of-scope section, and collaborates across engineering, design, sales, and support before finalizing.

Supporting frameworks include the Value vs. Effort matrix (quick wins, big bets, fill-ins, time sinks) and MoSCoW (Must/Should/Could/Won't Have) for prioritization; a timed customer interview guide, a "We believe / For / Will / We'll know" hypothesis template, and an opportunity solution tree for discovery; and a North Star Metric framework, a funnel-analysis template (acquisition through referral), and feature-success metrics (adoption, frequency, depth, retention, satisfaction) for measurement. A best-practices set covers writing PRDs (problem before solution, success metrics upfront, visuals over prose), effective prioritization (mixing quick wins with strategic bets, a 20% buffer for unexpected work), customer discovery (asking why five times, focusing on past behavior over stated intentions), and stakeholder management (RACI, async updates, demos over documentation). It also names six recurring pitfalls - solution-first thinking, analysis paralysis, the feature factory, ignoring technical debt, surprising stakeholders, and optimizing vanity "metric theater" over real value.

Use it for the standard product-management workflow: prioritizing a feature backlog, running and synthesizing customer discovery interviews, and writing a PRD at the right level of formality for the feature's size. It is not for tasks outside product management, or a different domain or tool entirely - and it stops to ask for clarification if required inputs, permissions, or success criteria are missing rather than guessing.

Input is a feature-request CSV for prioritization, an interview transcript for discovery analysis, or discovery findings for PRD drafting. Output is a RICE-scored, capacity-planned roadmap; a structured extraction of pain points, feature requests, jobs-to-be-done, and sentiment from an interview; or a populated PRD in the template matching the feature's scope - none of it a substitute for environment-specific validation or expert review.

The toolkit is built to sit alongside analytics tools (Amplitude, Mixpanel, Google Analytics), roadmapping tools (ProductBoard, Aha!, Roadmunk), design tools (Figma, Sketch, Miro), development trackers (Jira, Linear, GitHub), research tools (Dovetail, UserVoice, Pendo), and communication tools (Slack, Notion, Confluence).

Product managers who want a scriptable, opinionated toolkit for RICE-based prioritization, interview-driven discovery, and right-sized PRD writing, rather than assembling frameworks and templates from scratch.