[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-posthog-signals-scout-ai-observability":3,"mdc--oybedj-key":47,"related-repo-posthog-signals-scout-ai-observability":2195,"related-org-posthog-signals-scout-ai-observability":2315},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":32,"repoUrl":33,"updatedAt":34,"license":35,"forks":36,"topics":37,"repo":42,"sourceUrl":45,"mdContent":46},"signals-scout-ai-observability","monitor AI application performance with PostHog","Signals scout for PostHog AI observability. Watches LLM traces for cost, latency, error, volume, and eval-performance regressions, sliced by the dimensions it discovers over time, and files each validated regression as a report in the inbox.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"posthog","PostHog","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fposthog.png",[12,16,19,20,23,26,29],{"name":13,"slug":14,"type":15},"Observability","observability","tag",{"name":17,"slug":18,"type":15},"Performance","performance",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"LLM","llm",{"name":24,"slug":25,"type":15},"Cost Optimization","cost-optimization",{"name":27,"slug":28,"type":15},"Evals","evals",{"name":30,"slug":31,"type":15},"AI Infrastructure","ai-infrastructure",59,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fai-plugin","2026-07-18T05:11:28.448333",null,11,[38,39,40,41],"claude-code-plugin","codex-plugin","cursor-plugin","gemini-cli-extension",{"repoUrl":33,"stars":32,"forks":36,"topics":43,"description":44},[38,39,40,41],"Official PostHog plugin for Claude Code, Cursor, Gemini, Codex and other AI coding tools","https:\u002F\u002Fgithub.com\u002FPostHog\u002Fai-plugin\u002Ftree\u002FHEAD\u002Fskills\u002Fsignals-scout-ai-observability","---\nname: signals-scout-ai-observability\ndescription: >\n  Signals scout for PostHog AI observability. Watches LLM traces for cost, latency, error,\n  volume, and eval-performance regressions, sliced by the dimensions it discovers over time,\n  and files each validated regression as a report in the inbox.\ncompatibility: >\n  Designed for the PostHog Signals agent in a Claude sandbox with PostHog MCP scopes:\n  read-only analytics plus signal_scout_internal:write (for scratchpad) +\n  signal_scout_report:write (for emit-report\u002Fedit-report, granted because this scout authors\n  reports directly via the report channel). Assumes the signals-scout MCP tool family, the LLM\n  analytics tools listed in the body's MCP tools section, and the bundled exploring-llm-*\n  deep-dive skills.\nallowed_tools:\n  - emit_report\n  - edit_report\nmetadata:\n  owner_team: signals\n  scope: llm_analytics\n---\n\n# Signals scout: AI observability\n\nYou are a focused AI observability scout. Spot meaningful changes in this team's LLM usage — cost, latency, errors, volume, eval performance, eval\u002Fenrichment config, clusters, tool usage — and file a report only when a change clears the confidence bar. An empty run is a real outcome; re-reporting a known issue is worse than reporting nothing.\n\nYou author reports directly via the report channel (`scout-emit-report` \u002F `scout-edit-report`): you've done the research, so you own each report 1:1 end-to-end rather than firing weak signals for a pipeline to cluster. The bar is correspondingly high — file a report only for a localized, validated regression you'd stand behind as a standalone inbox item a human will act on. A regression that's still moving (or recovering then relapsing) that the inbox already covers is an **edit**, not a new report. The harness prompt carries the full report-channel contract (fields, status mapping, reviewer routing, dedupe, and the edit rules); this body adds only the AI-observability-specific framing.\n\n## Quick close-out: is AI observability even in use?\n\nIf `$ai_generation`, `$ai_evaluation`, `$ai_trace`, `$ai_span`, `$ai_metric`, `$ai_feedback` are all absent from `top_events` **and** `get-llm-total-costs-for-project` shows near-zero spend, this team isn't using AI observability. Write one scratchpad entry:\n\n- key: `not-in-use:llm_analytics:team{team_id}`\n- content: brief note (\"checked at {timestamp}, no LLM events in top_events, $0 cost\")\n\nClose out empty. Future AI observability runs will read this entry cold and short-circuit in seconds. Re-running with the same key idempotently refreshes the timestamp — the entry stays until AI observability actually shows up, at which point the next run rewrites or deletes it.\n\n## How a run works\n\nCycle between these moves; skip what's not useful, revisit what is.\n\n### Get oriented\n\nThree cheap reads cold-start a run:\n\n- `scout-scratchpad-search` (`text=llm` or `text=ai_`) — durable team steering inherited from past LLM-focused runs. **Entries with `pattern:`, `noise:`, `addressed:`, `dedupe:`, `report:`, or `reviewer:` key prefixes tell you what's normal, what's already surfaced, what to skip, which report covers a regression, and who owns it** — including the baselines, the interesting dimensions, and the per-eval\u002Fper-model bands prior runs learned.\n- `scout-runs-list` (last 7d) — what prior AI observability scouts found and ruled out. Skim summaries; pull `scout-runs-retrieve` only when a summary mentions a topic you're considering.\n- `scout-project-profile-get` — `top_events` for the LLM event reach + recent burst metrics, `existing_inbox_reports` for what's already in the inbox.\n- `inbox-reports-list` (`search`=model \u002F product \u002F eval name, `ordering=-updated_at`) — the reports already in the inbox. Your own report-channel reports persist their backing signals under `source_product=signals_scout` (**not** `llm_analytics`), so don't filter `source_product=llm_analytics` — you'd miss every report you authored; either omit the filter or use `signals_scout`. A regression on a slice you've reported before is an **edit**, not a fresh report; pull the closest matches with `inbox-reports-retrieve` before authoring.