[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-posthog-querying-posthog-data":3,"mdc-dt1eof-key":34,"related-org-posthog-querying-posthog-data":802,"related-repo-posthog-querying-posthog-data":974},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"querying-posthog-data","query PostHog data with HogQL and SQL","Required reading before writing any HogQL\u002FSQL 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). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"posthog","PostHog","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fposthog.png",[12,14,17,20],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"Data Analysis","data-analysis",{"name":18,"slug":19,"type":13},"Analytics","analytics",{"name":21,"slug":22,"type":13},"SQL","sql",56,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fskills","2026-05-01T06:03:16.963857",null,4,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"PostHog skills (under construction)","https:\u002F\u002Fgithub.com\u002FPostHog\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fomnibus\u002Fquerying-posthog-data","---\nname: querying-posthog-data\ndescription: 'Required reading before writing any HogQL\u002FSQL 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). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.'\n---\n\n# Querying data in PostHog\n\nThe [guidelines](.\u002Freferences\u002Fguidelines.md) contain the same instructions as `posthog:execute-sql`. If you've already read `posthog:execute-sql`, you don't need to read them again.\n\n## When to use this skill\n\n### Finding a specific PostHog entity\n\nWhen the user wants to find a specific entity created in PostHog (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse items, etc.), or when a list\u002Fsearch tool returns too many results to narrow down:\n\n1. Read the appropriate schema reference under Data Schema to understand the entity's table and columns.\n2. Use `posthog:execute-sql` to query the system table and find the matching entity (typically returning its ID).\n3. Use the dedicated read tool for that entity type (e.g. `posthog:insight-get`, `posthog:dashboard-get`) to retrieve the full entity by ID.\n\nDon't try to reconstruct the entity from SQL — `execute-sql` is for discovery, the read tool is for retrieval.\n\n### Querying analytics data\n\nWhen the user wants analytics data (trends, funnels, retention, paths, sessions, LLM traces, web analytics, errors, logs, etc.) and the existing insight schemas don't fit the request:\n\n1. Look for a matching example under Analytics Query Examples. The list is not exhaustive — there may not be an example for every scenario. If one is a close fit (same domain, similar aggregation), read it; otherwise skip this step.\n2. Adapt the example query (if one was found) to the user's request and run it via `posthog:execute-sql`. If no example fit, compose the query from scratch using the Data Schema and HogQL References.\n\n## Data Schema\n\nSchema reference for PostHog's core system models, organized by domain:\n\n- [Activity logs](.\u002Freferences\u002Fmodels-activity-logs.md)\n- [Actions](.\u002Freferences\u002Fmodels-actions.md)\n- [Alerts](.\u002Freferences\u002Fmodels-alerts.md)\n- [Annotations](.\u002Freferences\u002Fmodels-annotations.md)\n- [APM \u002F tracing (`posthog.trace_spans`)](.\u002Freferences\u002Fmodels-apm-spans.md)\n- [Batch exports](.\u002Freferences\u002Fmodels-batch-exports.md)\n- [Early Access Features](.\u002Freferences\u002Fmodels-early-access-features.md)\n- [Cohorts & Persons](.\u002Freferences\u002Fmodels-cohorts.md)\n- [Dashboards, Tiles & Insights](.\u002Freferences\u002Fmodels-dashboards-insights.md)\n- [Data Warehouse](.\u002Freferences\u002Fmodels-data-warehouse.md)\n- [Data Modeling Endpoints](.\u002Freferences\u002Fmodels-endpoints.md)\n- [Error Tracking](.\u002Freferences\u002Fmodels-error-tracking.md)\n- [Flags & Experiments](.\u002Freferences\u002Fmodels-flags-experiments.md)\n- [Heatmaps (`heatmaps` data + `system.heatmaps_saved`)](.\u002Freferences\u002Fmodels-heatmaps.md)\n- [Hog Flows](.\u002Freferences\u002Fmodels-hog-flows.md)\n- [Hog Functions](.\u002Freferences\u002Fmodels-hog-functions.md)\n- [Integrations](.\u002Freferences\u002Fmodels-integrations.md)\n- [AI observability events (`posthog.ai_events`)](.\u002Freferences\u002Fmodels-ai-observability-events.md)\n- [AI observability reviews](.\u002Freferences\u002Fmodels-ai-observability-reviews.md)\n- [Logs (`logs` data plane + saved views and alerts)](.\u002Freferences\u002Fmodels-logs.md)\n- [Metrics (`posthog.metrics`)](.\u002Freferences\u002Fmodels-metrics.md)\n- [Notebooks](.\u002Freferences\u002Fmodels-notebooks.md)\n- [Session Recording Playlists](.\u002Freferences\u002Fmodels-session-recording-playlists.md)\n- [Session Recordings](.\u002Freferences\u002Fmodels-session-recordings.md)\n- [Support Tickets](.\u002Freferences\u002Fmodels-support-tickets.md)\n- [Surveys](.\u002Freferences\u002Fmodels-surveys.md)\n- [Usage Metrics](.\u002Freferences\u002Fmodels-usage-metrics.md)\n- [SQL Variables](.\u002Freferences\u002Fmodels-variables.md)\n- [Skipped events in the read-data-schema tool](.\u002Freferences\u002Ftaxonomy-skipped-events.md)\n- [Dynamic person and event properties](.\u002Freferences\u002Ftaxonomy-dynamic-properties.md) — patterns like `$survey_dismissed\u002F{id}`, `$feature\u002F{key}` that don't appear in tool results\n\n## HogQL References\n\n- [Person property modes (event-time vs query-time)](.\u002Freferences\u002Fperson-property-modes.md). Read when working with `person.properties.*` to understand if values are historical or current.\n- [Sparkline, SemVer, Session replays, Actions, Translation, HTML tags and links, Text effects, and more](.