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name: analyzing-insights-across-teams description: > Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard usage — cases where system.insights only returns the current project's rows and the agent would otherwise report the data as inaccessible. Covers the synced table names for US and EU and the column-verification workflow.

Analyzing insights across teams

system.* entity tables (e.g. system.insights) are scoped to the current project, and the generic execute-sql guidance says other teams' data is inaccessible. For the dogfood project (US project 2) that is not the whole story: production Postgres tables are replicated into the project's data warehouse, so cross-team entity metadata is queryable with posthog:execute-sql. Do not stop at system.insights when the question spans teams.

This skill is deliberately repo-local (.agents/skills/): it documents PostHog's internal dogfood setup, applies only to agents working in this repo, and must not move into the packaged products/*/skills/ bundle that ships to every team.

Synced tables

Entity	US (prod-us)	EU (prod-eu)
Insights	postgres.posthog_dashboarditem	eu_postgres_posthog_dashboarditem
Dashboards	postgres.posthog_dashboard	eu_postgres_posthog_dashboard
Teams / projects	postgres.posthog_team	eu_postgres_posthog_team

• Underscore aliases (e.g. postgres_posthog_dashboarditem) point at the same synced data.

• These are replicas of the Django tables in this repo (posthog_dashboarditem backs the Insight model), so rows span every team; team_id is the scoping column.

• More prod tables than these are synced. Before concluding cross-team data is inaccessible, check the catalog:

  SELECT table_name, description
  FROM system.information_schema.tables
  WHERE table_type = 'data_warehouse' AND table_name ILIKE '%postgres%'
  

Workflow

1. Confirm columns before projecting — synced schemas drift with the Django models:

   SELECT column_name, data_type
   FROM system.information_schema.columns
   WHERE table_name = 'postgres.posthog_dashboarditem'
   

2. Query with posthog:execute-sql, filtering or grouping by team_id. Example — most active teams by insights created in the last 30 days:

   SELECT team_id, count() AS insights_created
   FROM postgres.posthog_dashboarditem
   WHERE NOT deleted AND saved AND created_at >= now() - INTERVAL 30 DAY
   GROUP BY team_id
   ORDER BY insights_created DESC
   LIMIT 20
   

   Join postgres.posthog_team on id = team_id for team names only when the output stays on an internal surface (see below).

3. Remember the sync lag: these are periodic replicas, not live reads — fine for analysis, not for "right now" state.

Output handling (required)

Rows in these tables are customer data: team names, insight names, descriptions, and queries.

• Never put customer team names, insight titles, or other row-level metadata on public surfaces — PR titles/descriptions, commit messages, issues, code comments, or uploaded screenshots. Aggregates and team_id-level figures without names are the ceiling for public copy.
• Keep named results in the private conversation, internal docs, or auth-gated links.
• Access is gated by membership in the internal dogfood project. If a query fails with a permissions error, report it and stop — do not look for another route to cross-team data.

Related

• For cross-team event/analytics data (not entity metadata), see the querying-production-databases-via-metabase skill instead.