数据库
数据库管理与查询
monitoring-ingestion-pipeline
PostHog
Guide for using the Grafana MCP to monitor and diagnose the Node.js ingestion pipeline workers in production. Use when investigating event lag, drops, pipeline errors, person/group processing, Kafka consumer health, Redis, Postgres, ClickHouse downstream health, or any ingestion worker question. Covers prod-us and prod-eu environments.
modeling-conversion-metrics
PostHog
Build reusable conversion models — funnel/step conversion rates, drop-off, and time-to-convert — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute a conversion rate, funnel, step completion, drop-off, activation-funnel, signup-to-paid, or any "what % of users who did A went on to do B (within N days)" metric. Covers the funnel model (ordered steps, the conversion-window time-box, strict vs any-order), the person-vs-group aggregation unit, overall vs step-to-step conversion (two different numbers), breakdown attribution, and when a saved funnel insight beats a warehouse view. On PostHog, model funnels in HogQL with windowFunnel; in dbt, stage the event stream and compute an fct_conversion mart with tests. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics; pairs with query-funnel for interactive analysis.
analyzing-insights-across-teams
PostHog
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.
manage-dashboard-widgets
PostHog
Guides PostHog engineers through dashboard widget platform work — ship a new widget_type (WIDGET_REGISTRY, catalog, run_widgets, WidgetCard) or update a shipped type (config, query, layout, RBAC, tile filter bar, list footer, titleHref, throttles). Use for WidgetSpec, widget_specs/, widget-configs.zod.ts, hogli build:openapi, error_tracking_list, session_replay_list, widgetFilters, formatWidgetListCountFooter, widget_query_throttle, or WidgetCard composition. New types need widget-intake confirmation first. Not for MCP batch-add of existing types or adding tiles to a dashboard.
exploring-llm-clusters
PostHog
'Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.'
warehouse-source-new-version
PostHog
Add support for a new vendor API version to an existing Data warehouse import source, or deprecate an old one. Use when a vendor ships a new API version (Stripe date versions, Shopify quarterly versions, header-pinned revisions, /vN/ URL bumps), when implementing a version-update or deprecation task for a source under products/warehouse_sources/backend/temporal/data_imports/sources, or when repinning an ExternalDataSource to a different version. Covers deciding whether a newly announced version needs supporting at all, version declaration, dispatch, pinning semantics, deprecation metadata, and migration scripts.
modifying-taxonomic-filter
PostHog
Guides safe changes to the TaxonomicFilter, PostHog's picker for events, actions, properties, cohorts, and more. Use when adding features, fixing bugs, improving search or loading performance, or refactoring the classic picker, rebuild menu, or headless filter panel. Covers real selection behavior, two live surfaces, shared telemetry, the conditional Postgres search plan, and result reveal rules.
django-migrations
PostHog
Django migration patterns and safety workflow for PostHog. Use when creating, adjusting, or reviewing Django/Postgres migrations, including non-blocking index/constraint changes, multi-phase schema changes, data backfills, migration conflict rebasing, and product model moves that require SeparateDatabaseAndState. Also use for any deletion or removal of a model, table, column, product, or app — including deleting migration files or retiring a feature — even when no migration is written.
writing-clickhouse-queries
PostHog
Guide for writing performant ClickHouse queries in PostHog product code. Use when writing HogQL query runners, designing a ClickHouse table for a new product, adding materialized columns or skip indexes, or choosing a row ID format. For optimizing an existing query that is already too slow, use /optimizing-clickhouse-and-hogql-queries instead.
adding-personhog-rpc
PostHog
Guide for adding a new RPC to personhog-replica and personhog-router. Covers eligibility checks, proto definition, code generation for Python and Node.js clients, Rust implementation (storage trait, postgres queries, service handler, router wiring), and index compatibility validation. Use when adding a new gRPC endpoint to personhog, migrating a Django ORM query to personhog, or extending the personhog service API.
generating-clickhouse-query-performance-reports
PostHog
Produce and structure slow-query performance reports for PostHog's production ClickHouse (US and EU). Use when asked for a slow query report, query performance analysis over the last N days, per-team query cost, OOM or timeout investigation, cluster cost/memory regressions, or materialization candidates. Covers the modern query_log_archive source (typed lc_* columns, multi-day retention), how to categorize and attribute slow queries, root-cause patterns (unmaterialized JSONExtract, high-cardinality breakdowns, heavy joins), and the report structure. Runs queries via the querying-production-databases-via-metabase skill.