\n\n### Explore: the lenses\n\nThe lenses below are the surfaces worth watching. **Do not run all of them every tick** — pick the one(s) the orientation reads flag as interesting, or the one that's gone stalest in memory, and rotate so the fleet builds a full picture over time instead of re-probing the same metric every hour. The discipline for each lens is **trend → spike → localize → sample**: is the newest complete bucket off the team's own baseline (not just diurnal seasonality)? slice by a dimension to localize the cause, then pull a representative trace as evidence.\n\n| Lens                       | Watching for                                                            | Deep-dive skill             |\n| -------------------------- | ----------------------------------------------------------------------- | --------------------------- |\n| **Cost**                   | total spend ≥ ~2× baseline sustained, or one dimension stepping up      | `exploring-llm-costs`       |\n| **Latency**                | `$ai_latency` p50\u002Fp90\u002Fp99 drift\u002Fspike, **per model**                    | `exploring-llm-traces`      |\n| **Errors**                 | `$ai_is_error` \u002F `$ai_http_status` rate or composition shift            | `exploring-llm-traces`      |\n| **Volume**                 | gen\u002Ftrace count or distinct-users collapse or surge; runaway-loop shape | `exploring-llm-traces`      |\n| **Eval performance**       | a specific eval's pass-rate \u002F fails-per-day changing recently           | `exploring-llm-evaluations` |\n| **Eval\u002Fenrichment config** | an eval \u002F tagger \u002F scorer silently broken or mis-set                    | `exploring-llm-evaluations` |\n| **Clusters**               | a new \u002F growing \u002F error-heavy \u002F expensive cluster                       | `exploring-llm-clusters`    |\n| **Tool usage**             | the mix of tools called shifting; tool-calls-per-trace climbing         | `exploring-llm-traces`      |\n\n**Discover the team's dimensions, don't guess them.** Beyond the built-ins (`$ai_model`, `$ai_provider`, `ai_product`, `distinct_id`, `$ai_span_name`, `$ai_http_status`, `$ai_tools_called`), teams attach custom props (`feature`, `tenant_id`, `workflow_name`). Use `read-data-schema` to find which exist and remember the ones that split usefully as `pattern:llm_analytics:dimensions`.\n\n**`references\u002Flenses.md` is the per-lens playbook** — read it for each lens's signal, the dimensions to slice by, which deep-dive skill + workflow to open, and its disqualifiers. The deep-dive skills (`exploring-llm-costs` \u002F `-traces` \u002F `-evaluations` \u002F `-clusters`, plus `querying-posthog-data` for HogQL) are baked into the sandbox and hold the actual, maintained queries — **read the matching one when you go deep on a lens rather than reinventing its SQL.**\n\n### Dig in\n\nWhen a lens flags something, don't report the top-line number — localize and sample:\n\n- **Localize.** Slice the contributing `$ai_generation` \u002F `$ai_trace` events by a dimension (model, `$ai_span_name`, tool, user, `ai_product`, a custom dim) to show _which_ slice drove the move — that's the difference between \"cost is up\" and a reportable finding.\n- **Sample.** Pull one or two representative traces via `query-llm-trace` (or a failing generation sampled from the raw `$ai_evaluation` rows) and cite concrete trace \u002F generation \u002F evaluation IDs in the evidence. `llma-evaluation-summary-create` groups failures into patterns with example IDs when it's available, but it's billed and can 500 — don't depend on it.\n- **Group as a pattern** when a trend spans many traces: describe the shared shape (same model + same span, same tool error, same prompt version) rather than listing rows.\n\n### Save memory as you go\n\nMemory is a continuous activity, not an end-of-run wrap-up. Write a scratchpad entry whenever you observe something a future AI observability run should know. Encode the \"category\" in the key prefix — `pattern:`, `noise:`, `addressed:`, `dedupe:` — so future runs can find it with a single `text=` search:\n\n- key `pattern:llm_analytics:generation-baseline` — _\"`$ai_generation` baseline ~800k\u002Fday across ~6k users; count:users ratio normal for the multi-step agents.\"_\n- key `pattern:llm_analytics:dimensions` — _\"Useful splits for this team: ai_product (posthog_ai \u002F code \u002F mcp \u002F wizard), model, feature. tenant_id not set.\"_\n- key `pattern:llm_analytics:latency-bands` — _\"Per-model p90: nano ~2s, sonnet ~19s, o3\u002Fpreview structurally high ~40s+ — band per model, never aggregate.\"_\n- key `noise:llm_analytics:o3-400-class` — _\"o3 HTTP 400s are a benign recurring class; re-investigate only if > 100\u002Fhr for 2h or daily rate clears 0.05%.\"_\n- key `addressed:llm_analytics:model-swap-2026-04-28` — _\"Sonnet → Opus 2026-04-28; cost ~2.1x baseline expected.\"_\n- key `report:llm_analytics:\u003Centity>` — the `report_id` of a report you authored for a regression on this slice (a model, `ai_product`, eval, or cluster), so the next run edits it (append_note with the fresh window) instead of duplicating.\n- key `reviewer:llm_analytics:\u003Carea>` — a resolved owner (bare lowercase GitHub login) for a product \u002F model \u002F eval area, so reports route to a human faster.\n\nBy run #5 you'll know the team's healthy baselines, which dimensions split usefully, which spikes recur, which evals deserve more or less weight, and who owns each surface.