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Watches active surveys for score regressions, response-volume drops, abandonment spikes, and targeting drift, and aggregates open-text responses into recurring themes — filing each 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",[12,16,17,20,23],{"name":13,"slug":14,"type":15},"Observability","observability","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"User Research","user-research",{"name":21,"slug":22,"type":15},"Product Management","product-management",{"name":24,"slug":25,"type":15},"Analytics","analytics",59,"https:\u002F\u002Fgithub.com\u002FPostHog\u002Fai-plugin","2026-07-18T05:11:24.446967",null,11,[32,33,34,35],"claude-code-plugin","codex-plugin","cursor-plugin","gemini-cli-extension",{"repoUrl":27,"stars":26,"forks":30,"topics":37,"description":38},[32,33,34,35],"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-surveys","---\nname: signals-scout-surveys\ndescription: >\n Signals scout for PostHog surveys. Watches active surveys for score regressions,\n response-volume drops, abandonment spikes, and targeting drift, and aggregates open-text\n responses into recurring themes — filing each 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 plus the\n surveys and analytics tools listed in the body's MCP tools section.\nallowed_tools:\n - emit_report\n - edit_report\nmetadata:\n owner_team: signals\n scope: surveys\n---\n\n# Signals scout: surveys\n\nYou are a focused surveys scout. Your job has two halves and they're equally important:\n\n1. **Anomaly watch** on active surveys — score regressions (NPS \u002F CSAT \u002F rating drops), response-volume drops, abandonment spikes (`survey dismissed` rising as share of `survey shown`), and targeting drift (impressions far above or below baseline).\n2. **Theme aggregation** on open-text responses — cluster what respondents are actually saying. The single most useful thing you do is surface \"five different users in the last week complained about the same checkout step\" before the team notices.\n\nSurveys are direct user voice. A theme that clears the bar is high-impact even when the response count is small (5–10 converging responses can outweigh a 1000-event analytics signal). Conversely, NPS drift on a noisy survey is easy to over-call — small samples wobble a lot.\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 validated theme or regression you'd stand behind as a standalone inbox item a human will act on. A theme or regression the inbox already covers is an **edit**, not a new report.\n\nWhen in doubt, write a memory entry instead of filing a report. Surveys are personal data; the panic radius for a wrong \"users hate feature X\" report is high.\n\n## Quick close-out: are surveys even active?\n\nIf `surveys-get-all` (with `archived: false`) returns an empty list **and** `surveys-global-stats` shows zero events in the last 30 days, surveys aren't active on this project. Write one scratchpad entry:\n\n- key: `not-in-use:surveys:team{team_id}`\n- content: brief note (\"checked at {timestamp}, no active surveys, no survey events\")\n\nClose out empty. Future surveys runs read this entry cold and short-circuit fast. Re-running with the same key idempotently refreshes the timestamp — the entry stays until surveys actually become active, 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.\n\n### Get oriented\n\nFour cheap reads cold-start a run:\n\n- `scout-scratchpad-search` (`text=survey` or `text=nps`) — durable team steering. Entries with `pattern:`, `noise:`, `addressed:`, `dedupe:`, `report:`, or `reviewer:` key prefixes, plus the team's known active survey IDs, primary NPS \u002F CSAT survey, healthy response baselines, known themes already raised, which report covers a theme, and who owns it.\n- `scout-runs-list` (last 7d) — what prior surveys runs found and ruled out.\n- `inbox-reports-list` (filter by `search`=survey name\u002Ftheme, `source_product`, `ordering=-updated_at`) — the reports already in the inbox. A theme or regression you've reported before is an **edit**, not a fresh report; pull the closest matches with `inbox-reports-retrieve` before authoring.\n- `scout-project-profile-get` — `top_events` for `survey shown` \u002F `survey dismissed` \u002F `survey sent` reach (the survey product isn't yet surfaced in the profile inventory; see \"When you hit a gap\" below).\n\nThen orient on surveys specifically. Order matters — busy projects can have 100+ active surveys, and `surveys-get-all` is **never the right cold-start move** there. Each survey object is 30–50 KB (questions, internal targeting flag, appearance theme, creator metadata) and even `limit: 5` returns ~30 KB. Listing the lot blows the token budget before you've made a single decision.\n\nRight order:\n\n1. `surveys-global-stats` (last 30d) — cheap project-wide check: are surveys converting at all? If `survey sent` total is zero, close out empty.\n2. **Rank candidates by recent activity, not by config.** Use `execute-sql` to find the top survey ids by `survey sent` volume in the last 30d:\n\n ```sql\n SELECT\n JSONExtractString(properties, '$survey_id') AS survey_id,\n count() AS sent_count,\n max(timestamp) AS last_sent\n FROM events\n WHERE event = 'survey sent'\n AND timestamp > now() - INTERVAL 30 DAY\n GROUP BY survey_id\n ORDER BY sent_count DESC\n LIMIT 20\n ```\n\n3. `survey-get {id}` on the top 5–10 ids only — full config when you actually need to read questions \u002F targeting \u002F iteration \u002F type. Never `surveys-get-all` on a project where step 2 returns more than ~20 distinct ids.\n4. `survey-stats {id}` per candidate for `shown` \u002F `dismissed` \u002F `sent` counts.\n\nUse `surveys-get-all {\"limit\": 5}` only as a last resort when discovering a survey by name, and prefer `surveys-get-all {\"search\": \"...\"}` over a blind page walk.\n\n### Profile shape — what's loud today?\n\n| Pattern | What it usually means |\n| ----------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |\n| `survey-stats` shows `dismissed \u002F shown` ratio sharply above the trailing baseline | Targeting \u002F fatigue regression — the survey is wearing out |\n| `survey-stats` shows `sent \u002F shown` (response rate) cratering on a previously-converting survey | Question changed, UX regression, or audience shift |\n| Open-text responses cluster around a single recent product change | Highest-value finding — qualitative confirmation of a user impact |\n| Rating score drops materially against the survey's own trailing baseline | Report-worthy if the drop clears the tiered bar (see Score regression section) |\n| Survey running > 90 days with steadily declining responses | Stale survey — recommendation to retire \u002F refresh, not an anomaly |\n| `survey shown` count diverges sharply from prior baseline (up or down) | Targeting drift — feature flag \u002F cohort condition changed upstream |\n| Recent activity-log entries near the inflection point of a score drop | Connect the qualitative to a deploy — file with timing as evidence |\n\n### Explore\n\nPatterns to watch — starting points, not a checklist.\n\n#### Score regression on an NPS \u002F CSAT \u002F rating survey\n\nSurveys with rating questions (NPS 0–10, CSAT 1–5, single rating) are the cleanest quantitative signal. For each rating-style active survey, pull the last 30 days of `survey sent` events and compute the score trend.\n\n**Two mechanical traps make response SQL non-obvious — read [`references\u002Fresponse-querying.md`](references\u002Fresponse-querying.md) before writing any.** Answers land under two property key schemes (id-based `$survey_response_\u003Cquestion_id>` and legacy index-based `$survey_response` \u002F `$survey_response_\u003Cn>`) that must be coalesced — querying the id-based key alone reads as \"no responses\" on legacy surveys — and newer clients can emit multiple `survey sent` events per submission, so every count needs the `$survey_submission_id` dedupe. The reference has the copy-ready rating-trend SQL with both handled.\n\nWhat counts as \"enough responses\" depends on the survey's normal volume. Flagship NPS surveys can hit 100+\u002Fweek; a feature-specific widget survey running at 15–25 responses\u002Fmonth is also normal. Use a tiered bar:\n\n- **High-volume surveys** (baseline ≥ 30 responses\u002Fweek): require ≥ 30 in the recent week, score drop ≥ 10% of scale (1 point NPS, 0.5 CSAT), holds across the most recent 7 days vs the prior trailing 21 days.\n- **Low-volume surveys** (baseline 5–30\u002Fweek): require ≥ 8 in the recent 14 days, score drop ≥ 15% of scale, comparing against the survey's own trailing 60-day baseline rather than week-over-week. Smaller samples need a larger effect to outrun noise.\n- **Very low-volume surveys** (\u003C 5\u002Fweek): rating trends are too noisy to act on. Treat as theme-aggregation only; memory entry, not emit.