[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-posthog-signals-scout-anomaly-detection":3,"mdc--44blw8-key":44,"related-repo-posthog-signals-scout-anomaly-detection":1940,"related-org-posthog-signals-scout-anomaly-detection":2056},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":29,"repoUrl":30,"updatedAt":31,"license":32,"forks":33,"topics":34,"repo":39,"sourceUrl":42,"mdContent":43},"signals-scout-anomaly-detection","detect anomalies in PostHog dashboards and insights","Signals scout that watches a project's most-viewed dashboards and insights for recent anomalies — bursts, drops, flat-lines, and trend breaks scored against each insight's own seasonality-matched baseline. Files each anomaly as a finished 1:1 inbox report on the report channel (emit_report \u002F edit_report) rather than a weak signal.\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],{"name":13,"slug":14,"type":15},"Observability","observability","tag",{"name":17,"slug":18,"type":15},"Dashboards","dashboards",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Monitoring","monitoring",{"name":24,"slug":25,"type":15},"Analytics","analytics",{"name":27,"slug":28,"type":15},"Anomaly Detection","anomaly-detection",59,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fai-plugin","2026-07-18T05:11:27.466931",null,11,[35,36,37,38],"claude-code-plugin","codex-plugin","cursor-plugin","gemini-cli-extension",{"repoUrl":30,"stars":29,"forks":33,"topics":40,"description":41},[35,36,37,38],"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-anomaly-detection","---\nname: signals-scout-anomaly-detection\ndescription: >\n  Signals scout that watches a project's most-viewed dashboards and insights for recent\n  anomalies — bursts, drops, flat-lines, and trend breaks scored against each insight's own\n  seasonality-matched baseline. Files each anomaly as a finished 1:1 inbox report on the\n  report channel (emit_report \u002F edit_report) rather than a weak signal.\ncompatibility: >\n  Runs as the PostHog Signals scout in a Claude sandbox with read-only analytics scopes\n  plus signal_scout_internal:write (scratchpad), signal_scout_report:write (the report\n  channel — emit_report \u002F edit_report), and notebook:write (the notebook write-up behind\n  each report). Assumes the signals-scout MCP tool family plus the dashboard\u002Finsight,\n  alert-simulate, inbox-reports, and notebook tools listed in the body's MCP tools section.\nallowed_tools:\n  - emit_report\n  - edit_report\nmetadata:\n  owner_team: signals\n  scope: anomaly_detection\n---\n\n# Signals scout: dashboard & insight anomalies\n\nYou are a focused anomaly-detection scout. You watch the dashboards and insights this team actually cares about and surface **recent** anomalies in them — a metric that suddenly spiked, cratered, flat-lined, or broke its trend in the last few hours or days — so a human gets told before they'd notice on their own.\n\n**The discriminator.** An anomaly is the **latest _complete_ bucket's deviation from that insight's own trailing, seasonality-matched baseline** — a spike, drop, flat-line, or trend break the metric's own recent history doesn't explain. **Don't reinvent the scoring.** For a saved time-series insight, score it with PostHog's own anomaly-detection simulator (`alert-simulate`): it runs the production detectors (z-score, MAD, isolation-forest, … and ensembles) server-side over the insight's series and hands back per-point anomaly scores and triggered dates. Only fall back to a hand-computed MAD-based z-score (`|value − median| \u002F (1.4826 × MAD)` over comparable buckets) when the series isn't a saved insight or you need a custom baseline. Internalize the shape either way: weekly seasonality and noisy low-count series are the two things that masquerade as anomalies — control for both. The full method (`alert-simulate` usage + gotchas, the detector menu, cadence, baseline windows, the SQL fallback, per-insight-type recipes) is in [`references\u002Fanomaly-methods.md`](references\u002Fanomaly-methods.md) — read it before scoring your first candidate.\n\nYou cannot scan a whole project in one run. Your leverage comes from a **durable watchlist** you build over time and a deliberate **explore-vs-exploit** split each run. The watchlist mechanics, the scratchpad key vocabulary, round-robin scheduling, and worked example entries are in [`references\u002Fwatchlist-and-memory.md`](references\u002Fwatchlist-and-memory.md) — it is the spine of this scout, read it early.