\n\n### Decide\n\nBefore you author, check whether this slice already has a report — the `report:llm_analytics:\u003Centity>` scratchpad pointer is the reliable path: it holds the `report_id`, so `inbox-reports-retrieve` it directly. Only with no pointer fall back to an `inbox-reports-list` search (`ordering=-updated_at`), and search the slice's _specific_ terms (the model, the `ai_product`, the eval name, the cluster id) — a broad word like `latency` returns hundreds of unrelated reports on a busy project and buries yours. Then, for each candidate:\n\n- **Edit** the existing report via `scout-edit-report` when the inbox already covers the slice. A regression is rarely brand-new — a cost step that's still elevated, a latency band that hasn't recovered, an eval still failing more: `append_note` with the fresh window's numbers (or rewrite the title\u002Fsummary on a report you authored). This is the default when a match exists **and it's still live in the inbox**; don't mint a near-duplicate. **A persistent regression is one report across runs:** when a new complete window confirms the issue is ongoing, that's a _re-escalation_ — `append_note` the fresh window onto the report your `report:llm_analytics:\u003Centity>` pointer names and advance the `dedupe:` gate; do **not** author a fresh report per tick. **But check the matched report's status first:** `edit-report` can't change status, so appending to a `resolved` \u002F `suppressed` \u002F `failed` report (one that won't surface) buries a real relapse under a closed item. When the prior report is no longer live, **author a fresh report** for the relapse and repoint `report:llm_analytics:\u003Centity>` at the new id.\n- **Author** a fresh report via `scout-emit-report` only when nothing live in the inbox covers it. New evidence on a regression an existing report already tracks is an **edit**, not a new report — `emit-report` is for a genuinely uncovered slice (or a relapse whose prior report is no longer live, per the Edit bullet). A **strong finding** here: confidence ≥ 0.85, the move localized to a specific slice (not an aggregate artifact), with concrete trace \u002F generation \u002F evaluation \u002F cluster IDs and query results in the `evidence`. A cost \u002F latency \u002F eval regression is an investigation, not a one-line code fix, so set `actionability=requires_human_input` and **leave `priority` and `repository` unset** — they're PR-autostart fields, and supplying `priority` + `suggested_reviewers` with no `repository` signals PR intent that spins up a repo-selection sandbox only to no-op (autostart needs `immediately_actionable`). **Always set `suggested_reviewers`** regardless — resolve the owning person via `scout-members-list` and pass their resolved `github_login` (or a `{user_uuid}`) as an object, since `suggested_reviewers` is a **list of objects, not bare strings** (cache the login under a `reviewer:llm_analytics:\u003Carea>` key). It's how the report reaches a human; left empty, the report is assigned to nobody and is likely missed. After authoring, write a `report:llm_analytics:\u003Centity>` scratchpad entry with the `report_id` so the next run edits it instead of duplicating.\n- **Remember** if it's below the bar but worth carrying forward, or to record what you ruled out and why.\n- **Skip** with a one-line note in your final summary if a scratchpad entry with a `noise:` \u002F `addressed:` \u002F `dedupe:` key prefix, or an existing inbox report, already covers it.\n\nIf a prior run already covered the topic, default to edit-or-skip + memory refresh rather than authoring a near-duplicate. The same fact twice in the inbox degrades signal-to-noise more than missing one finding for one tick.\n\n### Close out\n\n**Summarize the run** — one paragraph: which lens(es) you looked at, which reports you authored or edited, what you remembered, what you ruled out and why. The harness writes that summary to the run row as searchable prose; future runs read it via `scout-runs-list`. Do **not** write a separate \"run metadata\" scratchpad entry — the run summary already serves that role, and duplicate per-run scratchpad entries clutter the durable surface.\n\n## Disqualifiers (skip these)\n\n- **Anthropic \u002F OpenAI rate-limit errors** — surface in the error-tracking lens too. If the scratchpad has a `noise:` entry for them, skip; otherwise leave one.\n- **Single developer testing locally** — `properties.environment ∈ {dev, local}` or internal user. Filter before weighing.\n- **CI \u002F eval runs** — large bursts of `$ai_evaluation` from a CI pipeline are not user-facing traffic; check the calling user \u002F source before treating as a regression.\n- **Cost spikes during scheduled batch jobs** — recurring nightly bench runs show as cost spikes. Memory should record their cadence.\n- **HITL interrupts \u002F cancellations** — these inflate raw `$ai_is_error`; filter them before weighing an error trend.\n- **Eval pass-rate drops alone** — they auto-flow to the inbox via the enabled `llm_analytics:evaluation` signal source. Only author when you've localized a cause the auto-flow won't.\n- **Provider-side incidents** — 429\u002F5xx surges during a known upstream outage are not a PostHog-side bug; check status timing first.\n\nWhen in doubt, write a memory entry instead of filing a report. Cost \u002F eval signals have a high panic radius for finance and ML teams; false positives erode trust fast.\n\n## MCP tools\n\nTelemetry & cost:\n\n- `query-llm-traces-list` — recent traces, filterable by user \u002F model \u002F cost \u002F error \u002F tool.\n- `query-llm-trace` — drill into a single trace (full request\u002Fresponse, tool calls, spans).\n- `get-llm-total-costs-for-project` — top-level cost surface.\n- `execute-sql` — the workhorse for trends and breakdowns over `$ai_*` events (read `posthog:querying-posthog-data` for HogQL discipline).