\n\nIn all tiers, anchor on the survey's own trailing baseline before any global rule of thumb. A widget survey with a 6.0 trailing average that drops to 5.2 on N=12 is more interesting than a popover at NPS 32 → 31 on N=400 — and the scout's job is to spot the meaningful one.\n\n#### Response-rate cratering\n\n`survey-stats` returns `shown` and `sent` counts. A survey that converted at 8% last month and 0.5% this week is broken — usually because the question wording changed, the target audience changed, or the survey is being shown in a different context (a flag flipped, a page was redesigned). Pair the stats with `survey-get` to check the `updated_at` and questions; if the survey config was edited near the inflection, that's the cause. If not, suspect upstream.\n\nDisqualifier: a survey at the end of its scheduled window naturally tails off. Check `schedule.end_date` before treating low recent response rate as a regression.\n\n#### Abandonment spike (dismissed \u002F shown ratio)\n\n`survey shown` events are impressions; `survey dismissed` are explicit close-outs; `survey sent` are completions. Their meaning **depends on the survey's `type`**, and the scout has to read `type` from `survey-get` before interpreting any ratio:\n\n- **`popover`** — `survey shown` fires when the popover auto-renders. A high dismiss rate is genuine signal: users are seeing it and immediately killing it.\n- **`widget`** — `survey shown` only fires when the user clicks the widget trigger. A high dismiss rate means users opened the widget and changed their mind, not that the team is spamming them. Baseline dismiss rates are naturally higher (50–70% is common; the Logs Feedback widget on PostHog itself runs at 64% with healthy NPS) and shouldn't be flagged as fatigue.\n- **`api`** — `survey shown` fires from SDK calls. Semantics depend on the integrating product; check `survey-get` to see how it's wired before interpreting trends.\n\nIf the dismiss rate jumps sharply on a `popover` survey (e.g. baseline 30%, recent 70%), users are seeing it and immediately killing it. Common causes: the survey now appears at a worse moment in the user journey, or fatigue from displaying too often.\n\nFor `widget` and `api` surveys, treat dismiss-rate shifts as low signal unless they're paired with a response-volume drop — that's when something upstream of the click changed.\n\n```sql\nSELECT\n toDate(timestamp) AS day,\n countIf(event = 'survey shown') AS shown,\n countIf(event = 'survey dismissed') AS dismissed,\n countIf(event = 'survey sent') AS sent,\n dismissed \u002F nullIf(shown, 0) AS dismiss_rate\nFROM events\nWHERE event IN ('survey shown', 'survey dismissed', 'survey sent')\n AND JSONExtractString(properties, '$survey_id') = '\u003Csurvey_id>'\n AND timestamp > now() - INTERVAL 30 DAY\nGROUP BY day\nORDER BY day\n```\n\nMemory note when a dismiss rate is structurally high (e.g. an exit-intent survey naturally has high dismiss); don't re-flag every run.\n\n#### Recurring theme in open-text responses\n\nThis is the highest-value pattern — and the one with the highest false-positive risk. For each survey with at least one open-text question, pull recent responses (the open-text pull SQL — key coalesce and submission dedupe included — is in [`references\u002Fresponse-querying.md`](references\u002Fresponse-querying.md)) and look for clustering.\n\nRead the responses. Look for:\n\n- **Convergence on a noun phrase or feature name** — five users mentioning \"checkout\", \"the new editor\", \"API key page\" within 14 days is a real theme.\n- **Sentiment polarity** — separate complaints from praise from feature requests. Don't combine them into a single \"users said things\" finding.\n- **Specificity** — \"it's slow\" is too generic; \"the dashboard list page is slow when I have > 10 dashboards\" is concrete. The latter is report-worthy.\n\nTheme is report-worthy when:\n\n- ≥ 5 distinct respondents converge on the same theme within 14 days, OR\n- ≥ 3 distinct respondents converge AND the theme matches a recent activity-log entry (deploy, flag flip, new feature) within the same window — strong qualitative confirmation of an impact.\n\nWhen you file a report, quote 2–3 representative responses verbatim in the evidence (no PII; truncate at sentence level if a response is long). Name the theme as a concrete claim (\"Users report the dashboard list is slow with > 10 dashboards\"), not a vague summary (\"Users have feedback about dashboards\").\n\nDon't file a report when:\n\n- Responses are mostly NPS rating-only with no text — there's no theme to find.\n- Themes are evenly split (some users complaining, others praising the same feature) — the signal cancels itself; memory entry instead.\n- A memory entry tagged `addressed` already covers the same theme.\n\n#### Targeting drift\n\n`survey shown` count diverging sharply from baseline (up 5x or down 5x) usually means an upstream targeting condition changed. Four sources to check via `survey-get`:\n\n- **`linked_flag_id`** — survey shows only when this flag evaluates true. A flag rollout change directly resizes the audience.\n- **`targeting_flag_id`** — user-configured cohort \u002F property targeting. Same effect; also subject to cohort recomputation lag.\n- **`linked_insight_id`** — survey gates on viewing a specific insight. If the insight is deleted or its query is broken, the survey goes dead. Cross-check with `insight-get` and `inbox-reports-list` for any insight-side issues.\n- **`conditions`** — URL pattern, event-trigger, or `repeatedActivation` — config changes here directly resize the trigger surface.\n\nIf the upstream changed near the inflection, flag it as targeting drift, not a survey regression. (Note: the auto-managed `internal_targeting_flag` is a separate construct that suppresses already-responded \u002F already-dismissed users — not a targeting source the team controls, and changes to it are usually expected.)\n\nMemory-worthy unless the survey is load-bearing (e.g. NPS the team reports on publicly) — then file a report so the team knows the sample frame changed.\n\n#### Stale or abandoned surveys\n\nA survey created > 90 days ago with steadily declining response volume and no `updated_at` activity is probably forgotten. P3 recommendation, not an anomaly: suggest the team retire it, refresh the question, or rotate the audience. Don't re-file if a memory entry already flagged it.\n\n#### Theme correlated with recent change\n\nWhen a theme emerges, cross-check `advanced-activity-logs-list` for the period around the inflection. If a deploy \u002F flag flip \u002F feature change in the same week matches the theme content, the finding lands much harder (\"4 users complained about checkout slowness on $date; deploy of `checkout-rewrite-v2` flag rolled to 100% on $date-1\"). Timing is hint, not proof — say \"matches\" rather than \"caused by\".\n\n#### Theme drift across survey iterations\n\nRecurring surveys (`schedule: recurring`, `iteration_count > 1`, `iteration_frequency_days > 0`) cycle iterations every N days, and each iteration's responses are tagged with `$survey_iteration`. Comparing themes across iterations on the same survey is itself a signal:\n\n- Theme volume rising in iteration N+1 vs N on the same survey = the issue is growing, not new.\n- New theme appearing in iteration N+1 that wasn't in earlier iterations = recent product change introduced something.\n- Score baseline shifting between iterations = sustainable change in user perception, more interesting than within-iteration noise.\n\nFilter open-text and rating queries by `$survey_iteration` to compare cleanly:\n\n```sql\nAND JSONExtractString(properties, '$survey_iteration') = '\u003Cn>'\n```\n\nWhen filing a report on a recurring survey, name the iteration explicitly in the evidence (\"iteration 3 of `nps-q1-2026`, last 14d\") so the team reads it against the right baseline.\n\n### Save memory as you go\n\nMemory is a continuous activity. Write a scratchpad entry whenever you observe something a future surveys run should know. Encode the \"category\" in the key prefix — `pattern:`, `noise:`, `addressed:`, `dedupe:`, `report:`, `reviewer:` — so future runs find it with a single `text=` search:\n\n- key `pattern:surveys:active-inventory` — _\"Active surveys: `nps-q1-2026` (id `abc`, NPS 0–10), `feedback-modal` (id `def`, open text), `csat-after-purchase` (id `ghi`, 1–5 rating).\"_\n- key `pattern:surveys:nps-q1-2026` — _\"Primary NPS survey is `nps-q1-2026`; healthy baseline 32 ± 5 over last 90 days, ~120 responses\u002Fweek. Score \u003C 25 or responses \u003C 60\u002Fweek is the alert bar.\"_\n- key `noise:surveys:feedback-modal` — _\"`feedback-modal` exit-intent survey naturally has 70% dismiss rate — that's expected behavior for this trigger, not a regression.\"_\n- key `addressed:surveys:theme-checkout-step-2-2026-05-04` — _\"Theme `checkout-step-2-confusion` raised in run on 2026-04-30; team acknowledged, fix shipped 2026-05-04. Don't re-file unless theme reappears post-2026-05-04.