querying-posthog-data
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Also the first stop for a governed business number (MRR, activation, revenue): check the semantic layer (canonical metrics in system.information_schema.metrics) for an approved definition before deriving from raw events. Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
In `SKILL.md`, this skill instructs using `posthog:execute-sql` to query PostHog’s system/captured tables for discovery and analytics, and those SQL queries can read LLM trace content fields (e.g., `$ai_input`/`$ai_output`) from user-instrumented `posthog.ai_events` / `events` rather than only trusted first-party help text.
- Repository
- PostHog/posthog
- Commit
5b94e47
- Audited
- Security analysis
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