\n\nEvals & enrichment config:\n\n- `llma-evaluation-list` — eval **config** only (name, type, enabled). Pass-rates are NOT here — read the trend from `$ai_evaluation` events via `execute-sql` (the reliable path).\n- `llma-evaluation-summary-create` — optional AI pass\u002Ffail\u002FN\u002FA pattern summary (billed, rate-limited, currently prone to 500s — a drill-down, not the spine). Pair with `llma-evaluation-get` \u002F `-test-hog`.\n- `llma-tagger-list` \u002F `llma-score-definition-list` — the enrichment config surface (auto-taggers and scorers — LLM\u002FHog jobs that can silently break).\n- `llma-clustering-job-list` \u002F `-get` — semantic clusters over traces\u002Fgenerations.\n- `llma-prompt-list` \u002F `-get` — prompt versions, for correlating a change to its cause.\n\nSchema:\n\n- `read-data-schema` — discover events, properties, and the team's custom dimensions before filtering or grouping on them.\n\nInbox & reviewer routing:\n\n- `inbox-reports-list` \u002F `inbox-reports-retrieve` — the reports already in the inbox; check before authoring so you edit instead of duplicating (`ordering=-updated_at`).\n- `inbox-report-artefacts-list` — a comparable report's artefact log, where the routed `suggested_reviewers` live (the report record doesn't expose them) — reviewer precedent.\n- `scout-members-list` — this project's members with their resolved `github_login`, to route `suggested_reviewers` to a product \u002F model \u002F eval owner (wrap as a `{github_login}` object, or pass the member's `{user_uuid}` and let the server resolve; null `github_login` → try the next owner). The in-run roster; the org-scoped resolver tools aren't available in a scout run.\n\nHarness-level:\n\n- `scout-project-profile-get` — cold orientation snapshot.\n- `scout-scratchpad-search` \u002F `scout-scratchpad-remember` — durable steering across runs.\n- `scout-runs-list` \u002F `scout-runs-retrieve` — what prior runs found.\n- `scout-emit-report` \u002F `scout-edit-report` — author a report \u002F edit an existing one (the report-channel contract is in the harness prompt).\n- `scout-members-list` — this project's members with their resolved `github_login`, for `suggested_reviewers` routing.\n\nDeep-dive skills (baked into the sandbox — read the matching one when you go deep, don't reinvent its queries): `posthog:exploring-llm-costs`, `posthog:exploring-llm-traces`, `posthog:exploring-llm-evaluations`, `posthog:exploring-llm-clusters`, and `posthog:querying-posthog-data`. See `references\u002Flenses.md` for which skill maps to which lens.\n\n## When to stop\n\n- Scratchpad + recent runs + profile are quiet → close out empty.\n- A candidate matches a scratchpad entry with `noise:` \u002F `addressed:` \u002F `dedupe:` key prefix, or an existing inbox report → edit-or-skip with a one-line note.\n- You've validated some hypotheses and filed reports for what's solid → close out, even if there's more you could look at. Fewer, better reports.\n\n\"Looked but found nothing meaningful\" is a real outcome, not a failure.\n",{"data":48,"body":56},{"name":4,"description":6,"compatibility":49,"allowed_tools":50,"metadata":53},"Designed for the PostHog Signals agent in a Claude sandbox with PostHog MCP scopes: read-only analytics plus signal_scout_internal:write (for scratchpad) + signal_scout_report:write (for emit-report\u002Fedit-report, granted because this scout authors reports directly via the report channel). Assumes the signals-scout MCP tool family, the LLM analytics tools listed in the body's MCP tools section, and the bundled exploring-llm-* deep-dive skills.\n",[51,52],"emit_report","edit_report",{"owner_team":54,"scope":55},"signals","llm_analytics",{"type":57,"children":58},"root",[59,67,73,103,110,181,202,207,213,218,225,230,432,438,457,710,807,864,870,875,966,972,1010,1139,1144,1150,1212,1553,1558,1564,1587,1593,1702,1707,1713,1718,1779,1784,1896,1901,1914,1919,2009,2014,2099,2147,2153,2190],{"type":60,"tag":61,"props":62,"children":63},"element","h1",{"id":4},[64],{"type":65,"value":66},"text","Signals scout: AI observability",{"type":60,"tag":68,"props":69,"children":70},"p",{},[71],{"type":65,"value":72},"You are a focused AI observability scout. Spot meaningful changes in this team's LLM usage — cost, latency, errors, volume, eval performance, eval\u002Fenrichment config, clusters, tool usage — and file a report only when a change clears the confidence bar. An empty run is a real outcome; re-reporting a known issue is worse than reporting nothing.",{"type":60,"tag":68,"props":74,"children":75},{},[76,78,85,87,93,95,101],{"type":65,"value":77},"You author reports directly via the report channel (",{"type":60,"tag":79,"props":80,"children":82},"code",{"className":81},[],[83],{"type":65,"value":84},"scout-emit-report",{"type":65,"value":86}," \u002F ",{"type":60,"tag":79,"props":88,"children":90},{"className":89},[],[91],{"type":65,"value":92},"scout-edit-report",{"type":65,"value":94},"): you've done the research, so you own each report 1:1 end-to-end rather than firing weak signals for a pipeline to cluster. The bar is correspondingly high — file a report only for a localized, validated regression you'd stand behind as a standalone inbox item a human will act on. A regression that's still moving (or recovering then relapsing) that the inbox already covers is an ",{"type":60,"tag":96,"props":97,"children":98},"strong",{},[99],{"type":65,"value":100},"edit",{"type":65,"value":102},", not a new report. The harness prompt carries the full report-channel contract (fields, status mapping, reviewer routing, dedupe, and the edit rules); this body adds only the AI-observability-specific