\"_\n- key `addressed:surveys:csat-old-stale` — _\"Survey `csat-old` last got responses 2026-02; appears abandoned but the team still has it active. P3 recommendation already filed; don't re-recommend.\"_\n- key `report:surveys:theme-checkout-step-2` — _\"Authored report `019f0a96-…` for the checkout-step-2 confusion theme on 2026-06-30. Edit it (append_note) if the theme grows or recurs rather than filing a new one.\"_\n- key `reviewer:surveys:nps-q1-2026` — _\"`nps-q1-2026` owned by `alice` (GitHub login) — route its reports there.\"_\n\nBy run #5 you'll know the team's active surveys, healthy response volumes, score baselines, which dismiss rates are structural, which themes have already been raised, which report covers a theme, and who owns it — so when a real theme or regression appears, the report lands with the right context already attached.\n\n### Decide\n\nSearch the inbox before you author — a report covering this theme \u002F survey \u002F regression may already exist (`inbox-reports-list` with `ordering=-updated_at`, then `inbox-reports-retrieve` the closest matches). Then, for each candidate finding:\n\n- **Edit** the existing report via `scout-edit-report` when the inbox already covers the theme or survey. A theme that's growing, a regression that's deepening, a later iteration's responses confirming an earlier read: `append_note` with the fresh response counts, score deltas, and time range (or rewrite the title\u002Fsummary on a report you authored). This is the default when a match exists; don't mint a near-duplicate.\n- **Author** a fresh report via `scout-emit-report` when nothing in the inbox covers it. The natural fits are a single validated theme (≥ 5 converging respondents, with 2–3 verbatim quotes — no PII) or one survey's score \u002F response-rate \u002F abandonment regression that clears the tiered bar, with concrete survey ids, question ids, response counts, and score deltas as evidence (the bar is confidence ≥ 0.85; sample-size matters more here than other domains — a report on 10 responses needs to be tighter than one on 200). A survey finding is an investigation, not a one-line code fix, so default to `requires_human_input`. **Always set `suggested_reviewers`** — resolve the owning person with `scout-members-list` (each member carries a resolved `github_login`; cache it under a `reviewer:surveys:\u003Csurvey>` 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:surveys:\u003Ctheme-or-survey>` scratchpad entry with the `report_id` so the next run edits it instead of duplicating. The harness prompt carries the full report-channel contract (field schema, safety × actionability status mapping, reviewer routing, the non-idempotency caveat, and the edit rules) — this section only adds the surveys-specific framing.\n- **Remember** via `scout-scratchpad-remember` if below the bar but worth carrying forward (a theme with only 3 respondents that might grow, a score wobble that didn't yet hold for two weeks), or to record what you ruled out and why.\n- **Skip** with a one-line note if a scratchpad entry with a `noise:` or `addressed:` key prefix, or an existing inbox report, already covers it.\n\nIf a prior run already covered the theme, default to edit-or-skip + scratchpad refresh rather than a fresh report. The same theme 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 surveys, what themes \u002F anomalies you found, what reports you authored or edited, what you remembered, what you ruled out. 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.\n\n## Disqualifiers (skip these)\n\n- **Survey at the end of its scheduled window** — natural tail-off in responses; not a regression. Check `schedule.end_date` before flagging.\n- **NPS \u002F CSAT drift on \u003C 30 responses in the recent window** — sample too small to trust; memory entry only.\n- **Themes evenly split between positive and negative** — they cancel each other; no single direction to surface.\n- **Theme matching an `addressed:` scratchpad entry** — the team already saw it and acted; re-filing wastes inbox space.\n- **One-off rant or off-topic response** — a single user typing \"AAAA\" or quoting song lyrics isn't signal. Themes need ≥ 3 distinct respondents.\n- **Internal test \u002F placeholder responses** — `TEST`, `TEST FEEDBACK DELETE!`, `qwe`, `asdf`, single-character submissions, repeated submissions from the survey author or the host org's own users. These are endemic on real projects and will skew theme counts if you don't strip them. A `WHERE length(response) > 5 AND lower(response) NOT IN ('test', 'qwe', 'asdf')` guard plus an `email NOT LIKE '%@\u003Chost_org_domain>%'` person-property filter catches most of it.\n- **Survey paused or in draft** — not user-facing right now; check `archived` \u002F status \u002F `start_date` before treating zero responses as a regression.\n- **PII or sensitive content in responses** — never put verbatim PII in a report. Quote the themed claim, not the raw text, if responses contain personal data.\n\nWhen in doubt, write a memory entry instead of filing a report.\n\n## MCP tools\n\nDirect calls (read-only):\n\n- `surveys-global-stats` — project-wide aggregate. **Start here** every cold start; cheap sanity check on overall survey health before any per-survey work.\n- `survey-stats` — per-survey response statistics: `shown` \u002F `dismissed` \u002F `sent` counts, unique respondents, conversion rates, timing. Date-filterable.\n- `survey-get` — full survey config for a candidate: questions (with ids and types), `type` (popover \u002F widget \u002F api — affects how `survey shown` semantics read), targeting (`linked_flag_id` \u002F `targeting_flag_id` \u002F `linked_insight_id` \u002F `conditions`), schedule (`start_date`, `end_date`), iteration config, `updated_at`. Read this before drawing conclusions about score changes — question wording changes invalidate trend comparisons.\n- `surveys-get-all` — last-resort discovery. Each survey object is 30–50 KB and busy projects have 100+ active surveys; calling this with `limit > 5` will blow your token budget. Prefer `surveys-global-stats` + an `execute-sql` ranking query (see \"Get oriented\" above) to find the candidate set, then `survey-get` per id. Use `surveys-get-all {\"search\": \"...\"}` if you need to resolve a name from a memory entry.\n- `execute-sql` against `events` — for raw response analysis (rating trends, theme aggregation). The property reference, the dual response-key coalesce, and the `$survey_submission_id` dedupe SQL are all in [`references\u002Fresponse-querying.md`](references\u002Fresponse-querying.md).\n- `read-data-schema event_property_values` — sample response values to confirm property keys exist and have the shape you expect before running heavy aggregations.\n- `query-trends` — confirm `survey shown` \u002F `survey sent` volume trends with weekly comparisons. Cheaper than a full SQL aggregation when you just need the shape.\n- `advanced-activity-logs-list` — correlate themes \u002F score drops with recent product changes.\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 survey's owner (null `github_login` → can't route, 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` \u002F `scout-scratchpad-search` \u002F `scout-runs-list` \u002F `scout-runs-retrieve` — orientation + dedupe.\n- `scout-emit-report` \u002F `scout-edit-report` \u002F `scout-scratchpad-remember` — author a report \u002F edit an existing one \u002F remember.\n\n### When you hit a gap\n\nTwo MCP gaps are known and may be worth flagging in a separate PR rather than working around in-skill:\n\n- **Project profile doesn't include surveys.** Cold-start orientation has to call `surveys-get-all` directly. Adding a `_surveys` builder to `products\u002Fsignals\u002Fbackend\u002Fscout_harness\u002Fprofile\u002Fbuilders.py` (a few rows: active count, top surveys by recent volume, primary NPS \u002F CSAT survey if any) would let every scout — not just this one — see surveys at orientation time. Worth a P3.\n- **Survey summarization isn't MCP-callable.** The product has a summarization pipeline at `products\u002Fsurveys\u002Fbackend\u002Fsummarization\u002F` but it's not exposed as an MCP tool. If it were, this scout could lean on cached summaries instead of re-aggregating themes from scratch each run. Worth a P2 for accuracy and cost.\n\nIf you notice a third gap during a run that would meaningfully unlock this scout, write a scratchpad entry with key `mcp-gap:surveys:\u003Cshort-name>` so the gap surfaces in the next review via `text=mcp-gap`.\n\n## When to stop\n\n- No active surveys + no recent survey events → close out empty (after writing the `not-in-use:` scratchpad entry).\n- Profile + scratchpad show a stable picture (known baselines, no recent inflection) → close out empty.\n- A candidate matches a scratchpad entry with `noise:` \u002F `addressed:` \u002F `dedupe:` key prefix → skip.