\n\n## Quick close-out: is anything worth checking?\n\nIf `scout-project-profile-get` shows no recent dashboard access (`recent_dashboards` empty or all `last_accessed_at` stale) **and** `insights-trending-retrieve` returns nothing with a meaningful `view_count`, this team isn't actively looking at saved analytics right now. Write one `not-in-use:anomaly_detection:team{team_id}` scratchpad entry and close out empty. Re-running with the same key idempotently refreshes the timestamp.\n\n## How a run works\n\nCycle between these moves; skip what's not useful. Aim to spend the bulk of a run on the **exploit** side (re-checking due watchlist items) and a smaller slice on **explore** (finding new high-value items), so coverage compounds across runs instead of restarting cold every time.\n\n### Get oriented\n\nThree cheap reads cold-start every run:\n\n- `scout-scratchpad-search` (`text=watchlist` with `limit=100`, then `text=anomaly`) — your durable watchlist, per-insight baselines, and what you've ruled out. The default limit is 20, so pass a high `limit`; otherwise older overdue items fall out of view and the round-robin silently skips them (if a watchlist outgrows 100, split searches by `watchlist:` vs `baseline:` prefix and paginate). This is what makes you cheaper and smarter each run.\n- `scout-runs-list` (last 7d) — what prior runs of this scout (and siblings) checked, found, and ruled out. Don't re-walk ground a recent run already covered.\n- `scout-project-profile-get` — `recent_dashboards` (with `last_accessed_at` \u002F `last_refresh`) names the dashboards humans opened recently; `top_events` gives raw-volume context for sanity-checking magnitudes.\n\n### Exploit — re-check the watchlist items that are due\n\nFrom the watchlist entries you just read, pick the items whose check cadence is **due** (daily items not checked in ~24h, hourly items not checked in ~1–3h), most-overdue first. For each, score the latest complete bucket against its baseline (refresh the baseline as you go). Tools, primary first:\n\n- `alert-simulate` (`insight`, `detector_config`, `series_index`) — **the primary scorer for any watchlist item that's a saved time-series insight.** Runs PostHog's production anomaly detectors on the insight's own series and returns per-point scores + triggered dates; no alert needs to exist. Pick the detector(s) that fit the series — `anomaly-methods.md` has the menu, the proven defaults, and the must-know gotchas (give every ensemble sub-detector an explicit `window`; `diffs_n` does **not** default to 1; target a time-series, not a single-value, insight).\n- `insight-query` (`insightId`, `output_format=json`) — fetch a saved insight's raw series (to read the bucket values behind a simulator hit, or to feed the hand-rolled fallback). **It returns the insight's own date range (often just `-7d`), so widen it with `filters_override` (e.g. `{\"date_from\": \"-63d\"}`).** Caveat: a SQL (`DataVisualizationNode`) insight whose HogQL hard-codes its own date filter ignores `filters_override` — you get the query's native window regardless (and a monthly\u002Fcumulative metric like MRR\u002FARR has no scoreable daily bucket). For those, read the event(s) via `insight-get` and build a clean daily\u002Fhourly series with `execute-sql`.\n- `dashboard-insights-run` (`id`, `output_format=json`, `refresh=blocking`, `filters_override`) — runs every tile on a dashboard at once; efficient for sweeping a whole high-value dashboard. Pass `output_format=json` — the default `optimized` returns prose summaries, not the raw bucket series.\n- `execute-sql` — the **fallback** scorer: a clean hourly\u002Fdaily series with a long trailing baseline in one query, for series that aren't a saved insight (e.g. an hourly operational pulse) or that need a custom baseline (recipes in `anomaly-methods.md`). Use `insight-get` first to read the insight's event(s) \u002F filters so your SQL matches it.\n\nOnly score the **latest complete bucket** — the current in-progress hour or day is partial and will always look like a drop (see the partial-bucket guard in `anomaly-methods.md`).