\u002Freferences\u002Fhogql-extensions.md)\n- [SQL variables](.\u002Freferences\u002Fmodels-variables.md).\n- [Available functions in HogQL](.\u002Freferences\u002Favailable-functions.md). IMPORTANT: the list is long, so read data using bash commands like grep.\n\n## Analytics Query Examples\n\nUse the examples below to create optimized analytical queries.\n\n- [Trends (unique users, specific time range, single series)](.\u002Freferences\u002Fexample-trends-unique-users.md)\n- [Trends (total count with multiple breakdowns)](.\u002Freferences\u002Fexample-trends-breakdowns.md)\n- [Funnel (two steps, aggregated by unique users, broken down by the person's role, sequential, 14-day conversion window)](.\u002Freferences\u002Fexample-funnel-breakdown.md)\n- [Conversion trends (funnel, two steps, aggregated by unique groups, 1-day conversion window)](.\u002Freferences\u002Fexample-funnel-trends.md)\n- [Retention (unique users, returned to perform an event in the next 12 weeks, recurring)](.\u002Freferences\u002Fexample-retention.md)\n- [User paths (pageviews, three steps, applied path cleaning and filters, maximum 50 paths)](.\u002Freferences\u002Fexample-paths.md)\n- [Lifecycle (unique users by pageviews)](.\u002Freferences\u002Fexample-lifecycle.md)\n- [Stickiness (counted by pageviews from unique users, defined by at least one event for the interval, non-cumulative)](.\u002Freferences\u002Fexample-stickiness.md)\n- [LLM trace (generations, spans, embeddings, human feedback, captured AI metrics)](.\u002Freferences\u002Fexample-llm-trace.md)\n- [LLM traces list (searching and listing traces with property filters, two-phase query)](.\u002Freferences\u002Fexample-llm-traces-list.md)\n- [Web path stats (paths, visitors, views, bounce rate)](.\u002Freferences\u002Fexample-web-path-stats.md)\n- [Web traffic channels (direct, organic search, etc)](.\u002Freferences\u002Fexample-web-traffic-channels.md)\n- [Web views by devices](.\u002Freferences\u002Fexample-web-traffic-by-device-type.md)\n- [Web overview](.\u002Freferences\u002Fexample-web-overview.md)\n- [Error tracking (search for a value in an error and filtering by custom properties)](.\u002Freferences\u002Fexample-error-tracking.md)\n- [Logs (filtering by severity and searching for a term)](.\u002Freferences\u002Fexample-logs.md)\n- [Cross-signal correlation (metric exemplar → trace → logs)](.\u002Freferences\u002Fexample-observability-correlation.md)\n- [Sessions (listing sessions with duration, pageviews, and bounce rate)](.\u002Freferences\u002Fexample-sessions.md)\n- [Session replay (listing recordings with activity filters)](.\u002Freferences\u002Fexample-session-replay.md)\n- [Team taxonomy (top events by count, paginated)](.\u002Freferences\u002Fexample-team-taxonomy.md)\n- [Event taxonomy (properties of an event, with sample values)](.\u002Freferences\u002Fexample-event-taxonomy.md)\n- [Person property taxonomy (sample values for person properties)](.\u002Freferences\u002Fexample-person-property-taxonomy.md)\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,48,79,86,93,98,141,154,160,165,185,191,196,532,538,590,596,601],{"type":40,"tag":41,"props":42,"children":44},"element","h1",{"id":43},"querying-data-in-posthog",[45],{"type":46,"value":47},"text","Querying data in PostHog",{"type":40,"tag":49,"props":50,"children":51},"p",{},[52,54,61,63,70,72,77],{"type":46,"value":53},"The ",{"type":40,"tag":55,"props":56,"children":58},"a",{"href":57},".\u002Freferences\u002Fguidelines.md",[59],{"type":46,"value":60},"guidelines",{"type":46,"value":62}," contain the same instructions as ",{"type":40,"tag":64,"props":65,"children":67},"code",{"className":66},[],[68],{"type":46,"value":69},"posthog:execute-sql",{"type":46,"value":71},". If you've already read ",{"type":40,"tag":64,"props":73,"children":75},{"className":74},[],[76],{"type":46,"value":69},{"type":46,"value":78},", you don't need to read them again.",{"type":40,"tag":80,"props":81,"children":83},"h2",{"id":82},"when-to-use-this-skill",[84],{"type":46,"value":85},"When to use this skill",{"type":40,"tag":87,"props":88,"children":90},"h3",{"id":89},"finding-a-specific-posthog-entity",[91],{"type":46,"value":92},"Finding a specific PostHog entity",{"type":40,"tag":49,"props":94,"children":95},{},[96],{"type":46,"value":97},"When the user wants to find a specific entity created in PostHog (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse items, etc.), or when a list\u002Fsearch tool returns too many results to narrow down:",{"type":40,"tag":99,"props":100,"children":101},"ol",{},[102,108,120],{"type":40,"tag":103,"props":104,"children":105},"li",{},[106],{"type":46,"value":107},"Read the appropriate schema reference under Data Schema to understand the entity's table and columns.",{"type":40,"tag":103,"props":109,"children":110},{},[111,113,118],{"type":46,"value":112},"Use ",{"type":40,"tag":64,"props":114,"children":116},{"className":115},[],[117],{"type":46,"value":69},{"type":46,"value":119}," to query the system table and find the matching entity (typically returning its ID).",{"type":40,"tag":103,"props":121,"children":122},{},[123,125,131,133,139],{"type":46,"value":124},"Use the dedicated read tool for that entity type (e.g. ",{"type":40,"tag":64,"props":126,"children":128},{"className":127},[],[129],{"type":46,"value":130},"posthog:insight-get",{"type":46,"value":132},", ",{"type":40,"tag":64,"props":134,"children":136},{"className":135},[],[137],{"type":46,"value":138},"posthog:dashboard-get",{"type":46,"value":140},") to retrieve the full entity by ID.",{"type":40,"tag":49,"props":142,"children":143},{},[144,146,152],{"type":46,"value":145},"Don't try to reconstruct the entity from SQL — ",{"type":40,"tag":64,"props":147,"children":149},{"className":148},[],[150],{"type":46,"value":151},"execute-sql",{"type":46,"value":153}," is for discovery, the read tool is for retrieval.",{"type":40,"tag":87,"props":155,"children":157},{"id":156},"querying-analytics-data",[158],{"type":46,"value":159},"Querying analytics data",{"type":40,"tag":49,"props":161,"children":162},{},[163],{"type":46,"value":164},"When the user wants analytics data (trends, funnels, retention, paths, sessions, LLM traces, web analytics, errors, logs, etc.) and the existing insight schemas don't fit the request:",{"type":40,"tag":99,"props":166,"children":167},{},[168,173],{"type":40,"tag":103,"props":169,"children":170},{},[171],{"type":46,"value":172},"Look for a matching example under Analytics Query Examples. The list is not exhaustive — there may not be an example for every scenario. If one is a close fit (same domain, similar aggregation), read it; otherwise skip this step.",{"type":40,"tag":103,"props":174,"children":175},{},[176,178,183],{"type":46,"value":177},"Adapt the example query (if one was found) to the user's request and run it via ",{"type":40,"tag":64,"props":179,"children":181},{"className":180},[],[182],{"type":46,"value":69},{"type":46,"value":184},". If no example fit, compose the query from scratch using the Data Schema and HogQL References.",{"type":40,"tag":80,"props":186,"children":188},{"id":187},"data-schema",[189],{"type":46,"value":190},"Data Schema",{"type":40,"tag":49,"props":192,"children":193},{},[194],{"type":46,"value":195},"Schema reference for PostHog's core system models, organized by domain:",{"type":40,"tag":197,"props":198,"children":199},"ul",{},[200,209,218,227,236,253,262,271,280,289,298,307,316,325,349,358,367,376,392,401,418,434,443,452,461,470,479,488,497,506],{"type":40,"tag":103,"props":201,"children":202},{},[203],{"type":40,"tag":55,"props":204,"children":206},{"href":205},".\u002Freferences\u002Fmodels-activity-logs.md",[207],{"type":46,"value":208},"Activity logs",{"type":40,"tag":103,"props":210,"children":211},{},[212],{"type":40,"tag":55,"props":213,"children":215},{"href":214},".\u002Freferences\u002Fmodels-actions.md",[216],{"type":46,"value":217},"Actions",{"type":40,"tag":103,"props":219,"children":220},{},[221],{"type":40,"tag":55,"props":222,"children":224},{"href":223},".\u002Freferences\u002Fmodels-alerts.md",[225],{"type":46,"value":226},"Alerts",{"type":40,"tag":103,"props":228,"children":229},{},[230],{"type":40,"tag":55,"props":231,"children":233},{"href":232},".\u002Freferences\u002Fmodels-annotations.md",[234],{"type":46,"value":235},"Annotations",{"type":40,"tag":103,"props":237,"children":238},{},[239],{"type":40,"tag":55,"props":240,"children":242},{"href":241},".\u002Freferences\u002Fmodels-apm-spans.md",[243,245,251],{"type":46,"value":244},"APM \u002F tracing (",{"type":40,"tag":64,"props":246,"children":248},{"className":247},[],[249],{"type":46,"value":250},"posthog.trace_spans",{"type":46,"value":252},")",{"type":40,"tag":103,"props":254,"children":255},{},[256],{"type":40,"tag":55,"props":257,"children":259},{"href":258},".\u002Freferences\u002Fmodels-batch-exports.md",[260],{"type":46,"value":261},"Batch exports",{"type":40,"tag":103,"props":263,"children":264},{},[265],{"type":40,"tag":55,"props":266,"children":268},{"href":267},".\u002Freferences\u002Fmodels-early-access-features.md",[269],{"type":46,"value":270},"Early Access Features",{"type":40,"tag":103,"props":272,"children":273},{},[274],{"type":40,"tag":55,"props":275,"children":277},{"href":276},".\u002Freferences\u002Fmodels-cohorts.md",[278],{"type":46,"value":279},"Cohorts & Persons",{"type":40,"tag":103,"props":281,"children":282},{},[283],{"type":40,"tag":55,"props":284,"children":286},{"href":285},".\u002Freferences\u002Fmodels-dashboards-insights.md",[287],{"type":46,"value":288},"Dashboards, Tiles & Insights",{"type":40,"tag":103,"props":290,"children":291},{},[292],{"type":40,"tag":55,"props":293,"children":295},{"href":294},".\u002Freferences\u002Fmodels-data-warehouse.md",[296],{"type":46,"value":297},"Data Warehouse",{"type":40,"tag":103,"props":299,"children":300},{},[301],{"type":40,"tag":55,"props":302,"children":304},{"href":303},".\u002Freferences\u002Fmodels-endpoints.md",[305],{"type":46,"value":306},"Data Modeling Endpoints",{"type":40,"tag":103,"props":308,"children":309},{},[310],{"type":40,"tag":55,"props":311,"children":313},{"href":312},".\u002Freferences\u002Fmodels-error-tracking.md",[314],{"type":46,"value":315},"Error Tracking",{"type":40,"tag":103,"props":317,"children":318},{},[319],{"type":40,"tag":55,"props":320,"children":322},{"href":321},".\u002Freferences\u002Fmodels-flags-experiments.md",[323],{"type":46,"value":324},"Flags & Experiments",{"type":40,"tag":103,"props":326,"children":327},{},[328],{"type":40,"tag":55,"props":329,"children":331},{"href":330},".\u002Freferences\u002Fmodels-heatmaps.md",[332,334,340,342,348],{"type":46,"value":333},"Heatmaps (",{"type":40,"tag":64,"props":335,"children":337},{"className":336},[],[338],{"type":46,"value":339},"heatmaps",{"type":46,"value":341}," data + ",{"type":40,"tag":64,"props":343,"children":345},{"className":344},[],[346],{"type":46,"value":347},"system.heatmaps_saved",{"type":46,"value":252},{"type":40,"tag":103,"props":350,"children":351},{},[352],{"type":40,"tag":55,"props":353,"children":355},{"href":354},".\u002Freferences\u002Fmodels-hog-flows.md",[356],{"type":46,"value":357},"Hog Flows",{"type":40,"tag":103,"props":359,"children":360},{},[361],{"type":40,"tag":55,"props":362,"children":364},{"href":363},".\u002Freferences\u002Fmodels-hog-functions.md",[365],{"type":46,"value":366},"Hog Functions",{"type":40,"tag":103,"props":368,"children":369},{},[370],{"type":40,"tag":55,"props":371,"children":373},{"href":372},".