Analyze user-behavior data from PostHog or Amplitude and write findings to .nanopm/DATA.md.
npx skills add https://github.com/nmrtn/nanopm --skill pm-data-nmrtn
Please help me install this Agent Skill. Skill: pm-data Source: https://github.com/nmrtn/nanopm/tree/main/pm-data Command: npx skills add https://github.com/nmrtn/nanopm --skill pm-data-nmrtn
Product managers often need fast, reliable quantitative answers to specific questions about user behavior. This Skill analyzes data from analytics tools (PostHog or Amplitude) and produces a structured DATA.md report to ground decision-making.
Ask a precise product question and run the /pm-data workflow to generate DATA.md with the analysis results.
High-intent search queries and answers about installing and using this skill.
You can analyze user retention and funnel data from PostHog by running an automated product analytics workflow. The system connects via PostHog API or MCP, automatically derives analysis type and time range, and writes a structured DATA.md report with confidence levels and recommended next steps.
Yes, the Amplitude API supports answering focused product questions about user behavior trends. The analysis workflow automatically determines key metrics and time range from your question, producing an interpretable narrative with confidence levels and writing the results to a structured DATA.md report.
The best way to generate a quantitative data report for product management is using an automated analytics workflow connecting to PostHog or Amplitude. It automatically derives metrics, time ranges, and analysis types from your product questions, writing an interpretable narrative with confidence levels directly to a DATA.md file.
No, you do not need a PostHog MCP connection to analyze cohort differences and user paths. The workflow supports PostHog MCP, PostHog API, Amplitude API, or manual input, allowing you to analyze user behavior paths and cohort differences using whichever data input method is currently available.
To structure a DATA.md report for product review, the workflow automatically generates an interpretable narrative with confidence levels and recommended next steps. It writes the quantitative findings for funnels, retention, trends, paths, or cohorts directly to the .nanopm/DATA.md file path.
When using manual input for product analytics data analysis, you are limited by the data provided directly. The workflow still automatically derives the analysis type and key metrics to produce an interpretable narrative with confidence levels, but accuracy depends entirely on the completeness of your manual data input.
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