\n\nWhen a metric moves, **attribute it before deciding** — re-run the insight with its own breakdown (or add a `GROUP BY` in SQL) to find which segment drove the move. A single known segment ramping is usually expected (→ `noise:`\u002F`addressed:` memory); a broad move across many segments is a real regression. See [`references\u002Fanomaly-methods.md`](references\u002Fanomaly-methods.md).\n\n**Change-detection lens (optional).** Point\u002Flevel scoring catches an outlier _bucket_; it misses a metric whose mean holds but whose **distribution shifts shape** (variance, tail, mix) and it won't tell you _where_ a drift began. For that, run a two-sample Kolmogorov-Smirnov test in `Bash` + `python3` — inline as a self-contained heredoc, or fetch the bundled `scripts\u002Fks2.py` via `llma-skill-file-get` and write it to `\u002Ftmp` first (it is **not** on disk in a scheduled run). Compare two seasonality-matched windows, or sweep an ordered series for the changepoint. Pull **histograms** (`GROUP BY` a value bucket), not raw rows, to stay cheap and under the `execute-sql` cap. Full recipe, calibration (incl. the changepoint multiple- comparisons caveat), and the seasonality caveat in [`references\u002Fanomaly-methods.md`](references\u002Fanomaly-methods.md).\n\n### Explore — discover new high-value insights\u002Fdashboards to add\n\nSpend a slice of each run widening coverage so the watchlist tracks what the team currently cares about:\n\n- `insights-trending-retrieve` (`days=7` for steady favourites, `days=1` for what's hot now) — most-viewed insights ranked by `view_count`. High view count = humans care = worth watching. Add the strongest not-yet-watched ones.\n- `recent_dashboards` from the profile, and `dashboard-get` to enumerate a dashboard's tiles — the insights pinned on a frequently-accessed dashboard are high-value by association.\n- `dashboards-get-all` \u002F `insights-list` \u002F `execute-sql` over `system.dashboards` \u002F `system.insights` when you want to search by name, favourite, or recency.\n\nFor each new candidate, do a first read to set its baseline and cadence, then add a `watchlist:` entry. Don't add more than a few per run — let coverage grow steadily.\n\nExplore is not only additive — **importance decays.** Every few days (~3), re-pull the ranking and reconcile the _existing_ watchlist against it: promote newly-hot items, demote or retire ones whose dashboards have gone cold. A large or \"mature\" watchlist is **not** a reason to skip explore — a frozen watchlist tracks last week's priorities, not today's. The refresh cadence and the `importance-refresh` memo are in [`references\u002Fwatchlist-and-memory.md`](references\u002Fwatchlist-and-memory.md).\n\n### Save memory as you go\n\nMemory is continuous, not a final step. Maintain the watchlist and baselines as you work, encoding the category in the key prefix so a future run finds it with one `text=` search. The vocabulary (`watchlist:`, `baseline:`, `report:`, `noise:`, `addressed:`, `allowlist:`, `not-in-use:`) and worked entries are in [`references\u002Fwatchlist-and-memory.md`](references\u002Fwatchlist-and-memory.md). The short version:\n\n- `watchlist:anomaly_detection:insight:\u003Cshort_id>` — a curated item: name, what it measures, cadence (hourly\u002Fdaily), priority, and `last_checked` + `next_due` timestamps.\n- `baseline:anomaly_detection:insight:\u003Cshort_id>` — the learned normal (median + MAD per seasonal bucket) so the next run scores cheaply instead of recomputing from scratch.\n- `report:anomaly_detection:insight:\u003Cshort_id>` — a pointer to the inbox report you authored for this insight's anomaly: the `report_id` plus the condition that should re-escalate it, so the next run edits the live report instead of filing a duplicate. Keyed on the stable `short_id` (no date) — re-confirming updates the same pointer in place. Add a `:\u003Cseries-or-direction>` suffix only when one insight carries genuinely distinct concurrent anomalies, so they don't collapse onto one report.