product-manager-toolkit

Prioritize feature ideas from interview transcripts by extracting pain points and jobs-to-be-done, then calculating RICE scores to generate a capacity-aware quarterly roadmap. This toolkit uses Python scripts and markdown templates to synthesize discovery insights into ranked feature lists.

Extract pain points and feature requests from user research transcripts using the interview analyzer to identify themes, sentiment scores, jobs-to-be-done, and key quotes. The analysis output directly feeds into downstream PRD drafting and feature prioritization workflows.

Create a capacity-aware quarterly roadmap from candidate features by running the RICE prioritizer to calculate scores and analyze portfolio balance. The toolkit produces a quarter-by-quarter plan and exportable reports for stakeholder review.

Draft a PRD from validated product discovery insights by applying the included one-page PRD templates and markdown formats. These templates convert synthesized customer pain points and ranked features into clear requirements and acceptance criteria.

Python scripts are included to calculate RICE scores, analyze portfolio balance, and generate exportable reports for product management. The scripts automate the prioritization process to produce capacity-aware quarterly roadmaps from feature lists.

Synthesizing user research for product discovery works best with text-based interview transcripts to extract sentiment and themes. The toolkit focuses on RICE prioritization and PRD generation, so it does not handle quantitative survey data or real-time customer feedback analysis.