\n- You've validated some hypotheses and filed (or edited) reports for what's solid → close out, even if there's more you could look at. Themes especially — fewer, sharper reports beat a long list of weak clusters.\n\n\"Looked but found nothing meaningful\" is a real outcome.\n",{"data":42,"body":50},{"name":4,"description":6,"compatibility":43,"allowed_tools":44,"metadata":47},"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). 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A theme or regression you've reported before is an ",{"type":54,"tag":76,"props":360,"children":361},{},[362],{"type":59,"value":140},{"type":59,"value":364},", not a fresh report; pull the closest matches with ",{"type":54,"tag":84,"props":366,"children":368},{"className":367},[],[369],{"type":59,"value":370},"inbox-reports-retrieve",{"type":59,"value":372}," before authoring.",{"type":54,"tag":72,"props":374,"children":375},{},[376,382,384,390,392,397,398,403,404,410],{"type":54,"tag":84,"props":377,"children":379},{"className":378},[],[380],{"type":59,"value":381},"scout-project-profile-get",{"type":59,"value":383}," — ",{"type":54,"tag":84,"props":385,"children":387},{"className":386},[],[388],{"type":59,"value":389},"top_events",{"type":59,"value":391}," for ",{"type":54,"tag":84,"props":393,"children":395},{"className":394},[],[396],{"type":59,"value":97},{"type":59,"value":127},{"type":54,"tag":84,"props":399,"children":401},{"className":400},[],[402],{"type":59,"value":89},{"type":59,"value":127},{"type":54,"tag":84,"props":405,"children":407},{"className":406},[],[408],{"type":59,"value":409},"survey sent",{"type":59,"value":411}," reach (the survey product isn't yet surfaced in the profile inventory; see \"When you hit a gap\" below).",{"type":54,"tag":62,"props":413,"children":414},{},[415,417,422,424,429,431,437],{"type":59,"value":416},"Then orient on surveys specifically. 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Listing the lot blows the token budget before you've made a single decision.",{"type":54,"tag":62,"props":440,"children":441},{},[442],{"type":59,"value":443},"Right order:",{"type":54,"tag":68,"props":445,"children":446},{},[447,464,592,610],{"type":54,"tag":72,"props":448,"children":449},{},[450,455,457,462],{"type":54,"tag":84,"props":451,"children":453},{"className":452},[],[454],{"type":59,"value":188},{"type":59,"value":456}," (last 30d) — cheap project-wide check: are surveys converting at all? 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For each rating-style active survey, pull the last 30 days of ",{"type":54,"tag":84,"props":844,"children":846},{"className":845},[],[847],{"type":59,"value":409},{"type":59,"value":849}," events and compute the score trend.",{"type":54,"tag":62,"props":851,"children":852},{},[853,870,872,878,880,886,887,893,895,900,902,908],{"type":54,"tag":76,"props":854,"children":855},{},[856,858,868],{"type":59,"value":857},"Two mechanical traps make response SQL non-obvious — read ",{"type":54,"tag":859,"props":860,"children":862},"a",{"href":861},"references\u002Fresponse-querying.md",[863],{"type":54,"tag":84,"props":864,"children":866},{"className":865},[],[867],{"type":59,"value":861},{"type":59,"value":869}," before writing any.",{"type":59,"value":871}," Answers land under two property key schemes (id-based ",{"type":54,"tag":84,"props":873,"children":875},{"className":874},[],[876],{"type":59,"value":877},"$survey_response_\u003Cquestion_id>",{"type":59,"value":879}," and legacy index-based ",{"type":54,"tag":84,"props":881,"children":883},{"className":882},[],[884],{"type":59,"value":885},"$survey_response",{"type":59,"value":127},{"type":54,"tag":84,"props":888,"children":890},{"className":889},[],[891],{"type":59,"value":892},"$survey_response_\u003Cn>",{"type":59,"value":894},") that must be coalesced — querying the id-based key alone reads as \"no responses\" on legacy surveys — and newer clients can emit multiple ",{"type":54,"tag":84,"props":896,"children":898},{"className":897},[],[899],{"type":59,"value":409},{"type":59,"value":901}," events per submission, so every count needs the ",{"type":54,"tag":84,"props":903,"children":905},{"className":904},[],[906],{"type":59,"value":907},"$survey_submission_id",{"type":59,"value":909}," dedupe. The reference has the copy-ready rating-trend SQL with both handled.",{"type":54,"tag":62,"props":911,"children":912},{},[913],{"type":59,"value":914},"What counts as \"enough responses\" depends on the survey's normal volume. Flagship NPS surveys can hit 100+\u002Fweek; a feature-specific widget survey running at 15–25 responses\u002Fmonth is also normal. Use a tiered bar:",{"type":54,"tag":192,"props":916,"children":917},{},[918,928,938],{"type":54,"tag":72,"props":919,"children":920},{},[921,926],{"type":54,"tag":76,"props":922,"children":923},{},[924],{"type":59,"value":925},"High-volume surveys",{"type":59,"value":927}," (baseline ≥ 30 responses\u002Fweek): require ≥ 30 in the recent week, score drop ≥ 10% of scale (1 point NPS, 0.5 CSAT), holds across the most recent 7 days vs the prior trailing 21 days.",{"type":54,"tag":72,"props":929,"children":930},{},[931,936],{"type":54,"tag":76,"props":932,"children":933},{},[934],{"type":59,"value":935},"Low-volume surveys",{"type":59,"value":937}," (baseline 5–30\u002Fweek): require ≥ 8 in the recent 14 days, score drop ≥ 15% of scale, comparing against the survey's own trailing 60-day baseline rather than week-over-week. Smaller samples need a larger effect to outrun noise.",{"type":54,"tag":72,"props":939,"children":940},{},[941,946],{"type":54,"tag":76,"props":942,"children":943},{},[944],{"type":59,"value":945},"Very low-volume surveys",{"type":59,"value":947}," (\u003C 5\u002Fweek): rating trends are too noisy to act on. Treat as theme-aggregation only; memory entry, not emit.",{"type":54,"tag":62,"props":949,"children":950},{},[951],{"type":59,"value":952},"In all tiers, anchor on the survey's own trailing baseline before any global rule of thumb. A widget survey with a 6.0 trailing average that drops to 5.2 on N=12 is more interesting than a popover at NPS 32 → 31 on N=400 — and the scout's job is to spot the meaningful one.",{"type":54,"tag":832,"props":954,"children":956},{"id":955},"response-rate-cratering",[957],{"type":59,"value":958},"Response-rate cratering",{"type":54,"tag":62,"props":960,"children":961},{},[962,967,969,974,976,981,983,989,991,997],{"type":54,"tag":84,"props":963,"children":965},{"className":964},[],[966],{"type":59,"value":709},{"type":59,"value":968}," returns ",{"type":54,"tag":84,"props":970,"children":972},{"className":971},[],[973],{"type":59,"value":626},{"type":59,"value":975}," and ",{"type":54,"tag":84,"props":977,"children":979},{"className":978},[],[980],{"type":59,"value":640},{"type":59,"value":982}," counts. A survey that converted at 8% last month and 0.5% this week is broken — usually because the question wording changed, the target audience changed, or the survey is being shown in a different context (a flag flipped, a page was redesigned). Pair the stats with ",{"type":54,"tag":84,"props":984,"children":986},{"className":985},[],[987],{"type":59,"value":988},"survey-get",{"type":59,"value":990}," to check the ",{"type":54,"tag":84,"props":992,"children":994},{"className":993},[],[995],{"type":59,"value":996},"updated_at",{"type":59,"value":998}," and questions; if the survey config was edited near the inflection, that's the cause. If not, suspect upstream.",{"type":54,"tag":62,"props":1000,"children":1001},{},[1002,1004,1010],{"type":59,"value":1003},"Disqualifier: a survey at the end of its scheduled window naturally tails off. Check ",{"type":54,"tag":84,"props":1005,"children":1007},{"className":1006},[],[1008],{"type":59,"value":1009},"schedule.end_date",{"type":59,"value":1011}," before treating low recent response rate as a regression.",{"type":54,"tag":832,"props":1013,"children":1015},{"id":1014},"abandonment-spike-dismissed-shown-ratio",[1016],{"type":59,"value":1017},"Abandonment spike (dismissed \u002F shown ratio)",{"type":54,"tag":62,"props":1019,"children":1020},{},[1021,1026,1028,1033,1035,1040,1042,1053,1055,1060,1062,1067],{"type":54,"tag":84,"props":1022,"children":1024},{"className":1023},[],[1025],{"type":59,"value":97},{"type":59,"value":1027}," events are impressions; ",{"type":54,"tag":84,"props":1029,"children":1031},{"className":1030},[],[1032],{"type":59,"value":89},{"type":59,"value":1034}," are explicit close-outs; ",{"type":54,"tag":84,"props":1036,"children":1038},{"className":1037},[],[1039],{"type":59,"value":409},{"type":59,"value":1041}," are completions. Their meaning ",{"type":54,"tag":76,"props":1043,"children":1044},{},[1045,1047],{"type":59,"value":1046},"depends on the survey's ",{"type":54,"tag":84,"props":1048,"children":1050},{"className":1049},[],[1051],{"type":59,"value":1052},"type",{"type":59,"value":1054},", and the scout has to read ",{"type":54,"tag":84,"props":1056,"children":1058},{"className":1057},[],[1059],{"type":59,"value":1052},{"type":59,"value":1061}," from ",{"type":54,"tag":84,"props":1063,"children":1065},{"className":1064},[],[1066],{"type":59,"value":988},{"type":59,"value":1068}," before interpreting any ratio:",{"type":54,"tag":192,"props":1070,"children":1071},{},[1072,1092,1112],{"type":54,"tag":72,"props":1073,"children":1074},{},[1075,1084,1085,1090],{"type":54,"tag":76,"props":1076,"children":1077},{},[1078],{"type":54,"tag":84,"props":1079,"children":1081},{"className":1080},[],[1082],{"type":59,"value":1083},"popover",{"type":59,"value":383},{"type":54,"tag":84,"props":1086,"children":1088},{"className":1087},[],[1089],{"type":59,"value":97},{"type":59,"value":1091}," fires when the popover auto-renders. A high dismiss rate is genuine signal: users are seeing it and immediately killing it.",{"type":54,"tag":72,"props":1093,"children":1094},{},[1095,1104,1105,1110],{"type":54,"tag":76,"props":1096,"children":1097},{},[1098],{"type":54,"tag":84,"props":1099,"children":1101},{"className":1100},[],[1102],{"type":59,"value":1103},"widget",{"type":59,"value":383},{"type":54,"tag":84,"props":1106,"children":1108},{"className":1107},[],[1109],{"type":59,"value":97},{"type":59,"value":1111}," only fires when the user clicks the widget trigger. A high dismiss rate means users opened the widget and changed their mind, not that the team is spamming them. Baseline dismiss rates are naturally higher (50–70% is common; the Logs Feedback widget on PostHog itself runs at 64% with healthy NPS) and shouldn't be flagged as fatigue.",{"type":54,"tag":72,"props":1113,"children":1114},{},[1115,1124,1125,1130,1132,1137],{"type":54,"tag":76,"props":1116,"children":1117},{},[1118],{"type":54,"tag":84,"props":1119,"children":1121},{"className":1120},[],[1122],{"type":59,"value":1123},"api",{"type":59,"value":383},{"type":54,"tag":84,"props":1126,"children":1128},{"className":1127},[],[1129],{"type":59,"value":97},{"type":59,"value":1131}," fires from SDK calls. Semantics depend on the integrating product; check ",{"type":54,"tag":84,"props":1133,"children":1135},{"className":1134},[],[1136],{"type":59,"value":988},{"type":59,"value":1138}," to see how it's wired before interpreting trends.",{"type":54,"tag":62,"props":1140,"children":1141},{},[1142,1144,1149],{"type":59,"value":1143},"If the dismiss rate jumps sharply on a ",{"type":54,"tag":84,"props":1145,"children":1147},{"className":1146},[],[1148],{"type":59,"value":1083},{"type":59,"value":1150}," survey (e.g. baseline 30%, recent 70%), users are seeing it and immediately killing it. Common causes: the survey now appears at a worse moment in the user journey, or fatigue from displaying too often.",{"type":54,"tag":62,"props":1152,"children":1153},{},[1154,1156,1161,1162,1167],{"type":59,"value":1155},"For ",{"type":54,"tag":84,"props":1157,"children":1159},{"className":1158},[],[1160],{"type":59,"value":1103},{"type":59,"value":975},{"type":54,"tag":84,"props":1163,"children":1165},{"className":1164},[],[1166],{"type":59,"value":1123},{"type":59,"value":1168}," surveys, treat dismiss-rate shifts as low signal unless they're paired with a response-volume drop — that's when something upstream of the click changed.",{"type":54,"tag":490,"props":1170,"children":1172},{"className":492,"code":1171,"language":494,"meta":495,"style":495},"SELECT\n toDate(timestamp) AS day,\n countIf(event = 'survey shown') AS shown,\n countIf(event = 'survey dismissed') AS dismissed,\n countIf(event = 'survey sent') AS sent,\n dismissed \u002F nullIf(shown, 0) AS dismiss_rate\nFROM events\nWHERE event IN ('survey shown', 'survey dismissed', 'survey sent')\n AND JSONExtractString(properties, '$survey_id') = '\u003Csurvey_id>'\n AND timestamp > now() - INTERVAL 30 DAY\nGROUP BY day\nORDER BY day\n",[1173],{"type":54,"tag":84,"props":1174,"children":1175},{"__ignoreMap":495},[1176,1183,1191,1199,1207,1215,1223,1230,1238,1246,1253,1261],{"type":54,"tag":501,"props":1177,"children":1178},{"class":503,"line":504},[1179],{"type":54,"tag":501,"props":1180,"children":1181},{},[1182],{"type":59,"value":510},{"type":54,"tag":501,"props":1184,"children":1185},{"class":503,"line":513},[1186],{"type":54,"tag":501,"props":1187,"children":1188},{},[1189],{"type":59,"value":1190}," toDate(timestamp) AS day,\n",{"type":54,"tag":501,"props":1192,"children":1193},{"class":503,"line":522},[1194],{"type":54,"tag":501,"props":1195,"children":1196},{},[1197],{"type":59,"value":1198}," countIf(event = 'survey shown') AS shown,\n",{"type":54,"tag":501,"props":1200,"children":1201},{"class":503,"line":531},[1202],{"type":54,"tag":501,"props":1203,"children":1204},{},[1205],{"type":59,"value":1206}," countIf(event = 'survey dismissed') AS dismissed,\n",{"type":54,"tag":501,"props":1208,"children":1209},{"class":503,"line":540},[1210],{"type":54,"tag":501,"props":1211,"children":1212},{},[1213],{"type":59,"value":1214}," countIf(event = 'survey sent') AS sent,\n",{"type":54,"tag":501,"props":1216,"children":1217},{"class":503,"line":549},[1218],{"type":54,"tag":501,"props":1219,"children":1220},{},[1221],{"type":59,"value":1222}," dismissed \u002F nullIf(shown, 0) AS dismiss_rate\n",{"type":54,"tag":501,"props":1224,"children":1225},{"class":503,"line":558},[1226],{"type":54,"tag":501,"props":1227,"children":1228},{},[1229],{"type":59,"value":546},{"type":54,"tag":501,"props":1231,"children":1232},{"class":503,"line":567},[1233],{"type":54,"tag":501,"props":1234,"children":1235},{},[1236],{"type":59,"value":1237},"WHERE event IN ('survey shown', 'survey dismissed', 'survey sent')\n",{"type":54,"tag":501,"props":1239,"children":1240},{"class":503,"line":576},[1241],{"type":54,"tag":501,"props":1242,"children":1243},{},[1244],{"type":59,"value":1245}," AND JSONExtractString(properties, '$survey_id') = '\u003Csurvey_id>'\n",{"type":54,"tag":501,"props":1247,"children":1248},{"class":503,"line":585},[1249],{"type":54,"tag":501,"props":1250,"children":1251},{},[1252],{"type":59,"value":564},{"type":54,"tag":501,"props":1254,"children":1255},{"class":503,"line":30},[1256],{"type":54,"tag":501,"props":1257,"children":1258},{},[1259],{"type":59,"value":1260},"GROUP BY day\n",{"type":54,"tag":501,"props":1262,"children":1264},{"class":503,"line":1263},12,[1265],{"type":54,"tag":501,"props":1266,"children":1267},{},[1268],{"type":59,"value":1269},"ORDER BY day\n",{"type":54,"tag":62,"props":1271,"children":1272},{},[1273],{"type":59,"value":1274},"Memory note when a dismiss rate is structurally high (e.g. an exit-intent survey naturally has high dismiss); don't re-flag every run.",{"type":54,"tag":832,"props":1276,"children":1278},{"id":1277},"recurring-theme-in-open-text-responses",[1279],{"type":59,"value":1280},"Recurring theme in open-text responses",{"type":54,"tag":62,"props":1282,"children":1283},{},[1284,1286,1294],{"type":59,"value":1285},"This is the highest-value pattern — and the one with the highest false-positive risk. For each survey with at least one open-text question, pull recent responses (the open-text pull SQL — key coalesce and submission dedupe included — is in ",{"type":54,"tag":859,"props":1287,"children":1288},{"href":861},[1289],{"type":54,"tag":84,"props":1290,"children":1292},{"className":1291},[],[1293],{"type":59,"value":861},{"type":59,"value":1295},") and look for clustering.",{"type":54,"tag":62,"props":1297,"children":1298},{},[1299],{"type":59,"value":1300},"Read the responses. Look for:",{"type":54,"tag":192,"props":1302,"children":1303},{},[1304,1314,1324],{"type":54,"tag":72,"props":1305,"children":1306},{},[1307,1312],{"type":54,"tag":76,"props":1308,"children":1309},{},[1310],{"type":59,"value":1311},"Convergence on a noun phrase or feature name",{"type":59,"value":1313}," — five users mentioning \"checkout\", \"the new editor\", \"API key page\" within 14 days is a real theme.",{"type":54,"tag":72,"props":1315,"children":1316},{},[1317,1322],{"type":54,"tag":76,"props":1318,"children":1319},{},[1320],{"type":59,"value":1321},"Sentiment polarity",{"type":59,"value":1323}," — separate complaints from praise from feature requests. Don't combine them into a single \"users said things\" finding.",{"type":54,"tag":72,"props":1325,"children":1326},{},[1327,1332],{"type":54,"tag":76,"props":1328,"children":1329},{},[1330],{"type":59,"value":1331},"Specificity",{"type":59,"value":1333}," — \"it's slow\" is too generic; \"the dashboard list page is slow when I have > 10 dashboards\" is concrete. The latter is report-worthy.",{"type":54,"tag":62,"props":1335,"children":1336},{},[1337],{"type":59,"value":1338},"Theme is report-worthy when:",{"type":54,"tag":192,"props":1340,"children":1341},{},[1342,1347],{"type":54,"tag":72,"props":1343,"children":1344},{},[1345],{"type":59,"value":1346},"≥ 5 distinct respondents converge on the same theme within 14 days, OR",{"type":54,"tag":72,"props":1348,"children":1349},{},[1350],{"type":59,"value":1351},"≥ 3 distinct respondents