\u002Freferences\u002Fmodels-integrations.md",[374],{"type":46,"value":375},"Integrations",{"type":40,"tag":103,"props":377,"children":378},{},[379],{"type":40,"tag":55,"props":380,"children":382},{"href":381},".\u002Freferences\u002Fmodels-ai-observability-events.md",[383,385,391],{"type":46,"value":384},"AI observability events (",{"type":40,"tag":64,"props":386,"children":388},{"className":387},[],[389],{"type":46,"value":390},"posthog.ai_events",{"type":46,"value":252},{"type":40,"tag":103,"props":393,"children":394},{},[395],{"type":40,"tag":55,"props":396,"children":398},{"href":397},".\u002Freferences\u002Fmodels-ai-observability-reviews.md",[399],{"type":46,"value":400},"AI observability reviews",{"type":40,"tag":103,"props":402,"children":403},{},[404],{"type":40,"tag":55,"props":405,"children":407},{"href":406},".\u002Freferences\u002Fmodels-logs.md",[408,410,416],{"type":46,"value":409},"Logs (",{"type":40,"tag":64,"props":411,"children":413},{"className":412},[],[414],{"type":46,"value":415},"logs",{"type":46,"value":417}," data plane + saved views and alerts)",{"type":40,"tag":103,"props":419,"children":420},{},[421],{"type":40,"tag":55,"props":422,"children":424},{"href":423},".\u002Freferences\u002Fmodels-metrics.md",[425,427,433],{"type":46,"value":426},"Metrics (",{"type":40,"tag":64,"props":428,"children":430},{"className":429},[],[431],{"type":46,"value":432},"posthog.metrics",{"type":46,"value":252},{"type":40,"tag":103,"props":435,"children":436},{},[437],{"type":40,"tag":55,"props":438,"children":440},{"href":439},".\u002Freferences\u002Fmodels-notebooks.md",[441],{"type":46,"value":442},"Notebooks",{"type":40,"tag":103,"props":444,"children":445},{},[446],{"type":40,"tag":55,"props":447,"children":449},{"href":448},".\u002Freferences\u002Fmodels-session-recording-playlists.md",[450],{"type":46,"value":451},"Session Recording Playlists",{"type":40,"tag":103,"props":453,"children":454},{},[455],{"type":40,"tag":55,"props":456,"children":458},{"href":457},".\u002Freferences\u002Fmodels-session-recordings.md",[459],{"type":46,"value":460},"Session Recordings",{"type":40,"tag":103,"props":462,"children":463},{},[464],{"type":40,"tag":55,"props":465,"children":467},{"href":466},".\u002Freferences\u002Fmodels-support-tickets.md",[468],{"type":46,"value":469},"Support Tickets",{"type":40,"tag":103,"props":471,"children":472},{},[473],{"type":40,"tag":55,"props":474,"children":476},{"href":475},".\u002Freferences\u002Fmodels-surveys.md",[477],{"type":46,"value":478},"Surveys",{"type":40,"tag":103,"props":480,"children":481},{},[482],{"type":40,"tag":55,"props":483,"children":485},{"href":484},".\u002Freferences\u002Fmodels-usage-metrics.md",[486],{"type":46,"value":487},"Usage Metrics",{"type":40,"tag":103,"props":489,"children":490},{},[491],{"type":40,"tag":55,"props":492,"children":494},{"href":493},".\u002Freferences\u002Fmodels-variables.md",[495],{"type":46,"value":496},"SQL Variables",{"type":40,"tag":103,"props":498,"children":499},{},[500],{"type":40,"tag":55,"props":501,"children":503},{"href":502},".\u002Freferences\u002Ftaxonomy-skipped-events.md",[504],{"type":46,"value":505},"Skipped events in the read-data-schema tool",{"type":40,"tag":103,"props":507,"children":508},{},[509,515,517,523,524,530],{"type":40,"tag":55,"props":510,"children":512},{"href":511},".\u002Freferences\u002Ftaxonomy-dynamic-properties.md",[513],{"type":46,"value":514},"Dynamic person and event properties",{"type":46,"value":516}," — patterns like ",{"type":40,"tag":64,"props":518,"children":520},{"className":519},[],[521],{"type":46,"value":522},"$survey_dismissed\u002F{id}",{"type":46,"value":132},{"type":40,"tag":64,"props":525,"children":527},{"className":526},[],[528],{"type":46,"value":529},"$feature\u002F{key}",{"type":46,"value":531}," that don't appear in tool results",{"type":40,"tag":80,"props":533,"children":535},{"id":534},"hogql-references",[536],{"type":46,"value":537},"HogQL References",{"type":40,"tag":197,"props":539,"children":540},{},[541,560,569,579],{"type":40,"tag":103,"props":542,"children":543},{},[544,550,552,558],{"type":40,"tag":55,"props":545,"children":547},{"href":546},".\u002Freferences\u002Fperson-property-modes.md",[548],{"type":46,"value":549},"Person property modes (event-time vs query-time)",{"type":46,"value":551},". Read when working with ",{"type":40,"tag":64,"props":553,"children":555},{"className":554},[],[556],{"type":46,"value":557},"person.properties.*",{"type":46,"value":559}," to understand if values are historical or current.",{"type":40,"tag":103,"props":561,"children":562},{},[563],{"type":40,"tag":55,"props":564,"children":566},{"href":565},".\u002Freferences\u002Fhogql-extensions.md",[567],{"type":46,"value":568},"Sparkline, SemVer, Session replays, Actions, Translation, HTML tags and links, Text effects, and more",{"type":40,"tag":103,"props":570,"children":571},{},[572,577],{"type":40,"tag":55,"props":573,"children":574},{"href":493},[575],{"type":46,"value":576},"SQL variables",{"type":46,"value":578},".",{"type":40,"tag":103,"props":580,"children":581},{},[582,588],{"type":40,"tag":55,"props":583,"children":585},{"href":584},".\u002Freferences\u002Favailable-functions.md",[586],{"type":46,"value":587},"Available functions in HogQL",{"type":46,"value":589},". IMPORTANT: the list is long, so read data using bash commands like grep.",{"type":40,"tag":80,"props":591,"children":593},{"id":592},"analytics-query-examples",[594],{"type":46,"value":595},"Analytics Query Examples",{"type":40,"tag":49,"props":597,"children":598},{},[599],{"type":46,"value":600},"Use the examples below to create optimized analytical queries.",{"type":40,"tag":197,"props":602,"children":603},{},[604,613,622,631,640,649,658,667,676,685,694,703,712,721,730,739,748,757,766,775,784,793],{"type":40,"tag":103,"props":605,"children":606},{},[607],{"type":40,"tag":55,"props":608,"children":610},{"href":609},".