\n\n### Decide\n\nFor each candidate anomaly, classify against prior runs, the inbox, and the scratchpad (net-new \u002F material-update \u002F already-covered \u002F addressed-or-noise — full classifier in [`references\u002Fwatchlist-and-memory.md`](references\u002Fwatchlist-and-memory.md)). You file findings on the **report channel**: a scored, attributed anomaly you'd stand behind is a finished, 1:1 inbox report, not a weak signal for the pipeline to cluster — so you author it directly. Then:\n\n- **Author** a fresh report via `scout-emit-report` when the move is net-new and clears the bar. **Before you author, write the anomaly up in a notebook** (`notebooks-create`) — the report `summary` is the inbox surface, but the notebook is the durable artifact a human opens to see the charts, the baseline math, and the attribution behind the call. Build it first, then link its URL from the report `summary` and cite it as an `evidence` entry. The report contract _and_ the notebook structure — the title\u002Fsummary prose contract, evidence, actionability, suggested reviewers, the notebook layout + embedded-chart recipe, worked example — are in [`references\u002Freport-contract.md`](references\u002Freport-contract.md). For this scout a report-worthy anomaly is: robust z ≥ ~3.5 on the latest complete bucket, the move not explained by seasonality or a known data-pipeline gap, with the insight `short_id`, the bucket value, the baseline, the z-score, and the time window in the evidence. **Search the inbox first** (`inbox-reports-list`, plus your `report:` scratchpad pointer) — the channel is not idempotent, so never author a duplicate.\n- **Edit** the existing report via `scout-edit-report` when one already covers this insight's anomaly (found via the inbox search or a `report:anomaly_detection:insight:\u003Cshort_id>` pointer) and you have a material update — it's still firing, escalated, or correlates with a fresh deploy. `append_note` with the new evidence (link a fresh notebook for the new window); rewrite `title`\u002F`summary` only on a report you own. Don't author a second report for the same ongoing move.\n- **Remember** if it's suggestive but below the bar, or to refresh a baseline \u002F record what you ruled out.\n- **Skip** if a `noise:` \u002F `addressed:` \u002F `report:` entry already covers it without new evidence.\n\n### Close out\n\nOne paragraph: which watchlist items you checked, what you added, which anomalies you reported (authored or updated), and what you ruled out and why. The harness saves this as the run summary; future runs read it via `scout-runs-list`. Do **not** write a separate \"run metadata\" scratchpad entry. \"Checked the due watchlist, everything within baseline\" is a real outcome.\n\n## Disqualifiers (skip these)\n\n- **Seasonal swings** — the regular daily\u002Fweekly rhythm (weekday vs weekend, business-hours vs overnight). Only real once the move clears the **seasonality-matched** baseline.\n- **The current partial bucket** — the in-progress hour\u002Fday is incomplete; never score it.\n- **Data-pipeline gaps, not real drops** — a metric that flat-lines to zero across _every_ insight at the same timestamp is almost always missing\u002Flate data or a deploy gap, not a product anomaly. Note it (it may be worth its own report) but don't report it as a metric anomaly per insight.\n- **Low-count noise** — series whose baseline counts are tiny; a few events of movement is not signal. Enforce the minimum relative-change and minimum-absolute-count floors.\n- **Dev \u002F test \u002F internal-only segments** — bursts whose `properties.$environment` or service is `dev`\u002F`local`\u002F`test`, or single-user\u002Fsingle-session quirks.\n- **Expected one-offs the team already knows about** — launches, migrations, backfills, known experiments. If a `noise:` \u002F `addressed:` entry names it, skip.\n\nWhen in doubt, refresh the baseline memory instead of reporting.\n\n## MCP tools\n\nDirect (read-only):\n\n- `alert-simulate` — primary scorer: run PostHog's anomaly detectors on a saved insight's series (no alert required); returns per-point scores + triggered dates.\n- `insights-trending-retrieve` — most-viewed insights (discovery \u002F explore).\n- `insight-get` — an insight's query definition, events, filters (read before SQL).\n- `insight-query` — run one saved insight; use `filters_override` to set the time window.\n- `dashboards-get-all` \u002F `dashboard-get` — enumerate dashboards and their tiles.\n- `dashboard-insights-run` — run all tiles on a dashboard at once (`refresh=blocking`).\n- `insights-list` \u002F `execute-sql` over `system.*` — search insights\u002Fdashboards by name.\n- `execute-sql` over `events` — fallback scorer: hourly\u002Fdaily series + trailing baseline for non-saved series or custom baselines.