converge AND the theme matches a recent activity-log entry (deploy, flag flip, new feature) within the same window — strong qualitative confirmation of an impact.",{"type":54,"tag":62,"props":1353,"children":1354},{},[1355],{"type":59,"value":1356},"When you file a report, quote 2–3 representative responses verbatim in the evidence (no PII; truncate at sentence level if a response is long). Name the theme as a concrete claim (\"Users report the dashboard list is slow with > 10 dashboards\"), not a vague summary (\"Users have feedback about dashboards\").",{"type":54,"tag":62,"props":1358,"children":1359},{},[1360],{"type":59,"value":1361},"Don't file a report when:",{"type":54,"tag":192,"props":1363,"children":1364},{},[1365,1370,1375],{"type":54,"tag":72,"props":1366,"children":1367},{},[1368],{"type":59,"value":1369},"Responses are mostly NPS rating-only with no text — there's no theme to find.",{"type":54,"tag":72,"props":1371,"children":1372},{},[1373],{"type":59,"value":1374},"Themes are evenly split (some users complaining, others praising the same feature) — the signal cancels itself; memory entry instead.",{"type":54,"tag":72,"props":1376,"children":1377},{},[1378,1380,1386],{"type":59,"value":1379},"A memory entry tagged ",{"type":54,"tag":84,"props":1381,"children":1383},{"className":1382},[],[1384],{"type":59,"value":1385},"addressed",{"type":59,"value":1387}," already covers the same theme.",{"type":54,"tag":832,"props":1389,"children":1391},{"id":1390},"targeting-drift",[1392],{"type":59,"value":1393},"Targeting drift",{"type":54,"tag":62,"props":1395,"children":1396},{},[1397,1402,1404,1409],{"type":54,"tag":84,"props":1398,"children":1400},{"className":1399},[],[1401],{"type":59,"value":97},{"type":59,"value":1403}," count diverging sharply from baseline (up 5x or down 5x) usually means an upstream targeting condition changed. Four sources to check via ",{"type":54,"tag":84,"props":1405,"children":1407},{"className":1406},[],[1408],{"type":59,"value":988},{"type":59,"value":1410},":",{"type":54,"tag":192,"props":1412,"children":1413},{},[1414,1428,1442,1470],{"type":54,"tag":72,"props":1415,"children":1416},{},[1417,1426],{"type":54,"tag":76,"props":1418,"children":1419},{},[1420],{"type":54,"tag":84,"props":1421,"children":1423},{"className":1422},[],[1424],{"type":59,"value":1425},"linked_flag_id",{"type":59,"value":1427}," — survey shows only when this flag evaluates true. A flag rollout change directly resizes the audience.",{"type":54,"tag":72,"props":1429,"children":1430},{},[1431,1440],{"type":54,"tag":76,"props":1432,"children":1433},{},[1434],{"type":54,"tag":84,"props":1435,"children":1437},{"className":1436},[],[1438],{"type":59,"value":1439},"targeting_flag_id",{"type":59,"value":1441}," — user-configured cohort \u002F property targeting. Same effect; also subject to cohort recomputation lag.",{"type":54,"tag":72,"props":1443,"children":1444},{},[1445,1454,1456,1462,1463,1468],{"type":54,"tag":76,"props":1446,"children":1447},{},[1448],{"type":54,"tag":84,"props":1449,"children":1451},{"className":1450},[],[1452],{"type":59,"value":1453},"linked_insight_id",{"type":59,"value":1455}," — survey gates on viewing a specific insight. If the insight is deleted or its query is broken, the survey goes dead. Cross-check with ",{"type":54,"tag":84,"props":1457,"children":1459},{"className":1458},[],[1460],{"type":59,"value":1461},"insight-get",{"type":59,"value":975},{"type":54,"tag":84,"props":1464,"children":1466},{"className":1465},[],[1467],{"type":59,"value":333},{"type":59,"value":1469}," for any insight-side issues.",{"type":54,"tag":72,"props":1471,"children":1472},{},[1473,1482,1484,1490],{"type":54,"tag":76,"props":1474,"children":1475},{},[1476],{"type":54,"tag":84,"props":1477,"children":1479},{"className":1478},[],[1480],{"type":59,"value":1481},"conditions",{"type":59,"value":1483}," — URL pattern, event-trigger, or ",{"type":54,"tag":84,"props":1485,"children":1487},{"className":1486},[],[1488],{"type":59,"value":1489},"repeatedActivation",{"type":59,"value":1491}," — config changes here directly resize the trigger surface.",{"type":54,"tag":62,"props":1493,"children":1494},{},[1495,1497,1503],{"type":59,"value":1496},"If the upstream changed near the inflection, flag it as targeting drift, not a survey regression. (Note: the auto-managed ",{"type":54,"tag":84,"props":1498,"children":1500},{"className":1499},[],[1501],{"type":59,"value":1502},"internal_targeting_flag",{"type":59,"value":1504}," is a separate construct that suppresses already-responded \u002F already-dismissed users — not a targeting source the team controls, and changes to it are usually expected.)",{"type":54,"tag":62,"props":1506,"children":1507},{},[1508],{"type":59,"value":1509},"Memory-worthy unless the survey is load-bearing (e.g. NPS the team reports on publicly) — then file a report so the team knows the sample frame changed.",{"type":54,"tag":832,"props":1511,"children":1513},{"id":1512},"stale-or-abandoned-surveys",[1514],{"type":59,"value":1515},"Stale or abandoned surveys",{"type":54,"tag":62,"props":1517,"children":1518},{},[1519,1521,1526],{"type":59,"value":1520},"A survey created > 90 days ago with steadily declining response volume and no ",{"type":54,"tag":84,"props":1522,"children":1524},{"className":1523},[],[1525],{"type":59,"value":996},{"type":59,"value":1527}," activity is probably forgotten. P3 recommendation, not an anomaly: suggest the team retire it, refresh the question, or rotate the audience. Don't re-file if a memory entry already flagged it.",{"type":54,"tag":832,"props":1529,"children":1531},{"id":1530},"theme-correlated-with-recent-change",[1532],{"type":59,"value":1533},"Theme correlated with recent change",{"type":54,"tag":62,"props":1535,"children":1536},{},[1537,1539,1545,1547,1553],{"type":59,"value":1538},"When a theme emerges, cross-check ",{"type":54,"tag":84,"props":1540,"children":1542},{"className":1541},[],[1543],{"type":59,"value":1544},"advanced-activity-logs-list",{"type":59,"value":1546}," for the period around the inflection. If a deploy \u002F flag flip \u002F feature change in the same week matches the theme content, the finding lands much harder (\"4 users complained about checkout slowness on $date; deploy of ",{"type":54,"tag":84,"props":1548,"children":1550},{"className":1549},[],[1551],{"type":59,"value":1552},"checkout-rewrite-v2",{"type":59,"value":1554}," flag rolled to 100% on $date-1\"). 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These are endemic on real projects and will skew theme counts if you don't strip them. A ",{"type":54,"tag":84,"props":2254,"children":2256},{"className":2255},[],[2257],{"type":59,"value":2258},"WHERE length(response) > 5 AND lower(response) NOT IN ('test', 'qwe', 'asdf')",{"type":59,"value":2260}," guard plus an ",{"type":54,"tag":84,"props":2262,"children":2264},{"className":2263},[],[2265],{"type":59,"value":2266},"email NOT LIKE '%@\u003Chost_org_domain>%'",{"type":59,"value":2268}," person-property filter catches most of it.",{"type":54,"tag":72,"props":2270,"children":2271},{},[2272,2277,2279,2285,2287,2293],{"type":54,"tag":76,"props":2273,"children":2274},{},[2275],{"type":59,"value":2276},"Survey paused or in draft",{"type":59,"value":2278}," — not user-facing right now; check ",{"type":54,"tag":84,"props":2280,"children":2282},{"className":2281},[],[2283],{"type":59,"value":2284},"archived",{"type":59,"value":2286}," \u002F status \u002F ",{"type":54,"tag":84,"props":2288,"children":2290},{"className":2289},[],[2291],{"type":59,"value":2292},"start_date",{"type":59,"value":2294}," before treating zero responses as a regression.",{"type":54,"tag":72,"props":2296,"children":2297},{},[2298,2303],{"type":54,"tag":76,"props":2299,"children":2300},{},[2301],{"type":59,"value":2302},"PII or sensitive content in responses",{"type":59,"value":2304}," — never put verbatim PII in a report. Quote the themed claim, not the raw text, if responses contain personal data.",{"type":54,"tag":62,"props":2306,"children":2307},{},[2308],{"type":59,"value":2309},"When in doubt, write a memory entry instead of filing a report.",{"type":54,"tag":149,"props":2311,"children":2313},{"id":2312},"mcp-tools",[2314],{"type":59,"value":2315},"MCP tools",{"type":54,"tag":62,"props":2317,"children":2318},{},[2319],{"type":59,"value":2320},"Direct calls (read-only):",{"type":54,"tag":192,"props":2322,"children":2323},{},[2324,2341,2370,2440,2486,2521,2532,2556,2566,2589,2607],{"type":54,"tag":72,"props":2325,"children":2326},{},[2327,2332,2334,2339],{"type":54,"tag":84,"props":2328,"children":2330},{"className":2329},[],[2331],{"type":59,"value":188},{"type":59,"value":2333}," — project-wide aggregate. ",{"type":54,"tag":76,"props":2335,"children":2336},{},[2337],{"type":59,"value":2338},"Start here",{"type":59,"value":2340}," every cold start; cheap sanity check on overall survey health before any per-survey