\u002Freferences\u002Fexample-trends-unique-users.md",[611],{"type":46,"value":612},"Trends (unique users, specific time range, single series)",{"type":40,"tag":103,"props":614,"children":615},{},[616],{"type":40,"tag":55,"props":617,"children":619},{"href":618},".\u002Freferences\u002Fexample-trends-breakdowns.md",[620],{"type":46,"value":621},"Trends (total count with multiple breakdowns)",{"type":40,"tag":103,"props":623,"children":624},{},[625],{"type":40,"tag":55,"props":626,"children":628},{"href":627},".\u002Freferences\u002Fexample-funnel-breakdown.md",[629],{"type":46,"value":630},"Funnel (two steps, aggregated by unique users, broken down by the person's role, sequential, 14-day conversion window)",{"type":40,"tag":103,"props":632,"children":633},{},[634],{"type":40,"tag":55,"props":635,"children":637},{"href":636},".\u002Freferences\u002Fexample-funnel-trends.md",[638],{"type":46,"value":639},"Conversion trends (funnel, two steps, aggregated by unique groups, 1-day conversion window)",{"type":40,"tag":103,"props":641,"children":642},{},[643],{"type":40,"tag":55,"props":644,"children":646},{"href":645},".\u002Freferences\u002Fexample-retention.md",[647],{"type":46,"value":648},"Retention (unique users, returned to perform an event in the next 12 weeks, recurring)",{"type":40,"tag":103,"props":650,"children":651},{},[652],{"type":40,"tag":55,"props":653,"children":655},{"href":654},".\u002Freferences\u002Fexample-paths.md",[656],{"type":46,"value":657},"User paths (pageviews, three steps, applied path cleaning and filters, maximum 50 paths)",{"type":40,"tag":103,"props":659,"children":660},{},[661],{"type":40,"tag":55,"props":662,"children":664},{"href":663},".\u002Freferences\u002Fexample-lifecycle.md",[665],{"type":46,"value":666},"Lifecycle (unique users by pageviews)",{"type":40,"tag":103,"props":668,"children":669},{},[670],{"type":40,"tag":55,"props":671,"children":673},{"href":672},".\u002Freferences\u002Fexample-stickiness.md",[674],{"type":46,"value":675},"Stickiness (counted by pageviews from unique users, defined by at least one event for the interval, non-cumulative)",{"type":40,"tag":103,"props":677,"children":678},{},[679],{"type":40,"tag":55,"props":680,"children":682},{"href":681},".\u002Freferences\u002Fexample-llm-trace.md",[683],{"type":46,"value":684},"LLM trace (generations, spans, embeddings, human feedback, captured AI metrics)",{"type":40,"tag":103,"props":686,"children":687},{},[688],{"type":40,"tag":55,"props":689,"children":691},{"href":690},".\u002Freferences\u002Fexample-llm-traces-list.md",[692],{"type":46,"value":693},"LLM traces list (searching and listing traces with property filters, two-phase query)",{"type":40,"tag":103,"props":695,"children":696},{},[697],{"type":40,"tag":55,"props":698,"children":700},{"href":699},".\u002Freferences\u002Fexample-web-path-stats.md",[701],{"type":46,"value":702},"Web path stats (paths, visitors, views, bounce rate)",{"type":40,"tag":103,"props":704,"children":705},{},[706],{"type":40,"tag":55,"props":707,"children":709},{"href":708},".\u002Freferences\u002Fexample-web-traffic-channels.md",[710],{"type":46,"value":711},"Web traffic channels (direct, organic search, etc)",{"type":40,"tag":103,"props":713,"children":714},{},[715],{"type":40,"tag":55,"props":716,"children":718},{"href":717},".\u002Freferences\u002Fexample-web-traffic-by-device-type.md",[719],{"type":46,"value":720},"Web views by devices",{"type":40,"tag":103,"props":722,"children":723},{},[724],{"type":40,"tag":55,"props":725,"children":727},{"href":726},".\u002Freferences\u002Fexample-web-overview.md",[728],{"type":46,"value":729},"Web overview",{"type":40,"tag":103,"props":731,"children":732},{},[733],{"type":40,"tag":55,"props":734,"children":736},{"href":735},".\u002Freferences\u002Fexample-error-tracking.md",[737],{"type":46,"value":738},"Error tracking (search for a value in an error and filtering by custom properties)",{"type":40,"tag":103,"props":740,"children":741},{},[742],{"type":40,"tag":55,"props":743,"children":745},{"href":744},".\u002Freferences\u002Fexample-logs.md",[746],{"type":46,"value":747},"Logs (filtering by severity and searching for a term)",{"type":40,"tag":103,"props":749,"children":750},{},[751],{"type":40,"tag":55,"props":752,"children":754},{"href":753},".\u002Freferences\u002Fexample-observability-correlation.md",[755],{"type":46,"value":756},"Cross-signal correlation (metric exemplar → trace → logs)",{"type":40,"tag":103,"props":758,"children":759},{},[760],{"type":40,"tag":55,"props":761,"children":763},{"href":762},".\u002Freferences\u002Fexample-sessions.md",[764],{"type":46,"value":765},"Sessions (listing sessions with duration, pageviews, and bounce rate)",{"type":40,"tag":103,"props":767,"children":768},{},[769],{"type":40,"tag":55,"props":770,"children":772},{"href":771},".\u002Freferences\u002Fexample-session-replay.md",[773],{"type":46,"value":774},"Session replay (listing recordings with activity filters)",{"type":40,"tag":103,"props":776,"children":777},{},[778],{"type":40,"tag":55,"props":779,"children":781},{"href":780},".\u002Freferences\u002Fexample-team-taxonomy.md",[782],{"type":46,"value":783},"Team taxonomy (top events by count, paginated)",{"type":40,"tag":103,"props":785,"children":786},{},[787],{"type":40,"tag":55,"props":788,"children":790},{"href":789},".