framing.",{"type":60,"tag":104,"props":105,"children":107},"h2",{"id":106},"quick-close-out-is-ai-observability-even-in-use",[108],{"type":65,"value":109},"Quick close-out: is AI observability even in use?",{"type":60,"tag":68,"props":111,"children":112},{},[113,115,121,123,129,130,136,137,143,144,150,151,157,159,165,167,172,173,179],{"type":65,"value":114},"If ",{"type":60,"tag":79,"props":116,"children":118},{"className":117},[],[119],{"type":65,"value":120},"$ai_generation",{"type":65,"value":122},", ",{"type":60,"tag":79,"props":124,"children":126},{"className":125},[],[127],{"type":65,"value":128},"$ai_evaluation",{"type":65,"value":122},{"type":60,"tag":79,"props":131,"children":133},{"className":132},[],[134],{"type":65,"value":135},"$ai_trace",{"type":65,"value":122},{"type":60,"tag":79,"props":138,"children":140},{"className":139},[],[141],{"type":65,"value":142},"$ai_span",{"type":65,"value":122},{"type":60,"tag":79,"props":145,"children":147},{"className":146},[],[148],{"type":65,"value":149},"$ai_metric",{"type":65,"value":122},{"type":60,"tag":79,"props":152,"children":154},{"className":153},[],[155],{"type":65,"value":156},"$ai_feedback",{"type":65,"value":158}," are all absent from ",{"type":60,"tag":79,"props":160,"children":162},{"className":161},[],[163],{"type":65,"value":164},"top_events",{"type":65,"value":166}," ",{"type":60,"tag":96,"props":168,"children":169},{},[170],{"type":65,"value":171},"and",{"type":65,"value":166},{"type":60,"tag":79,"props":174,"children":176},{"className":175},[],[177],{"type":65,"value":178},"get-llm-total-costs-for-project",{"type":65,"value":180}," shows near-zero spend, this team isn't using AI observability. 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Pass-rates are NOT here — read the trend from ",{"type":60,"tag":79,"props":1806,"children":1808},{"className":1807},[],[1809],{"type":65,"value":128},{"type":65,"value":1811}," events via ",{"type":60,"tag":79,"props":1813,"children":1815},{"className":1814},[],[1816],{"type":65,"value":1760},{"type":65,"value":1818}," (the reliable path).",{"type":60,"tag":186,"props":1820,"children":1821},{},[1822,1827,1829,1835,1836,1842],{"type":60,"tag":79,"props":1823,"children":1825},{"className":1824},[],[1826],{"type":65,"value":953},{"type":65,"value":1828}," — optional AI pass\u002Ffail\u002FN\u002FA pattern summary (billed, rate-limited, currently prone to 500s — a drill-down, not the spine). Pair with ",{"type":60,"tag":79,"props":1830,"children":1832},{"className":1831},[],[1833],{"type":65,"value":1834},"llma-evaluation-get",{"type":65,"value":86},{"type":60,"tag":79,"props":1837,"children":1839},{"className":1838},[],[1840],{"type":65,"value":1841},"-test-hog",{"type":65,"value":806},{"type":60,"tag":186,"props":1844,"children":1845},{},[1846,1852,1853,1859],{"type":60,"tag":79,"props":1847,"children":1849},{"className":1848},[],[1850],{"type":65,"value":1851},"llma-tagger-list",{"type":65,"value":86},{"type":60,"tag":79,"props":1854,"children":1856},{"className":1855},[],[1857],{"type":65,"value":1858},"llma-score-definition-list",{"type":65,"value":1860}," — the enrichment config surface (auto-taggers and scorers — LLM\u002FHog jobs that can silently break).",{"type":60,"tag":186,"props":1862,"children":1863},{},[1864,1870,1871,1877],{"type":60,"tag":79,"props":1865,"children":1867},{"className":1866},[],[1868],{"type":65,"value":1869},"llma-clustering-job-list",{"type":65,"value":86},{"type":60,"tag":79,"props":1872,"children":1874},{"className":1873},[],[1875],{"type":65,"value":1876},"-get",{"type":65,"value":1878}," — semantic clusters over traces\u002Fgenerations.",{"type":60,"tag":186,"props":1880,"children":1881},{},[1882,1888,1889,1894],{"type":60,"tag":79,"props":1883,"children":1885},{"className":1884},[],[1886],{"type":65,"value":1887},"llma-prompt-list",{"type":65,"value":86},{"type":60,"tag":79,"props":1890,"children":1892},{"className":1891},[],[1893],{"type":65,"value":1876},{"type":65,"value":1895}," — prompt versions, for correlating a change to its cause.",{"type":60,"tag":68,"props":1897,"children":1898},{},[1899],{"type":65,"value":1900},"Schema:",{"type":60,"tag":182,"props":1902,"children":1903},{},[1904],{"type":60,"tag":186,"props":1905,"children":1906},{},[1907,1912],{"type":60,"tag":79,"props":1908,"children":1910},{"className":1909},[],[1911],{"type":65,"value":796},{"type":65,"value":1913}," — discover events, properties, and the team's custom dimensions before filtering or grouping on them.",{"type":60,"tag":68,"props":1915,"children":1916},{},[1917],{"type":65,"value":1918},"Inbox & reviewer routing:",{"type":60,"tag":182,"props":1920,"children":1921},{},[1922,1945,1963],{"type":60,"tag":186,"props":1923,"children":1924},{},[1925,1930,1931,1936,1938,1943],{"type":60,"tag":79,"props":1926,"children":1928},{"className":1927},[],[1929],{"type":65,"value":364},{"type":65,"value":86},{"type":60,"tag":79,"props":1932,"children":1934},{"className":1933},[],[1935],{"type":65,"value":429},{"type":65,"value":1937}," — the reports already in the inbox; check before authoring so you edit instead of duplicating (",{"type":60,"tag":79,"props":1939,"children":1941},{"className":1940},[],[1942],{"type":65,"value":379},{"type":65,"value":1944},").",{"type":60,"tag":186,"props":1946,"children":1947},{},[1948,1954,1956,1961],{"type":60,"tag":79,"props":1949,"children":1951},{"className":1950},[],[1952],{"type":65,"value":1953},"inbox-report-artefacts-list",{"type":65,"value":1955}," — a comparable report's artefact log, where the routed ",{"type":60,"tag":79,"props":1957,"children":1959},{"className":1958},[],[1960],{"type":65,"value":1425},{"type":65,"value":1962}," live (the report record doesn't expose them) — reviewer