Query PostHog analytics data using HogQL and SQL foundations.
npx skills add https://github.com/PostHog/ai-plugin --skill querying-posthog-data
Please help me install this Agent Skill. Skill: querying-posthog-data Source: https://github.com/PostHog/ai-plugin/tree/main/skills/querying-posthog-data Command: npx skills add https://github.com/PostHog/ai-plugin --skill querying-posthog-data
PostHog data is rich but often requires guidance to search, filter, and aggregate effectively. This skill provides structured guidance on HogQL/SQL usage, system schemas, and query patterns to help users query analytics data across insights, dashboards, cohorts, feature flags, experiments, data warehouse, and more.
Read the guidelines in references/guidelines.md and run a basic HogQL query against the events table to validate connectivity and syntax.
High-intent search queries and answers about installing and using this skill.
To write HogQL queries for PostHog data discovery, apply SQL foundations to filter and aggregate events. Follow schema references and time-range discipline to ensure valid syntax and safe querying workflows across your analytics data.
Use HogQL instead of standard SQL when querying PostHog-specific entities like events, dashboards, cohorts, and feature flags. HogQL provides tailored functions and schema access for precise analytics, whereas standard SQL handles external data warehouse tables.
PostHog data schemas cover events, insights, dashboards, cohorts, feature flags, and experiments. Referencing these schemas and available HogQL functions speeds up query construction and ensures accurate filtering for trends, funnels, retention, and paths.
Yes, you can query web analytics, errors, logs, and LLM traces using HogQL. The skill provides query patterns and data schema references to help you search, filter, and aggregate these specific data types effectively within PostHog.
Safe querying workflows in PostHog require enforcing time-range discipline and validating data schemas before execution. Following best-practice patterns for post-hoc analytics prevents errors and ensures precise discovery across your events and insights.
Querying PostHog data with HogQL does not strictly require a data warehouse for native events and insights, but schema references and query patterns also support data warehouse integration if you need to analyze external data sources.
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