Analyze PostHog MCP data to surface product insights and recommendations.
npx skills add https://github.com/petekp/agent-skills --skill posthog-analytics
Please help me install this Agent Skill. Skill: posthog-analytics Source: https://github.com/petekp/agent-skills/tree/main/skills/posthog-analytics Command: npx skills add https://github.com/petekp/agent-skills --skill posthog-analytics
PostHog data is a goldmine for product teams but translating it into actionable decisions can be slow and opaque.
Ask it to analyze recent user journeys and surface top insights from PostHog MCP.
High-intent search queries and answers about installing and using this skill.
To turn PostHog analytics into actionable product insights, you query events and properties via PostHog MCP endpoints to surface user journeys, retention, and conversion patterns. The analysis generates trends, funnels, and dashboards to propose data-informed recommendations with evidence.
Building dashboards for feature adoption with PostHog involves fetching event data through MCP endpoints to track activation and user journeys. You can generate visual trend insights and funnel analyses to monitor how features are adopted and present recommendations based on that evidence.
Analyzing user journeys and retention in PostHog requires querying event and property data via MCP endpoints to identify behavioral patterns. The process surfaces top insights regarding user activation, conversion drop-offs, and retention rates to guide product decisions.
Yes, you can run experiments and funnel analyses using PostHog MCP by fetching event data to measure conversion and performance. The process generates funnel insights and trend data to evaluate experiment outcomes and propose data-informed product decisions.
No, answering PostHog analytics questions does not require manual querying because the process leverages PostHog MCP endpoints automatically. It fetches the relevant events and properties, generates insights, and presents evidence-based recommendations without manual SQL or query construction.
With PostHog MCP data, you can answer product analytics questions regarding user journeys, retention, conversion, feature adoption, and performance. The analysis surfaces patterns from events and properties to build dashboards and propose data-informed product decisions.
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