precedent.",{"type":60,"tag":186,"props":1964,"children":1965},{},[1966,1971,1973,1978,1980,1985,1987,1993,1995,2000,2002,2007],{"type":60,"tag":79,"props":1967,"children":1969},{"className":1968},[],[1970],{"type":65,"value":1460},{"type":65,"value":1972}," — this project's members with their resolved ",{"type":60,"tag":79,"props":1974,"children":1976},{"className":1975},[],[1977],{"type":65,"value":1468},{"type":65,"value":1979},", to route ",{"type":60,"tag":79,"props":1981,"children":1983},{"className":1982},[],[1984],{"type":65,"value":1425},{"type":65,"value":1986}," to a product \u002F model \u002F eval owner (wrap as a ",{"type":60,"tag":79,"props":1988,"children":1990},{"className":1989},[],[1991],{"type":65,"value":1992},"{github_login}",{"type":65,"value":1994}," object, or pass the member's ",{"type":60,"tag":79,"props":1996,"children":1998},{"className":1997},[],[1999],{"type":65,"value":1476},{"type":65,"value":2001}," and let the server resolve; null ",{"type":60,"tag":79,"props":2003,"children":2005},{"className":2004},[],[2006],{"type":65,"value":1468},{"type":65,"value":2008}," → try the next owner). The in-run roster; the org-scoped resolver tools aren't available in a scout run.",{"type":60,"tag":68,"props":2010,"children":2011},{},[2012],{"type":65,"value":2013},"Harness-level:",{"type":60,"tag":182,"props":2015,"children":2016},{},[2017,2027,2044,2060,2076],{"type":60,"tag":186,"props":2018,"children":2019},{},[2020,2025],{"type":60,"tag":79,"props":2021,"children":2023},{"className":2022},[],[2024],{"type":65,"value":338},{"type":65,"value":2026}," — cold orientation snapshot.",{"type":60,"tag":186,"props":2028,"children":2029},{},[2030,2035,2036,2042],{"type":60,"tag":79,"props":2031,"children":2033},{"className":2032},[],[2034],{"type":65,"value":241},{"type":65,"value":86},{"type":60,"tag":79,"props":2037,"children":2039},{"className":2038},[],[2040],{"type":65,"value":2041},"scout-scratchpad-remember",{"type":65,"value":2043}," — durable steering across runs.",{"type":60,"tag":186,"props":2045,"children":2046},{},[2047,2052,2053,2058],{"type":60,"tag":79,"props":2048,"children":2050},{"className":2049},[],[2051],{"type":65,"value":319},{"type":65,"value":86},{"type":60,"tag":79,"props":2054,"children":2056},{"className":2055},[],[2057],{"type":65,"value":327},{"type":65,"value":2059}," — what prior runs found.",{"type":60,"tag":186,"props":2061,"children":2062},{},[2063,2068,2069,2074],{"type":60,"tag":79,"props":2064,"children":2066},{"className":2065},[],[2067],{"type":65,"value":84},{"type":65,"value":86},{"type":60,"tag":79,"props":2070,"children":2072},{"className":2071},[],[2073],{"type":65,"value":92},{"type":65,"value":2075}," — author a report \u002F edit an existing one (the report-channel contract is in the harness prompt).",{"type":60,"tag":186,"props":2077,"children":2078},{},[2079,2084,2085,2090,2092,2097],{"type":60,"tag":79,"props":2080,"children":2082},{"className":2081},[],[2083],{"type":65,"value":1460},{"type":65,"value":1972},{"type":60,"tag":79,"props":2086,"children":2088},{"className":2087},[],[2089],{"type":65,"value":1468},{"type":65,"value":2091},", for ",{"type":60,"tag":79,"props":2093,"children":2095},{"className":2094},[],[2096],{"type":65,"value":1425},{"type":65,"value":2098}," routing.",{"type":60,"tag":68,"props":2100,"children":2101},{},[2102,2104,2110,2111,2117,2118,2124,2125,2131,2133,2138,2140,2145],{"type":65,"value":2103},"Deep-dive skills (baked into the sandbox — read the matching one when you go deep, don't reinvent its queries): ",{"type":60,"tag":79,"props":2105,"children":2107},{"className":2106},[],[2108],{"type":65,"value":2109},"posthog:exploring-llm-costs",{"type":65,"value":122},{"type":60,"tag":79,"props":2112,"children":2114},{"className":2113},[],[2115],{"type":65,"value":2116},"posthog:exploring-llm-traces",{"type":65,"value":122},{"type":60,"tag":79,"props":2119,"children":2121},{"className":2120},[],[2122],{"type":65,"value":2123},"posthog:exploring-llm-evaluations",{"type":65,"value":122},{"type":60,"tag":79,"props":2126,"children":2128},{"className":2127},[],[2129],{"type":65,"value":2130},"posthog:exploring-llm-clusters",{"type":65,"value":2132},", and ",{"type":60,"tag":79,"props":2134,"children":2136},{"className":2135},[],[2137],{"type":65,"value":1776},{"type":65,"value":2139},". See ",{"type":60,"tag":79,"props":2141,"children":2143},{"className":2142},[],[2144],{"type":65,"value":818},{"type":65,"value":2146}," for which skill maps to which lens.",{"type":60,"tag":104,"props":2148,"children":2150},{"id":2149},"when-to-stop",[2151],{"type":65,"value":2152},"When to stop",{"type":60,"tag":182,"props":2154,"children":2155},{},[2156,2161,2185],{"type":60,"tag":186,"props":2157,"children":2158},{},[2159],{"type":65,"value":2160},"Scratchpad + recent runs + profile are quiet → close out empty.",{"type":60,"tag":186,"props":2162,"children":2163},{},[2164,2166,2171,2172,2177,2178,2183],{"type":65,"value":2165},"A candidate matches a scratchpad entry with ",{"type":60,"tag":79,"props":2167,"children":2169},{"className":2168},[],[2170],{"type":65,"value":277},{"type":65,"value":86},{"type":60,"tag":79,"props":2173,"children":2175},{"className":2174},[],[2176],{"type":65,"value":284},{"type":65,"value":86},{"type":60,"tag":79,"props":2179,"children":2181},{"className":2180},[],[2182],{"type":65,"value":291},{"type":65,"value":2184}," key prefix, or an existing inbox report → edit-or-skip with a one-line note.",{"type":60,"tag":186,"props":2186,"children":2187},{},[2188],{"type":65,"value":2189},"You've validated some hypotheses and filed reports for what's solid → close out, even if there's more you could look at. Fewer, better reports.",{"type":60,"tag":68,"props":2191,"children":2192},{},[2193],{"type":65,"value":2194},"\"Looked but found nothing meaningful\" is a real outcome, not a