insights/.agents/skills/analyzing-insights-across-teams/SKILL.md

name, description

name

description

analyzing-insights-across-teams

Analyze Insights insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "Insights 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 insights:execute-sql. Do not stop at system.insights when the question spans teams.

This skill is deliberately repo-local (.agents/skills/): it documents Insights'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.insights_dashboarditem	eu_postgres_insights_dashboarditem
Dashboards	postgres.insights_dashboard	eu_postgres_insights_dashboard
Teams / projects	postgres.insights_team	eu_postgres_insights_team

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

• These are replicas of the Django tables in this repo (insights_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.insights_dashboarditem'
   

2. Query with insights: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.insights_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.insights_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 query-datastore-via-metabase skill instead.