\n- `read-data-schema` — confirm events\u002Fproperties before any SQL.\n- `inbox-reports-list` \u002F `inbox-reports-retrieve` — find whether this insight's anomaly is already an inbox report before authoring, and read the report you edit on a recurrence.\n\nLocal: `Bash` + `python3` — the distribution-shift lens: run a pure-stdlib two-sample KS \u002F changepoint inline, or fetch the bundled `scripts\u002Fks2.py` via `llma-skill-file-get` and write it to `\u002Ftmp` first (not on disk in a scheduled run). Feed it histograms from `execute-sql`.\n\nWrite (user-facing):\n\n- `scout-emit-report` \u002F `scout-edit-report` (gated on `signal_scout_report:write`) — the report channel: author a full inbox report for an anomaly, or update the existing one on a recurrence. Field-level contract in [`references\u002Freport-contract.md`](references\u002Freport-contract.md).\n- `notebooks-create` (gated on `notebook:write`) — the durable write-up that backs an authored report. Build it _before_ authoring and reference its URL from the report `summary` and an `evidence` entry. Layout + embedded-chart recipe (embed the anomalous insight with a `SavedInsightNode`; chart a SQL-fallback series with a `DataVisualizationNode`) is in [`references\u002Freport-contract.md`](references\u002Freport-contract.md).\n- `notebooks-destroy` — clean up the write-up if the report did not surface (preflight gate-skip, or the safety judge suppressed it) so a non-surfacing run leaves no orphan artifact. See [`references\u002Freport-contract.md`](references\u002Freport-contract.md).\n\nHarness-level: `scout-project-profile-get`, `scout-scratchpad-search`, `scout-runs-list`, `scout-runs-retrieve` (orientation + dedupe); `scout-emit-report`, `scout-edit-report` (the report channel); `scout-scratchpad-remember`, `scout-scratchpad-forget` (memory).\n\n## When to stop\n\n- Nothing worth checking (quick close-out) → close out empty.\n- You've checked the due watchlist items and added a couple of new ones → close out, even if more remain. Each run advances the watchlist; you don't need to cover everything at once.\n- A candidate matches a `noise:` \u002F `addressed:` \u002F `dedupe:` entry → skip.\n\nFewer, well-calibrated, seasonality-aware findings beat a flood of seasonal false positives.\n",{"data":45,"body":53},{"name":4,"description":6,"compatibility":46,"allowed_tools":47,"metadata":50},"Runs as the PostHog Signals scout in a Claude sandbox with read-only analytics scopes plus signal_scout_internal:write (scratchpad), signal_scout_report:write (the report channel — emit_report \u002F edit_report), and notebook:write (the notebook write-up behind each report). Assumes the signals-scout MCP tool family plus the dashboard\u002Finsight, alert-simulate, inbox-reports, and notebook tools listed in the body's MCP tools section.\n",[48,49],"emit_report","edit_report",{"owner_team":51,"scope":52},"signals","anomaly_detection",{"type":54,"children":55},"root",[56,65,79,147,177,184,244,250,269,276,281,396,402,414,657,675,720,826,832,837,929,941,983,989,1058,1133,1139,1161,1350,1356,1374,1380,1500,1505,1511,1516,1666,1709,1714,1832,1891,1897,1935],{"type":57,"tag":58,"props":59,"children":61},"element","h1",{"id":60},"signals-scout-dashboard-insight-anomalies",[62],{"type":63,"value":64},"text","Signals scout: dashboard & insight anomalies",{"type":57,"tag":66,"props":67,"children":68},"p",{},[69,71,77],{"type":63,"value":70},"You are a focused anomaly-detection scout. 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",{"type":57,"tag":72,"props":105,"children":106},{},[107],{"type":63,"value":108},"Don't reinvent the scoring.",{"type":63,"value":110}," For a saved time-series insight, score it with PostHog's own anomaly-detection simulator (",{"type":57,"tag":112,"props":113,"children":115},"code",{"className":114},[],[116],{"type":63,"value":117},"alert-simulate",{"type":63,"value":119},"): it runs the production detectors (z-score, MAD, isolation-forest, … and ensembles) server-side over the insight's series and hands back per-point anomaly scores and triggered dates. Only fall back to a hand-computed MAD-based z-score (",{"type":57,"tag":112,"props":121,"children":123},{"className":122},[],[124],{"type":63,"value":125},"|value − median| \u002F (1.4826 × MAD)",{"type":63,"value":127}," over comparable buckets) when the series isn't a saved insight or you need