\u002Freferences\u002Fexample-event-taxonomy.md",[791],{"type":46,"value":792},"Event taxonomy (properties of an event, with sample values)",{"type":40,"tag":103,"props":794,"children":795},{},[796],{"type":40,"tag":55,"props":797,"children":799},{"href":798},".\u002Freferences\u002Fexample-person-property-taxonomy.md",[800],{"type":46,"value":801},"Person property taxonomy (sample values for person properties)",{"items":803,"total":973},[804,821,833,845,858,873,889,906,920,935,945,963],{"slug":805,"name":805,"fn":806,"description":807,"org":808,"tags":809,"stars":818,"repoUrl":819,"updatedAt":820},"analyzing-expensive-users","analyze expensive users in AI observability","Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[810,811,814,817],{"name":18,"slug":19,"type":13},{"name":812,"slug":813,"type":13},"Cost Optimization","cost-optimization",{"name":815,"slug":816,"type":13},"Observability","observability",{"name":9,"slug":8,"type":13},35568,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fposthog","2026-07-28T05:34:11.117757",{"slug":822,"name":822,"fn":823,"description":824,"org":825,"tags":826,"stars":818,"repoUrl":819,"updatedAt":832},"auditing-endpoints","audit PostHog project endpoints","Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions. Use when the user asks \"what endpoints can I clean up?\", \"are any of my endpoints broken?\", \"which materialised versions are still being called?\", or wants a one-shot cleanup pass over the Endpoints product. Produces a prioritised report grouped by issue type, with recommended actions but does not modify anything without explicit confirmation.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[827,828,831],{"name":18,"slug":19,"type":13},{"name":829,"slug":830,"type":13},"Audit","audit",{"name":9,"slug":8,"type":13},"2026-06-08T08:08:33.693989",{"slug":834,"name":834,"fn":835,"description":836,"org":837,"tags":838,"stars":818,"repoUrl":819,"updatedAt":844},"auditing-warehouse-source-health","audit PostHog data warehouse source health","Audit the health of a PostHog project's data warehouse sources and syncs — find every broken or degraded source connection, sync schema, and webhook channel. Use when the user asks \"why are my imports failing?\", \"what's broken with my sources?\", \"why is my warehouse data stale?\", or wants a one-shot triage of source\u002Fsync health before deciding where to dig in. Produces a prioritized report grouped by severity, with recommended next steps. For materialized-view health use `auditing-warehouse-view-health`; for a single failing sync use `diagnosing-failed-warehouse-syncs`.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[839,840,842,843],{"name":829,"slug":830,"type":13},{"name":297,"slug":841,"type":13},"data-warehouse",{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-06-18T08:22:57.67984",{"slug":846,"name":846,"fn":847,"description":848,"org":849,"tags":850,"stars":818,"repoUrl":819,"updatedAt":857},"auditing-warehouse-view-health","audit PostHog materialized view health","Audit the health of a PostHog project's materialized views (saved queries) — find every failed materialization and flag unused or stale materialized views that cost storage and compute. Use when the user asks \"which of my views are broken?\", \"why is this materialized view failing?\", \"are any of my views wasting compute?\", or wants a one-shot triage of view health. For source\u002Fsync health use `auditing-warehouse-source-health`.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[851,852,853,856],{"name":829,"slug":830,"type":13},{"name":297,"slug":841,"type":13},{"name":854,"slug":855,"type":13},"Performance","performance",{"name":9,"slug":8,"type":13},"2026-06-18T08:25:10.936787",{"slug":859,"name":859,"fn":860,"description":861,"org":862,"tags":863,"stars":818,"repoUrl":819,"updatedAt":872},"authoring-error-tracking-alerts","author PostHog error tracking alerts","Author error tracking alerts that fire when an issue is created, reopened, or starts spiking. Use when the user asks to set up error notifications, route exceptions to Slack\u002Fwebhook\u002FLinear, or evaluate which error events are worth alerting on. Covers trigger-event selection, integration choice, dedup against existing alerts, and shipping with the canonical message body shape.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[864,867,870,871],{"name":865,"slug":866,"type":13},"Alerting","alerting",{"name":868,"slug":869,"type":13},"Debugging","debugging",{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-06-18T08:24:40.318583",{"slug":874,"name":874,"fn":875,"description":876,"org":877,"tags":878,"stars":818,"repoUrl":819,"updatedAt":888},"authoring-log-alerts","author log alerts in PostHog","Author useful, low-noise log alerts on services in a PostHog project. Use when the user asks to set up alerts for their logs, suggest alerts they should add, or evaluate whether a service is worth monitoring. Covers service triage, baseline characterisation, threshold drafting, back-testing via simulate, and shipping with a notification destination.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[879,880,883,884,887],{"name":18,"slug":19,"type":13},{"name":881,"slug":882,"type":13},"Monitoring","monitoring",{"name":815,"slug":816,"type":13},{"name":885,"slug":886,"type":13},"Operations","operations",{"name":9,"slug":8,"type":13},"2026-07-18T05:10:54.430898",{"slug":890,"name":890,"fn":891,"description":892,"org":893,"tags":894,"stars":818,"repoUrl":819,"updatedAt":905},"building-workflows","build and edit PostHog workflows","Build, edit, test, enable, and monitor PostHog workflows over MCP. Author the action\u002Fedge graph so it runs and opens cleanly in the visual editor, then change drafts surgically with patch operations. Use when asked to build, set up, automate, change, fix, or debug a workflow, campaign, broadcast, drip sequence, or event-triggered automation in the workflows product.