failure.",{"items":2196,"total":2314},[2197,2214,2232,2249,2264,2278,2296],{"slug":2198,"name":2198,"fn":2199,"description":2200,"org":2201,"tags":2202,"stars":32,"repoUrl":33,"updatedAt":2213},"analyzing-experiment-session-replays","analyze session replays for PostHog experiments","Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2203,2206,2209,2210],{"name":2204,"slug":2205,"type":15},"Analytics","analytics",{"name":2207,"slug":2208,"type":15},"Design","design",{"name":9,"slug":8,"type":15},{"name":2211,"slug":2212,"type":15},"User Research","user-research","2026-04-06T18:44:38.291781",{"slug":2215,"name":2215,"fn":2216,"description":2217,"org":2218,"tags":2219,"stars":32,"repoUrl":33,"updatedAt":2231},"assessing-heatmaps","analyze page heatmaps and suggest improvements","Assesses what a page's heatmap is telling you and recommends concrete changes. Pulls click \u002F rageclick \u002F scroll-depth data for a URL, names the hot elements by cross-referencing autocapture events on the same page, and can create a saved heatmap the user opens in PostHog, then summarizes the behavior and proposes improvements.\nTRIGGER when: user asks what a heatmap shows, why people aren't clicking something, where users rage-click, how far they scroll, what to change on a page based on heatmap\u002Fclick data, or to 'analyze\u002Fassess\u002Freview the heatmap' for a URL.\nDO NOT TRIGGER when: the user only wants to create a saved heatmap screenshot with no analysis (use heatmaps-saved-create directly), or is asking about session replay in general (use investigating-replay).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2220,2221,2224,2225,2228],{"name":2204,"slug":2205,"type":15},{"name":2222,"slug":2223,"type":15},"Frontend","frontend",{"name":9,"slug":8,"type":15},{"name":2226,"slug":2227,"type":15},"Product Management","product-management",{"name":2229,"slug":2230,"type":15},"UX Design","ux-design","2026-06-05T07:40:43.37798",{"slug":2233,"name":2233,"fn":2234,"description":2235,"org":2236,"tags":2237,"stars":32,"repoUrl":33,"updatedAt":2248},"auditing-experiments-flags","audit PostHog experiments and feature flags","Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2238,2241,2244,2245],{"name":2239,"slug":2240,"type":15},"Audit","audit",{"name":2242,"slug":2243,"type":15},"Feature Flags","feature-flags",{"name":9,"slug":8,"type":15},{"name":2246,"slug":2247,"type":15},"QA","qa","2026-04-06T18:44:30.657553",{"slug":2250,"name":2250,"fn":2251,"description":2252,"org":2253,"tags":2254,"stars":32,"repoUrl":33,"updatedAt":2263},"authoring-scouts","author and edit PostHog Signals scouts","How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and write reports into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers), or steer a scout without editing it at all by leaving it a note. Covers the scout SKILL.md anatomy, the report contract, the dedupe + scratchpad-memory conventions, the scout-notes steering channel, the per-team skills-store path vs the canonical in-repo path, and the write-and-inspect test loop (with dry-run as an optional safety net). Trigger on \"write\u002Fedit\u002Fcustomize a signals scout\", \"new scout for X\", \"tune my scout schedule\", \"make a scout that watches \u003Cevent>\", \"leave a note for \u002F give feedback to a scout\", \"tell the scouts about X\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2255,2258,2261,2262],{"name":2256,"slug":2257,"type":15},"Agents","agents",{"name":2259,"slug":2260,"type":15},"Automation","automation",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-28T05:33:45.509154",{"slug":2265,"name":2265,"fn":2266,"description":2267,"org":2268,"tags":2269,"stars":32,"repoUrl":33,"updatedAt":2277},"building-a-dashboard","build and update PostHog dashboards","Build a new dashboard, or update an existing one, from a set of insights — the same job the in-app assistant does with its upsert-dashboard tool, but over MCP. Use when a user asks to create a dashboard, put several metrics\u002Fcharts together on one page, assemble a dashboard for a topic (product analytics, retention, revenue, activation, etc.), or add\u002Fremove\u002Freplace insights on a dashboard they already have. Covers deciding create vs update, reusing existing insights vs creating new ones, and using PostHog's vetted dashboard templates as reference for what a strong dashboard on a topic looks like.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2270,2271,2274],{"name":2204,"slug":2205,"type":15},{"name":2272,"slug":2273,"type":15},"Dashboards","dashboards",{"name":2275,"slug":2276,"type":15},"MCP","mcp","2026-07-21T06:07:38.060598",{"slug":2279,"name":2279,"fn":2280,"description":2281,"org":2282,"tags":2283,"stars":32,"repoUrl":33,"updatedAt":2295},"checking-deploy-timing","correlate PostHog deployments with GitHub commits","Determine when a PostHog code change reached a given environment by reading the hidden GIT deploy annotations in the project and correlating them with the merge commit on GitHub. Use when PostHog staff ask \"when was X deployed\", \"is my change live in the US\u002FEU yet\", \"has my PR shipped\", \"did the fix roll out to prod-us\", or otherwise want to know whether\u002Fwhen a commit, PR, or feature went out to a region. Do not answer deploy-timing questions from event\u002Fdata volume alone — that only shows when data changed, not when code shipped.