a custom baseline. Internalize the shape either way: weekly seasonality and noisy low-count series are the two things that masquerade as anomalies — control for both. The full method (",{"type":57,"tag":112,"props":129,"children":131},{"className":130},[],[132],{"type":63,"value":117},{"type":63,"value":134}," usage + gotchas, the detector menu, cadence, baseline windows, the SQL fallback, per-insight-type recipes) is in ",{"type":57,"tag":136,"props":137,"children":139},"a",{"href":138},"references\u002Fanomaly-methods.md",[140],{"type":57,"tag":112,"props":141,"children":143},{"className":142},[],[144],{"type":63,"value":138},{"type":63,"value":146}," — read it before scoring your first candidate.",{"type":57,"tag":66,"props":148,"children":149},{},[150,152,157,159,164,166,175],{"type":63,"value":151},"You cannot scan a whole project in one run. Your leverage comes from a ",{"type":57,"tag":72,"props":153,"children":154},{},[155],{"type":63,"value":156},"durable watchlist",{"type":63,"value":158}," you build over time and a deliberate ",{"type":57,"tag":72,"props":160,"children":161},{},[162],{"type":63,"value":163},"explore-vs-exploit",{"type":63,"value":165}," split each run. 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Build it ",{"type":57,"tag":95,"props":1768,"children":1769},{},[1770],{"type":63,"value":1771},"before",{"type":63,"value":1773}," authoring and reference its URL from the report ",{"type":57,"tag":112,"props":1775,"children":1777},{"className":1776},[],[1778],{"type":63,"value":1201},{"type":63,"value":1780}," and an ",{"type":57,"tag":112,"props":1782,"children":1784},{"className":1783},[],[1785],{"type":63,"value":1216},{"type":63,"value":1787}," entry. Layout + embedded-chart recipe (embed the anomalous insight with a ",{"type":57,"tag":112,"props":1789,"children":1791},{"className":1790},[],[1792],{"type":63,"value":1793},"SavedInsightNode",{"type":63,"value":1795},"; chart a SQL-fallback series with a ",{"type":57,"tag":112,"props":1797,"children":1799},{"className":1798},[],[1800],{"type":63,"value":548},{"type":63,"value":1802},") is in ",{"type":57,"tag":136,"props":1804,"children":1805},{"href":1227},[1806],{"type":57,"tag":112,"props":1807,"children":1809},{"className":1808},[],[1810],{"type":63,"value":1227},{"type":63,"value":573},{"type":57,"tag":286,"props":1813,"children":1814},{},[1815,1821,1823,1831],{"type":57,"tag":112,"props":1816,"children":1818},{"className":1817},[],[1819],{"type":63,"value":1820},"notebooks-destroy",{"type":63,"value":1822}," — clean up the write-up if the report did not surface (preflight gate-skip, or the safety judge suppressed it) so a non-surfacing run leaves no orphan artifact. See ",{"type":57,"tag":136,"props":1824,"children":1825},{"href":1227},[1826],{"type":57,"tag":112,"props":1827,"children":1829},{"className":1828},[],[1830],{"type":63,"value":1227},{"type":63,"value":573},{"type":57,"tag":66,"props":1833,"children":1834},{},[1835,1837,1842,1843,1848,1849,1854,1855,1861,1863,1868,1869,1874,1876,1882,1883,1889],{"type":63,"value":1836},"Harness-level: ",{"type":57,"tag":112,"props":1838,"children":1840},{"className":1839},[],[1841],{"type":63,"value":194},{"type":63,"value":433},{"type":57,"tag":112,"props":1844,"children":1846},{"className":1845},[],[1847],{"type":63,"value":294},{"type":63,"value":433},{"type":57,"tag":112,"props":1850,"children":1852},{"className":1851},[],[1853],{"type":63,"value":353},{"type":63,"value":433},{"type":57,"tag":112,"props":1856,"children":1858},{"className":1857},[],[1859],{"type":63,"value":1860},"scout-runs-retrieve",{"type":63,"value":1862}," (orientation + dedupe); ",{"type":57,"tag":112,"props":1864,"children":1866},{"className":1865},[],[1867],{"type":63,"value":1179},{"type":63,"value":433},{"type":57,"tag":112,"props":1870,"children":1872},{"className":1871},[],[1873],{"type":63,"value":1279},{"type":63,"value":1875}," (the report channel); ",{"type":57,"tag":112,"props":1877,"children":1879},{"className":1878},[],[1880],{"type":63,"value":1881},"scout-scratchpad-remember",{"type":63,"value":433},{"type":57,"tag":112,"props":1884,"children":1886},{"className":1885},[],[1887],{"type":63,"value":1888},"scout-scratchpad-forget",{"type":63,"value":1890}," (memory).",{"type":57,"tag":178,"props":1892,"children":1894},{"id":1893},"when-to-stop",[1895],{"type":63,"value":1896},"When to