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[895,898,901,902],{"name":896,"slug":897,"type":13},"Automation","automation",{"name":899,"slug":900,"type":13},"MCP","mcp",{"name":9,"slug":8,"type":13},{"name":903,"slug":904,"type":13},"Workflow Automation","workflow-automation","2026-07-28T05:34:12.167015",{"slug":907,"name":907,"fn":908,"description":909,"org":910,"tags":911,"stars":818,"repoUrl":819,"updatedAt":919},"check-posthog-loading","inspect PostHog SDK loading across URLs","Inspect how the PostHog JavaScript SDK is loaded across a list of URLs. Use to confirm consistent installation across pages, find pages missing the snippet, detect mismatched API keys or hosts between pages, and verify the load method (head snippet vs deferred vs array.js).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[912,913,914,917,918],{"name":18,"slug":19,"type":13},{"name":868,"slug":869,"type":13},{"name":915,"slug":916,"type":13},"Frontend","frontend",{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-05-07T05:56:19.828048",{"slug":921,"name":921,"fn":922,"description":923,"org":924,"tags":925,"stars":818,"repoUrl":819,"updatedAt":934},"consuming-endpoints-from-client-code","integrate PostHog endpoints into client applications","Wire a PostHog endpoint into a client app or SDK. Covers fetching the OpenAPI spec, generating a typed client with openapi-generator or @hey-api\u002Fopenapi-ts, sending the right auth header, shaping the variables payload (HogQL code_name vs insight breakdown property), handling rate-limit and materialised-endpoint error responses. Use when the user says \"how do I call my endpoint\", \"generate a client for this\", or \"what auth header do I use\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[926,929,930,931],{"name":927,"slug":928,"type":13},"API Development","api-development",{"name":915,"slug":916,"type":13},{"name":9,"slug":8,"type":13},{"name":932,"slug":933,"type":13},"SDK","sdk","2026-06-08T08:08:34.929454",{"slug":936,"name":936,"fn":937,"description":938,"org":939,"tags":940,"stars":818,"repoUrl":819,"updatedAt":944},"copying-endpoints-across-projects","copy PostHog endpoints across projects","Copy a PostHog endpoint (a saved HogQL\u002Finsight query exposed as an API route) to another project in the same organization, or duplicate it under a new name in the same project. Use when the user wants to duplicate an endpoint, promote an endpoint from staging to production, replicate an endpoint's query\u002Fvariables\u002Ffreshness config in another workspace, or clone an endpoint to iterate on it. Unlike feature flags and experiments, endpoints have NO native cross-project copy tool — this skill covers the read-then-recreate flow (endpoint-get then endpoint-create), the active-project switching it requires, name-collision checks, and the safe defaults (land unmaterialised in the target, verify with endpoint-run). Does not cover editing endpoint versions (see managing-endpoint-versions) or authoring a brand-new endpoint from scratch (see creating-an-endpoint).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[941,942,943],{"name":927,"slug":928,"type":13},{"name":885,"slug":886,"type":13},{"name":9,"slug":8,"type":13},"2026-07-15T05:29:58.442727",{"slug":946,"name":946,"fn":947,"description":948,"org":949,"tags":950,"stars":818,"repoUrl":819,"updatedAt":962},"creating-ai-subscription","schedule recurring AI-generated PostHog reports","Create a recurring AI-generated PostHog report — schedule a free-text prompt to run on a cron, with the LLM-synthesized markdown delivered to email or Slack on each tick. Use when the user wants a recurring AI summary of X on any cadence (daily, weekly, monthly, yearly) rather than a one-off report. (To attach an AI summary to an existing insight\u002Fdashboard subscription instead of a free-text prompt, see `managing-subscriptions` and its `summary_enabled` option.)\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[951,952,955,956,959],{"name":896,"slug":897,"type":13},{"name":953,"slug":954,"type":13},"Email","email",{"name":9,"slug":8,"type":13},{"name":957,"slug":958,"type":13},"Reporting","reporting",{"name":960,"slug":961,"type":13},"Slack","slack","2026-06-09T07:32:27.935712",{"slug":964,"name":964,"fn":965,"description":966,"org":967,"tags":968,"stars":818,"repoUrl":819,"updatedAt":972},"creating-an-endpoint","create PostHog API endpoints","Create a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says \"create an endpoint\", \"expose this query as an API\", \"turn this insight into an endpoint\", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or compare mode, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[969,970,971],{"name":18,"slug":19,"type":13},{"name":927,"slug":928,"type":13},{"name":9,"slug":8,"type":13},"2026-06-08T08:08:29.624498",231,{"items":975,"total":1076},[976,993,1009,1023,1037,1049,1060],{"slug":977,"name":977,"fn":978,"description":979,"org":980,"tags":981,"stars":23,"repoUrl":24,"updatedAt":992},"account-handover","draft sales account handover notes","Draft structured handover notes for transitioning a PostHog account from one TAM or CSM to another. Use this skill when a TAM needs to hand over an account, prepare a transition briefing, write handover notes, create an account summary for a new owner, or any request involving account transitions between TAMs or CSMs. Triggers on \"hand over this account\", \"transition account to\", \"draft handover notes\", \"account briefing for new TAM\", \"prepare account transition\", or when a TAM names an account and says they're leaving or reassigning it.