work.",{"type":54,"tag":72,"props":2342,"children":2343},{},[2344,2349,2351,2356,2357,2362,2363,2368],{"type":54,"tag":84,"props":2345,"children":2347},{"className":2346},[],[2348],{"type":59,"value":709},{"type":59,"value":2350}," — per-survey response statistics: ",{"type":54,"tag":84,"props":2352,"children":2354},{"className":2353},[],[2355],{"type":59,"value":626},{"type":59,"value":127},{"type":54,"tag":84,"props":2358,"children":2360},{"className":2359},[],[2361],{"type":59,"value":633},{"type":59,"value":127},{"type":54,"tag":84,"props":2364,"children":2366},{"className":2365},[],[2367],{"type":59,"value":640},{"type":59,"value":2369}," counts, unique respondents, conversion rates, timing. Date-filterable.",{"type":54,"tag":72,"props":2371,"children":2372},{},[2373,2378,2380,2385,2387,2392,2394,2399,2400,2405,2406,2411,2412,2417,2419,2424,2425,2431,2433,2438],{"type":54,"tag":84,"props":2374,"children":2376},{"className":2375},[],[2377],{"type":59,"value":988},{"type":59,"value":2379}," — full survey config for a candidate: questions (with ids and types), ",{"type":54,"tag":84,"props":2381,"children":2383},{"className":2382},[],[2384],{"type":59,"value":1052},{"type":59,"value":2386}," (popover \u002F widget \u002F api — affects how ",{"type":54,"tag":84,"props":2388,"children":2390},{"className":2389},[],[2391],{"type":59,"value":97},{"type":59,"value":2393}," semantics read), targeting (",{"type":54,"tag":84,"props":2395,"children":2397},{"className":2396},[],[2398],{"type":59,"value":1425},{"type":59,"value":127},{"type":54,"tag":84,"props":2401,"children":2403},{"className":2402},[],[2404],{"type":59,"value":1439},{"type":59,"value":127},{"type":54,"tag":84,"props":2407,"children":2409},{"className":2408},[],[2410],{"type":59,"value":1453},{"type":59,"value":127},{"type":54,"tag":84,"props":2413,"children":2415},{"className":2414},[],[2416],{"type":59,"value":1481},{"type":59,"value":2418},"), schedule (",{"type":54,"tag":84,"props":2420,"children":2422},{"className":2421},[],[2423],{"type":59,"value":2292},{"type":59,"value":276},{"type":54,"tag":84,"props":2426,"children":2428},{"className":2427},[],[2429],{"type":59,"value":2430},"end_date",{"type":59,"value":2432},"), iteration config, ",{"type":54,"tag":84,"props":2434,"children":2436},{"className":2435},[],[2437],{"type":59,"value":996},{"type":59,"value":2439},". Read this before drawing conclusions about score changes — question wording changes invalidate trend comparisons.",{"type":54,"tag":72,"props":2441,"children":2442},{},[2443,2448,2450,2456,2458,2463,2465,2470,2472,2477,2479,2484],{"type":54,"tag":84,"props":2444,"children":2446},{"className":2445},[],[2447],{"type":59,"value":165},{"type":59,"value":2449}," — last-resort discovery. Each survey object is 30–50 KB and busy projects have 100+ active surveys; calling this with ",{"type":54,"tag":84,"props":2451,"children":2453},{"className":2452},[],[2454],{"type":59,"value":2455},"limit > 5",{"type":59,"value":2457}," will blow your token budget. Prefer ",{"type":54,"tag":84,"props":2459,"children":2461},{"className":2460},[],[2462],{"type":59,"value":188},{"type":59,"value":2464}," + an ",{"type":54,"tag":84,"props":2466,"children":2468},{"className":2467},[],[2469],{"type":59,"value":479},{"type":59,"value":2471}," ranking query (see \"Get oriented\" above) to find the candidate set, then ",{"type":54,"tag":84,"props":2473,"children":2475},{"className":2474},[],[2476],{"type":59,"value":988},{"type":59,"value":2478}," per id. Use ",{"type":54,"tag":84,"props":2480,"children":2482},{"className":2481},[],[2483],{"type":59,"value":661},{"type":59,"value":2485}," if you need to resolve a name from a memory entry.",{"type":54,"tag":72,"props":2487,"children":2488},{},[2489,2494,2496,2502,2504,2509,2511,2519],{"type":54,"tag":84,"props":2490,"children":2492},{"className":2491},[],[2493],{"type":59,"value":479},{"type":59,"value":2495}," against ",{"type":54,"tag":84,"props":2497,"children":2499},{"className":2498},[],[2500],{"type":59,"value":2501},"events",{"type":59,"value":2503}," — for raw response analysis (rating trends, theme aggregation). The property reference, the dual response-key coalesce, and the ",{"type":54,"tag":84,"props":2505,"children":2507},{"className":2506},[],[2508],{"type":59,"value":907},{"type":59,"value":2510}," dedupe SQL are all in ",{"type":54,"tag":859,"props":2512,"children":2513},{"href":861},[2514],{"type":54,"tag":84,"props":2515,"children":2517},{"className":2516},[],[2518],{"type":59,"value":861},{"type":59,"value":2520},".",{"type":54,"tag":72,"props":2522,"children":2523},{},[2524,2530],{"type":54,"tag":84,"props":2525,"children":2527},{"className":2526},[],[2528],{"type":59,"value":2529},"read-data-schema event_property_values",{"type":59,"value":2531}," — sample response values to confirm property keys exist and have the shape you expect before running heavy aggregations.",{"type":54,"tag":72,"props":2533,"children":2534},{},[2535,2541,2543,2548,2549,2554],{"type":54,"tag":84,"props":2536,"children":2538},{"className":2537},[],[2539],{"type":59,"value":2540},"query-trends",{"type":59,"value":2542}," — confirm ",{"type":54,"tag":84,"props":2544,"children":2546},{"className":2545},[],[2547],{"type":59,"value":97},{"type":59,"value":127},{"type":54,"tag":84,"props":2550,"children":2552},{"className":2551},[],[2553],{"type":59,"value":409},{"type":59,"value":2555}," volume trends with weekly comparisons. Cheaper than a full SQL aggregation when you just need the shape.",{"type":54,"tag":72,"props":2557,"children":2558},{},[2559,2564],{"type":54,"tag":84,"props":2560,"children":2562},{"className":2561},[],[2563],{"type":59,"value":1544},{"type":59,"value":2565}," — correlate themes \u002F score drops with recent product changes.",{"type":54,"tag":72,"props":2567,"children":2568},{},[2569,2574,2575,2580,2582,2587],{"type":54,"tag":84,"props":2570,"children":2572},{"className":2571},[],[2573],{"type":59,"value":333},{"type":59,"value":127},{"type":54,"tag":84,"props":2576,"children":2578},{"className":2577},[],[2579],{"type":59,"value":370},{"type":59,"value":2581}," — the reports already in the inbox; check before authoring so you edit instead of duplicating (",{"type":54,"tag":84,"props":2583,"children":2585},{"className":2584},[],[2586],{"type":59,"value":356},{"type":59,"value":2588},").",{"type":54,"tag":72,"props":2590,"children":2591},{},[2592,2598,2600,2605],{"type":54,"tag":84,"props":2593,"children":2595},{"className":2594},[],[2596],{"type":59,"value":2597},"inbox-report-artefacts-list",{"type":59,"value":2599}," — a comparable report's artefact log, where the routed ",{"type":54,"tag":84,"props":2601,"children":2603},{"className":2602},[],[2604],{"type":59,"value":2023},{"type":59,"value":2606}," live (the report record doesn't expose them) — reviewer precedent.",{"type":54,"tag":72,"props":2608,"children":2609},{},[2610,2615,2617,2622,2624,2629,2631,2636],{"type":54,"tag":84,"props":2611,"children":2613},{"className":2612},[],[2614],{"type":59,"value":2031},{"type":59,"value":2616}," — this project's members with their resolved ",{"type":54,"tag":84,"props":2618,"children":2620},{"className":2619},[],[2621],{"type":59,"value":2039},{"type":59,"value":2623},", to route ",{"type":54,"tag":84,"props":2625,"children":2627},{"className":2626},[],[2628],{"type":59,"value":2023},{"type":59,"value":2630}," to a survey's owner (null ",{"type":54,"tag":84,"props":2632,"children":2634},{"className":2633},[],[2635],{"type":59,"value":2039},{"type":59,"value":2637}," → can't route, try the next owner). The in-run roster; the org-scoped resolver tools aren't available in a scout run.",{"type":54,"tag":62,"props":2639,"children":2640},{},[2641],{"type":59,"value":2642},"Harness-level:",{"type":54,"tag":192,"props":2644,"children":2645},{},[2646,2675],{"type":54,"tag":72,"props":2647,"children":2648},{},[2649,2654,2655,2660,2661,2666,2667,2673],{"type":54,"tag":84,"props":2650,"children":2652},{"className":2651},[],[2653],{"type":59,"value":381},{"type":59,"value":127},{"type":54,"tag":84,"props":2656,"children":2658},{"className":2657},[],[2659],{"type":59,"value":250},{"type":59,"value":127},{"type":54,"tag":84,"props":2662,"children":2664},{"className":2663},[],[2665],{"type":59,"value":322},{"type":59,"value":127},{"type":54,"tag":84,"props":2668,"children":2670},{"className":2669},[],[2671],{"type":59,"value":2672},"scout-runs-retrieve",{"type":59,"value":2674}," — orientation + dedupe.",{"type":54,"tag":72,"props":2676,"children":2677},{},[2678,2683,2684,2689,2690,2695],{"type":54,"tag":84,"props":2679,"children":2681},{"className":2680},[],[2682],{"type":59,"value":125},{"type":59,"value":127},{"type":54,"tag":84,"props":2685,"children":2687},{"className":2686},[],[2688],{"type":59,"value":133},{"type":59,"value":127},{"type":54,"tag":84,"props":2691,"children":2693},{"className":2692},[],[2694],{"type":59,"value":2081},{"type":59,"value":2696}," — author a report \u002F