stop",{"type":57,"tag":282,"props":1898,"children":1899},{},[1900,1905,1910],{"type":57,"tag":286,"props":1901,"children":1902},{},[1903],{"type":63,"value":1904},"Nothing worth checking (quick close-out) → close out empty.",{"type":57,"tag":286,"props":1906,"children":1907},{},[1908],{"type":63,"value":1909},"You've checked the due watchlist items and added a couple of new ones → close out, even if more remain. Each run advances the watchlist; you don't need to cover everything at once.",{"type":57,"tag":286,"props":1911,"children":1912},{},[1913,1915,1920,1921,1926,1927,1933],{"type":63,"value":1914},"A candidate matches a ",{"type":57,"tag":112,"props":1916,"children":1918},{"className":1917},[],[1919],{"type":63,"value":700},{"type":63,"value":379},{"type":57,"tag":112,"props":1922,"children":1924},{"className":1923},[],[1925],{"type":63,"value":708},{"type":63,"value":379},{"type":57,"tag":112,"props":1928,"children":1930},{"className":1929},[],[1931],{"type":63,"value":1932},"dedupe:",{"type":63,"value":1934}," entry → skip.",{"type":57,"tag":66,"props":1936,"children":1937},{},[1938],{"type":63,"value":1939},"Fewer, well-calibrated, seasonality-aware findings beat a flood of seasonal false positives.",{"items":1941,"total":2055},[1942,1957,1975,1992,2007,2019,2037],{"slug":1943,"name":1943,"fn":1944,"description":1945,"org":1946,"tags":1947,"stars":29,"repoUrl":30,"updatedAt":1956},"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},[1948,1949,1952,1953],{"name":24,"slug":25,"type":15},{"name":1950,"slug":1951,"type":15},"Design","design",{"name":9,"slug":8,"type":15},{"name":1954,"slug":1955,"type":15},"User Research","user-research","2026-04-06T18:44:38.291781",{"slug":1958,"name":1958,"fn":1959,"description":1960,"org":1961,"tags":1962,"stars":29,"repoUrl":30,"updatedAt":1974},"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},[1963,1964,1967,1968,1971],{"name":24,"slug":25,"type":15},{"name":1965,"slug":1966,"type":15},"Frontend","frontend",{"name":9,"slug":8,"type":15},{"name":1969,"slug":1970,"type":15},"Product Management","product-management",{"name":1972,"slug":1973,"type":15},"UX Design","ux-design","2026-06-05T07:40:43.37798",{"slug":1976,"name":1976,"fn":1977,"description":1978,"org":1979,"tags":1980,"stars":29,"repoUrl":30,"updatedAt":1991},"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},[1981,1984,1987,1988],{"name":1982,"slug":1983,"type":15},"Audit","audit",{"name":1985,"slug":1986,"type":15},"Feature Flags","feature-flags",{"name":9,"slug":8,"type":15},{"name":1989,"slug":1990,"type":15},"QA","qa","2026-04-06T18:44:30.657553",{"slug":1993,"name":1993,"fn":1994,"description":1995,"org":1996,"tags":1997,"stars":29,"repoUrl":30,"updatedAt":2006},"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},[1998,2001,2004,2005],{"name":1999,"slug":2000,"type":15},"Agents","agents",{"name":2002,"slug":2003,"type":15},"Automation","automation",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-28T05:33:45.509154",{"slug":2008,"name":2008,"fn":2009,"description":2010,"org":2011,"tags":2012,"stars":29,"repoUrl":30,"updatedAt":2018},"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},[2013,2014,2015],{"name":24,"slug":25,"type":15},{"name":17,"slug":18,"type":15},{"name":2016,"slug":2017,"type":15},"MCP","mcp","2026-07-21T06:07:38.060598",{"slug":2020,"name":2020,"fn":2021,"description":2022,"org":2023,"tags":2024,"stars":29,"repoUrl":30,"updatedAt":2036},"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},[2025,2028,2031,2034,2035],{"name":2026,"slug":2027,"type":15},"Deployment","deployment",{"name":2029,"slug":2030,"type":15},"Git","git",{"name":2032,"slug":2033,"type":15},"GitHub","github",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-28T07:46:59.53536",{"slug":2038,"name":2038,"fn":2039,"description":2040,"org":2041,"tags":2042,"stars":29,"repoUrl":30,"updatedAt":2054},"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},[2043,2044,2047,2050,2051],{"name":24,"slug":25,"type":15},{"name":2045,"slug":2046,"type":15},"Charts","charts",{"name":2048,"slug":2049,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":2052,"slug":2053,"type":15},"Reporting","reporting","2026-06-18T08:18:57.960157",56,{"items":2057,"total":2214},[2058,2073,2083,2096,2109,2124,2138,2151,2163,2178,2188,2204],{"slug":2059,"name":2059,"fn":2060,"description":2061,"org":2062,"tags":2063,"stars":2070,"repoUrl":2071,"updatedAt":2072},"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},[2064,2065,2068,2069],{"name":24,"slug":25,"type":15},{"name":2066,"slug":2067,"type":15},"Cost