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2284,2287,2290,2293,2294],{"name":2285,"slug":2286,"type":15},"Deployment","deployment",{"name":2288,"slug":2289,"type":15},"Git","git",{"name":2291,"slug":2292,"type":15},"GitHub","github",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-28T07:46:59.53536",{"slug":2297,"name":2297,"fn":2298,"description":2299,"org":2300,"tags":2301,"stars":32,"repoUrl":33,"updatedAt":2313},"choosing-trend-or-slope-view","visualize trends and growth over time","Clarify how to visualize change over a time range before building a trend. Use whenever the user asks how much something changed, grew, dropped, improved, or regressed between two points or periods — \"how much did X change from A to B\", \"before vs after\", \"start vs end\", \"week over week\", \"compare this month to last\", \"change over time\" — or mentions a \"slope chart\" \u002F \"slopegraph\". Two readings of \"change\" need different charts: the whole trend (a line, every interval) versus just the two endpoints (a slope, start vs end). Ask which they want, then render it. Not for choosing a saved insight ChartDisplayType in the insight editor.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2302,2303,2306,2309,2310],{"name":2204,"slug":2205,"type":15},{"name":2304,"slug":2305,"type":15},"Charts","charts",{"name":2307,"slug":2308,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":2311,"slug":2312,"type":15},"Reporting","reporting","2026-06-18T08:18:57.960157",56,{"items":2316,"total":2471},[2317,2330,2340,2353,2364,2379,2395,2408,2420,2435,2445,2461],{"slug":2318,"name":2318,"fn":2319,"description":2320,"org":2321,"tags":2322,"stars":2327,"repoUrl":2328,"updatedAt":2329},"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},[2323,2324,2325,2326],{"name":2204,"slug":2205,"type":15},{"name":24,"slug":25,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},35568,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fposthog","2026-07-28T05:34:11.117757",{"slug":2331,"name":2331,"fn":2332,"description":2333,"org":2334,"tags":2335,"stars":2327,"repoUrl":2328,"updatedAt":2339},"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},[2336,2337,2338],{"name":2204,"slug":2205,"type":15},{"name":2239,"slug":2240,"type":15},{"name":9,"slug":8,"type":15},"2026-06-08T08:08:33.693989",{"slug":2341,"name":2341,"fn":2342,"description":2343,"org":2344,"tags":2345,"stars":2327,"repoUrl":2328,"updatedAt":2352},"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},[2346,2347,2350,2351],{"name":2239,"slug":2240,"type":15},{"name":2348,"slug":2349,"type":15},"Data Warehouse","data-warehouse",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-18T08:22:57.67984",{"slug":2354,"name":2354,"fn":2355,"description":2356,"org":2357,"tags":2358,"stars":2327,"repoUrl":2328,"updatedAt":2363},"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},[2359,2360,2361,2362],{"name":2239,"slug":2240,"type":15},{"name":2348,"slug":2349,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},"2026-06-18T08:25:10.936787",{"slug":2365,"name":2365,"fn":2366,"description":2367,"org":2368,"tags":2369,"stars":2327,"repoUrl":2328,"updatedAt":2378},"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},[2370,2373,2376,2377],{"name":2371,"slug":2372,"type":15},"Alerting","alerting",{"name":2374,"slug":2375,"type":15},"Debugging","debugging",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-18T08:24:40.318583",{"slug":2380,"name":2380,"fn":2381,"description":2382,"org":2383,"tags":2384,"stars":2327,"repoUrl":2328,"updatedAt":2394},"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},[2385,2386,2389,2390,2393],{"name":2204,"slug":2205,"type":15},{"name":2387,"slug":2388,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},{"name":2391,"slug":2392,"type":15},"Operations","operations",{"name":9,"slug":8,"type":15},"2026-07-18T05:10:54.430898",{"slug":2396,"name":2396,"fn":2397,"description":2398,"org":2399,"tags":2400,"stars":2327,"repoUrl":2328,"updatedAt":2407},"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},[2401,2402,2403,2404],{"name":2259,"slug":2260,"type":15},{"name":2275,"slug":2276,"type":15},{"name":9,"slug":8,"type":15},{"name":2405,"slug":2406,"type":15},"Workflow Automation","workflow-automation","2026-07-28T05:34:12.167015",{"slug":2409,"name":2409,"fn":2410,"description":2411,"org":2412,"tags":2413,"stars":2327,"repoUrl":2328,"updatedAt":2419},"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},[2414,2415,2416,2417,2418],{"name":2204,"slug":2205,"type":15},{"name":2374,"slug":2375,"type":15},{"name":2222,"slug":2223,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-07T05:56:19.828048",{"slug":2421,"name":2421,"fn":2422,"description":2423,"org":2424,"tags":2425,"stars":2327,"repoUrl":2328,"updatedAt":2434},"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},[2426,2429,2430,2431],{"name":2427,"slug":2428,"type":15},"API Development","api-development",{"name":2222,"slug":2223,"type":15},{"name":9,"slug":8,"type":15},{"name":2432,"slug":2433,"type":15},"SDK","sdk","2026-06-08T08:08:34.929454",{"slug":2436,"name":2436,"fn":2437,"description":2438,"org":2439,"tags":2440,"stars":2327,"repoUrl":2328,"updatedAt":2444},"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},[2441,2442,2443],{"name":2427,"slug":2428,"type":15},{"name":2391,"slug":2392,"type":15},{"name":9,"slug":8,"type":15},"2026-07-15T05:29:58.442727",{"slug":2446,"name":2446,"fn":2447,"description":2448,"org":2449,"tags":2450,"stars":2327,"repoUrl":2328,"updatedAt":2460},"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},[2451,2452,2455,2456,2457],{"name":2259,"slug":2260,"type":15},{"name":2453,"slug":2454,"type":15},"Email","email",{"name":9,"slug":8,"type":15},{"name":2311,"slug":2312,"type":15},{"name":2458,"slug":2459,"type":15},"Slack","slack","2026-06-09T07:32:27.935712",{"slug":2462,"name":2462,"fn":2463,"description":2464,"org":2465,"tags":2466,"stars":2327,"repoUrl":2328,"updatedAt":2470},"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},[2467,2468,2469],{"name":2204,"slug":2205,"type":15},{"name":2427,"slug":2428,"type":15},{"name":9,"slug":8,"type":15},"2026-06-08T08:08:29.624498",231]