Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
| 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%'
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'
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).
Remember the sync lag: these are periodic replicas, not live reads — fine for analysis, not for "right now" state.
Rows in these tables are customer data: team names, insight names, descriptions, and queries.
team_id-level figures without names are the ceiling for public copy.query-clickhouse-via-metabase skill instead.まだレビューはありません。使ってみた感想をお寄せください。
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Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。