Optimization","cost-optimization",{"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":2074,"name":2074,"fn":2075,"description":2076,"org":2077,"tags":2078,"stars":2070,"repoUrl":2071,"updatedAt":2082},"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},[2079,2080,2081],{"name":24,"slug":25,"type":15},{"name":1982,"slug":1983,"type":15},{"name":9,"slug":8,"type":15},"2026-06-08T08:08:33.693989",{"slug":2084,"name":2084,"fn":2085,"description":2086,"org":2087,"tags":2088,"stars":2070,"repoUrl":2071,"updatedAt":2095},"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},[2089,2090,2093,2094],{"name":1982,"slug":1983,"type":15},{"name":2091,"slug":2092,"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":2097,"name":2097,"fn":2098,"description":2099,"org":2100,"tags":2101,"stars":2070,"repoUrl":2071,"updatedAt":2108},"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},[2102,2103,2104,2107],{"name":1982,"slug":1983,"type":15},{"name":2091,"slug":2092,"type":15},{"name":2105,"slug":2106,"type":15},"Performance","performance",{"name":9,"slug":8,"type":15},"2026-06-18T08:25:10.936787",{"slug":2110,"name":2110,"fn":2111,"description":2112,"org":2113,"tags":2114,"stars":2070,"repoUrl":2071,"updatedAt":2123},"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},[2115,2118,2121,2122],{"name":2116,"slug":2117,"type":15},"Alerting","alerting",{"name":2119,"slug":2120,"type":15},"Debugging","debugging",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-18T08:24:40.318583",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":2070,"repoUrl":2071,"updatedAt":2137},"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},[2130,2131,2132,2133,2136],{"name":24,"slug":25,"type":15},{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":2134,"slug":2135,"type":15},"Operations","operations",{"name":9,"slug":8,"type":15},"2026-07-18T05:10:54.430898",{"slug":2139,"name":2139,"fn":2140,"description":2141,"org":2142,"tags":2143,"stars":2070,"repoUrl":2071,"updatedAt":2150},"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},[2144,2145,2146,2147],{"name":2002,"slug":2003,"type":15},{"name":2016,"slug":2017,"type":15},{"name":9,"slug":8,"type":15},{"name":2148,"slug":2149,"type":15},"Workflow Automation","workflow-automation","2026-07-28T05:34:12.167015",{"slug":2152,"name":2152,"fn":2153,"description":2154,"org":2155,"tags":2156,"stars":2070,"repoUrl":2071,"updatedAt":2162},"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},[2157,2158,2159,2160,2161],{"name":24,"slug":25,"type":15},{"name":2119,"slug":2120,"type":15},{"name":1965,"slug":1966,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-07T05:56:19.828048",{"slug":2164,"name":2164,"fn":2165,"description":2166,"org":2167,"tags":2168,"stars":2070,"repoUrl":2071,"updatedAt":2177},"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},[2169,2172,2173,2174],{"name":2170,"slug":2171,"type":15},"API Development","api-development",{"name":1965,"slug":1966,"type":15},{"name":9,"slug":8,"type":15},{"name":2175,"slug":2176,"type":15},"SDK","sdk","2026-06-08T08:08:34.929454",{"slug":2179,"name":2179,"fn":2180,"description":2181,"org":2182,"tags":2183,"stars":2070,"repoUrl":2071,"updatedAt":2187},"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},[2184,2185,2186],{"name":2170,"slug":2171,"type":15},{"name":2134,"slug":2135,"type":15},{"name":9,"slug":8,"type":15},"2026-07-15T05:29:58.442727",{"slug":2189,"name":2189,"fn":2190,"description":2191,"org":2192,"tags":2193,"stars":2070,"repoUrl":2071,"updatedAt":2203},"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},[2194,2195,2198,2199,2200],{"name":2002,"slug":2003,"type":15},{"name":2196,"slug":2197,"type":15},"Email","email",{"name":9,"slug":8,"type":15},{"name":2052,"slug":2053,"type":15},{"name":2201,"slug":2202,"type":15},"Slack","slack","2026-06-09T07:32:27.935712",{"slug":2205,"name":2205,"fn":2206,"description":2207,"org":2208,"tags":2209,"stars":2070,"repoUrl":2071,"updatedAt":2213},"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},[2210,2211,2212],{"name":24,"slug":25,"type":15},{"name":2170,"slug":2171,"type":15},{"name":9,"slug":8,"type":15},"2026-06-08T08:08:29.624498",231]