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[982,985,988,989],{"name":983,"slug":984,"type":13},"Communications","communications",{"name":986,"slug":987,"type":13},"CRM","crm",{"name":9,"slug":8,"type":13},{"name":990,"slug":991,"type":13},"Sales","sales","2026-04-16T05:13:00.172732",{"slug":994,"name":994,"fn":995,"description":996,"org":997,"tags":998,"stars":23,"repoUrl":24,"updatedAt":1008},"auditing-warehouse-data-health","audit PostHog data warehouse health","Audit the health of a PostHog project's data warehouse — find every broken or degraded pipeline item across sources, sync schemas, materialized views, batch exports, and transformations. Use when the user asks \"what's broken in my warehouse?\", \"give me a health check\", \"audit my data pipeline\", \"why are some dashboards stale?\", or wants a one-shot triage summary before deciding where to spend time. Produces a prioritized report of issues grouped by severity and type, with recommended next steps.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[999,1000,1003,1006,1007],{"name":829,"slug":830,"type":13},{"name":1001,"slug":1002,"type":13},"Data Engineering","data-engineering",{"name":1004,"slug":1005,"type":13},"Data Quality","data-quality",{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-06-21T08:19:05.85849",{"slug":1010,"name":1010,"fn":1011,"description":1012,"org":1013,"tags":1014,"stars":23,"repoUrl":24,"updatedAt":1022},"copying-flags-across-projects","copy feature flags across PostHog projects","Copy a feature flag from one PostHog project to one or more target projects in the same organization. Use when the user wants to duplicate a flag, promote a flag from staging to production, sync flags across projects, or replicate a flag configuration in a different workspace. Covers cohort remapping, scheduled-change handling, encrypted payloads, and the safe defaults (disabled in target, no scheduled changes).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1015,1018,1021],{"name":1016,"slug":1017,"type":13},"Deployment","deployment",{"name":1019,"slug":1020,"type":13},"Feature Flags","feature-flags",{"name":9,"slug":8,"type":13},"2026-05-04T05:56:44.484909",{"slug":1024,"name":1024,"fn":1025,"description":1026,"org":1027,"tags":1028,"stars":23,"repoUrl":24,"updatedAt":1036},"diagnosing-experiment-results","diagnose PostHog experiment results and anomalies","Diagnoses bias, anomalies, and strange-looking results on a specific PostHog experiment. Covers empty \u002F 0-exposure experiments, sample ratio mismatch, identity fragmentation, multi-variant exposure, uneven-split exclusion bias, significance traps (peeking, A\u002FA, Bayesian vs Frequentist), PostHog-vs-SQL discrepancies, and surprises after mid-run edits. Symptom-driven dispatch to the right diagnostic.\nTRIGGER when: user asks 'is my experiment biased?' or 'why 0 exposures?', references the bias banner, says a variant looks strange \u002F wrong \u002F off, sees significance flipping, notices PostHog numbers disagreeing with their SQL, sees an A\u002FA test showing significance, or reports surprises after mid-run edits.\nDO NOT TRIGGER when: creating a new experiment (use creating-experiments), only configuring rollout (use configuring-experiment-rollout) or metrics (use configuring-experiment-analytics), or only asking lifecycle questions (use managing-experiment-lifecycle).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1029,1032,1033,1034,1035],{"name":1030,"slug":1031,"type":13},"A\u002FB Testing","a-b-testing",{"name":18,"slug":19,"type":13},{"name":15,"slug":16,"type":13},{"name":868,"slug":869,"type":13},{"name":9,"slug":8,"type":13},"2026-05-22T06:59:58.103867",{"slug":1038,"name":1038,"fn":1039,"description":1040,"org":1041,"tags":1042,"stars":23,"repoUrl":24,"updatedAt":1048},"diagnosing-missing-recordings","diagnose missing PostHog session recordings","Diagnoses why a session recording is missing or was not captured. Use when a user asks why a session has no replay, why recordings aren't appearing, or wants to troubleshoot session replay capture issues for a specific session ID or across their project. Covers SDK diagnostic signals, project settings, sampling, triggers, ad blockers, and quota\u002Fbilling scenarios.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1043,1044,1045,1046,1047],{"name":18,"slug":19,"type":13},{"name":868,"slug":869,"type":13},{"name":915,"slug":916,"type":13},{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-04-22T05:06:51.989772",{"slug":1050,"name":1050,"fn":1051,"description":1052,"org":1053,"tags":1054,"stars":23,"repoUrl":24,"updatedAt":1059},"diagnosing-sdk-health","diagnose PostHog SDK health","Diagnoses the health of a project's PostHog SDK integrations — which SDKs are out of date and how to fix them. Use when a user asks about PostHog SDK versions, outdated SDKs, upgrade recommendations, \"SDK health\", \"SDK doctor\" (the former name), or when events or features seem off and it might be due to an old SDK.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1055,1056,1057,1058],{"name":18,"slug":19,"type":13},{"name":868,"slug":869,"type":13},{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-04-27T05:46:14.554016",{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1064,"tags":1065,"stars":23,"repoUrl":24,"updatedAt":1075},"error-tracking-android","track Android errors with PostHog","PostHog error tracking for Android",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1066,1069,1070,1073,1074],{"name":1067,"slug":1068,"type":13},"Android","android",{"name":868,"slug":869,"type":13},{"name":1071,"slug":1072,"type":13},"Mobile","mobile",{"name":815,"slug":816,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:46:26.982494",110]