modeling-dimension-tables
PostHog
Build reusable dimension / lookup tables for a star schema — country/region, timezone, currency, date, plan/product, and other descriptive attributes — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model dimension tables, lookup tables, a star schema, conformed dimensions, or wants to enrich events/revenue/usage with country, region, timezone, plan, or currency attributes without repeating JOINs. Covers sourcing the dimension data (upload, warehouse source, or derive from events), shaping it into an aliased one-row-per-entity view (optionally materialized on a slow schedule since dimensions change rarely), and attaching it to facts via a saved or person join so its columns read as native fields. Key rule: for currency use the built-in convertCurrency() instead of a hand-rolled rate table. Read modeling-warehouse-foundations first; dimensions here are reused by the revenue, conversion, activation, and product-usage modeling skills.
metabase-data-app-actions
metabase
Use when a Metabase data app needs to trigger a write or mutation — submitting a form, updating a row, deleting an entry, running a saved action, or any "do something" interaction. Covers invoking an existing action via useAction, parameter typing, response handling, and the critical post-action refresh of any UI data the action may have changed.
serdes-workflow
metabase
Export content from a running Metabase instance, validate with checkers, edit YAML, and import back. Use when the user wants to export, import, or run the full serdes round-trip workflow.
drizzle
lobehub
'LobeHub Drizzle ORM schema and query style. Use for pgTable schemas, indexes, joins, inferred types, db.select/db.query, schema fields, foreign keys, junction tables, or postgres query patterns.'
implementing-warehouse-sources
PostHog
Implement and extend PostHog Data warehouse import sources. Use when adding a new source under products/warehouse_sources/backend/temporal/data_imports/sources, adding datasets/endpoints to an existing source, or adding incremental sync, resumable imports, webhook ingestion, pagination, credentials validation, and source tests.
modeling-product-usage-metrics
PostHog
Build reusable product-usage and engagement models — retention, stickiness, and lifecycle — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute whether users come back (retention / churn), how frequently they engage (stickiness / power users / DAU-WAU-MAU ratio), or the composition of the active base (new / returning / resurrecting / dormant lifecycle). These three are one engagement family sharing a start-event/return-event vocabulary and an interval granularity; this skill treats them together and helps pick the right lens: retention for the return-rate cohort matrix, stickiness for the frequency distribution, lifecycle for growth quality. On PostHog, model them in HogQL (mirroring query-retention / query-stickiness / query-lifecycle); in dbt, build fct_retention / fct_stickiness / fct_lifecycle marts with tests. Read modeling-warehouse-foundations first; feeds the retention validation used by modeling-activation-metrics.
querying-local-postgres
PostHog
Run read-only SQL against the local Postgres app database (SELECT, EXPLAIN, EXPLAIN ANALYZE on SELECT). Default local URL postgres://posthog:posthog@localhost:5432/posthog; else DATABASE_URL. Use when querying the local DB, inspecting tables, debugging data, or analyzing query plans. Mutations are strictly forbidden.
setting-up-data-catalog
PostHog
Populates and maintains a project's data catalog (semantic layer): canonical metrics, trust marks (certifications) on warehouse tables/views, and reviewed table relationships. Use when asked to set up / seed / bootstrap the data catalog or semantic layer, to catalog a project's metrics, to certify or deprecate data sources, to propose or review table joins, or to work through the proposal review queue. To use an existing catalog to answer a business-number question, see querying-posthog-data instead. Trigger terms: data catalog, semantic layer, canonical metric, certify table, deprecate source, relationship proposal, metric drift, review queue.
cpu-profile
ClickHouse
Profile a ClickHouse query using the sampling query profiler and system.trace_log. Use when the user wants to find CPU hotspots, analyze where time is spent in a query, or investigate performance bottlenecks.
create-test-datasets
ClickHouse
Create test datasets (hits, visits, tpcds, tpch) from standard scripts. Ensures the server is running first.
graincrawl
openclaw
"Granola archive: search, sync freshness, notes, transcripts, panels, SQL counts, and Graincrawl repo work."
update-team-server
openclaw
"Update the operator-configured Team server through its canonical owner; trust native validation and acceptance without duplicate checks or schedulers."
writing-tests
PostHog
Gates whether a new test should exist and forces it to be efficient, protecting CI from low-value test bloat. Use before any change to what a pytest, Jest, or Playwright test asserts or sets up, down to one fixture or one assertion added to an existing block. Front-loads the value bar (every test must catch a realistic regression no existing test already catches; extend the nearest existing test before writing a new standalone one; test behavior through the public interface, not implementation details; collapse near-duplicates into parameterized cases) and the efficiency bar (deterministic, isolated, fast; pick the cheapest test level; Django TestCase over TransactionTestCase; no sleeps, no real network; no time bombs from absolute dates left to age against the real clock). Includes a "don't write it" decision tree. For fixing an existing flaky test use /fixing-flaky-tests; after this gate says a Playwright test is warranted, use /playwright-test for mechanics.