edit an existing one \u002F remember.",{"type":54,"tag":228,"props":2698,"children":2700},{"id":2699},"when-you-hit-a-gap",[2701],{"type":59,"value":2702},"When you hit a gap",{"type":54,"tag":62,"props":2704,"children":2705},{},[2706],{"type":59,"value":2707},"Two MCP gaps are known and may be worth flagging in a separate PR rather than working around in-skill:",{"type":54,"tag":192,"props":2709,"children":2710},{},[2711,2744],{"type":54,"tag":72,"props":2712,"children":2713},{},[2714,2719,2721,2726,2728,2734,2736,2742],{"type":54,"tag":76,"props":2715,"children":2716},{},[2717],{"type":59,"value":2718},"Project profile doesn't include surveys.",{"type":59,"value":2720}," Cold-start orientation has to call ",{"type":54,"tag":84,"props":2722,"children":2724},{"className":2723},[],[2725],{"type":59,"value":165},{"type":59,"value":2727}," directly. Adding a ",{"type":54,"tag":84,"props":2729,"children":2731},{"className":2730},[],[2732],{"type":59,"value":2733},"_surveys",{"type":59,"value":2735}," builder to ",{"type":54,"tag":84,"props":2737,"children":2739},{"className":2738},[],[2740],{"type":59,"value":2741},"products\u002Fsignals\u002Fbackend\u002Fscout_harness\u002Fprofile\u002Fbuilders.py",{"type":59,"value":2743}," (a few rows: active count, top surveys by recent volume, primary NPS \u002F CSAT survey if any) would let every scout — not just this one — see surveys at orientation time. Worth a P3.",{"type":54,"tag":72,"props":2745,"children":2746},{},[2747,2752,2754,2760],{"type":54,"tag":76,"props":2748,"children":2749},{},[2750],{"type":59,"value":2751},"Survey summarization isn't MCP-callable.",{"type":59,"value":2753}," The product has a summarization pipeline at ",{"type":54,"tag":84,"props":2755,"children":2757},{"className":2756},[],[2758],{"type":59,"value":2759},"products\u002Fsurveys\u002Fbackend\u002Fsummarization\u002F",{"type":59,"value":2761}," but it's not exposed as an MCP tool. If it were, this scout could lean on cached summaries instead of re-aggregating themes from scratch each run. Worth a P2 for accuracy and cost.",{"type":54,"tag":62,"props":2763,"children":2764},{},[2765,2767,2773,2775,2781],{"type":59,"value":2766},"If you notice a third gap during a run that would meaningfully unlock this scout, write a scratchpad entry with key ",{"type":54,"tag":84,"props":2768,"children":2770},{"className":2769},[],[2771],{"type":59,"value":2772},"mcp-gap:surveys:\u003Cshort-name>",{"type":59,"value":2774}," so the gap surfaces in the next review via ",{"type":54,"tag":84,"props":2776,"children":2778},{"className":2777},[],[2779],{"type":59,"value":2780},"text=mcp-gap",{"type":59,"value":2520},{"type":54,"tag":149,"props":2783,"children":2785},{"id":2784},"when-to-stop",[2786],{"type":59,"value":2787},"When to stop",{"type":54,"tag":192,"props":2789,"children":2790},{},[2791,2804,2809,2833],{"type":54,"tag":72,"props":2792,"children":2793},{},[2794,2796,2802],{"type":59,"value":2795},"No active surveys + no recent survey events → close out empty (after writing the ",{"type":54,"tag":84,"props":2797,"children":2799},{"className":2798},[],[2800],{"type":59,"value":2801},"not-in-use:",{"type":59,"value":2803}," scratchpad entry).",{"type":54,"tag":72,"props":2805,"children":2806},{},[2807],{"type":59,"value":2808},"Profile + scratchpad show a stable picture (known baselines, no recent inflection) → close out empty.",{"type":54,"tag":72,"props":2810,"children":2811},{},[2812,2814,2819,2820,2825,2826,2831],{"type":59,"value":2813},"A candidate matches a scratchpad entry with ",{"type":54,"tag":84,"props":2815,"children":2817},{"className":2816},[],[2818],{"type":59,"value":282},{"type":59,"value":127},{"type":54,"tag":84,"props":2821,"children":2823},{"className":2822},[],[2824],{"type":59,"value":289},{"type":59,"value":127},{"type":54,"tag":84,"props":2827,"children":2829},{"className":2828},[],[2830],{"type":59,"value":296},{"type":59,"value":2832}," key prefix → skip.",{"type":54,"tag":72,"props":2834,"children":2835},{},[2836],{"type":59,"value":2837},"You've validated some hypotheses and filed (or edited) reports for what's solid → close out, even if there's more you could look at. Themes especially — fewer, sharper reports beat a long list of weak clusters.",{"type":54,"tag":62,"props":2839,"children":2840},{},[2841],{"type":59,"value":2842},"\"Looked but found nothing meaningful\" is a real outcome.",{"type":54,"tag":2844,"props":2845,"children":2846},"style",{},[2847],{"type":59,"value":2848},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":2850,"total":3019},[2851,2866,2878,2891,2904,2919,2935,2952,2966,2981,2991,3009],{"slug":2852,"name":2852,"fn":2853,"description":2854,"org":2855,"tags":2856,"stars":2863,"repoUrl":2864,"updatedAt":2865},"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},[2857,2858,2861,2862],{"name":24,"slug":25,"type":15},{"name":2859,"slug":2860,"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":2867,"name":2867,"fn":2868,"description":2869,"org":2870,"tags":2871,"stars":2863,"repoUrl":2864,"updatedAt":2877},"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},[2872,2873,2876],{"name":24,"slug":25,"type":15},{"name":2874,"slug":2875,"type":15},"Audit","audit",{"name":9,"slug":8,"type":15},"2026-06-08T08:08:33.693989",{"slug":2879,"name":2879,"fn":2880,"description":2881,"org":2882,"tags":2883,"stars":2863,"repoUrl":2864,"updatedAt":2890},"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},[2884,2885,2888,2889],{"name":2874,"slug":2875,"type":15},{"name":2886,"slug":2887,"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":2892,"name":2892,"fn":2893,"description":2894,"org":2895,"tags":2896,"stars":2863,"repoUrl":2864,"updatedAt":2903},"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},[2897,2898,2899,2902],{"name":2874,"slug":2875,"type":15},{"name":2886,"slug":2887,"type":15},{"name":2900,"slug":2901,"type":15},"Performance","performance",{"name":9,"slug":8,"type":15},"2026-06-18T08:25:10.936787",{"slug":2905,"name":2905,"fn":2906,"description":2907,"org":2908,"tags":2909,"stars":2863,"repoUrl":2864,"updatedAt":2918},"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},[2910,2913,2916,2917],{"name":2911,"slug":2912,"type":15},"Alerting","alerting",{"name":2914,"slug":2915,"type":15},"Debugging","debugging",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-18T08:24:40.318583",{"slug":2920,"name":2920,"fn":2921,"description":2922,"org":2923,"tags":2924,"stars":2863,"repoUrl":2864,"updatedAt":2934},"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},[2925,2926,2929,2930,2933],{"name":24,"slug":25,"type":15},{"name":2927,"slug":2928,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},{"name":2931,"slug":2932,"type":15},"Operations","operations",{"name":9,"slug":8,"type":15},"2026-07-18T05:10:54.430898",{"slug":2936,"name":2936,"fn":2937,"description":2938,"org":2939,"tags":2940,"stars":2863,"repoUrl":2864,"updatedAt":2951},"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},[2941,2944,2947,2948],{"name":2942,"slug":2943,"type":15},"Automation","automation",{"name":2945,"slug":2946,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},{"name":2949,"slug":2950,"type":15},"Workflow Automation","workflow-automation","2026-07-28T05:34:12.167015",{"slug":2953,"name":2953,"fn":2954,"description":2955,"org":2956,"tags":2957,"stars":2863,"repoUrl":2864,"updatedAt":2965},"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},[2958,2959,2960,2963,2964],{"name":24,"slug":25,"type":15},{"name":2914,"slug":2915,"type":15},{"name":2961,"slug":2962,"type":15},"Frontend","frontend",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-07T05:56:19.828048",{"slug":2967,"name":2967,"fn":2968,"description":2969,"org":2970,"tags":2971,"stars":2863,"repoUrl":2864,"updatedAt":2980},"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},[2972,2975,2976,2977],{"name":2973,"slug":2974,"type":15},"API Development","api-development",{"name":2961,"slug":2962,"type":15},{"name":9,"slug":8,"type":15},{"name":2978,"slug":2979,"type":15},"SDK","sdk","2026-06-08T08:08:34.929454",{"slug":2982,"name":2982,"fn":2983,"description":2984,"org":2985,"tags":2986,"stars":2863,"repoUrl":2864,"updatedAt":2990},"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},[2987,2988,2989],{"name":2973,"slug":2974,"type":15},{"name":2931,"slug":2932,"type":15},{"name":9,"slug":8,"type":15},"2026-07-15T05:29:58.442727",{"slug":2992,"name":2992,"fn":2993,"description":2994,"org":2995,"tags":2996,"stars":2863,"repoUrl":2864,"updatedAt":3008},"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},[2997,2998,3001,3002,3005],{"name":2942,"slug":2943,"type":15},{"name":2999,"slug":3000,"type":15},"Email","email",{"name":9,"slug":8,"type":15},{"name":3003,"slug":3004,"type":15},"Reporting","reporting",{"name":3006,"slug":3007,"type":15},"Slack","slack","2026-06-09T07:32:27.935712",{"slug":3010,"name":3010,"fn":3011,"description":3012,"org":3013,"tags":3014,"stars":2863,"repoUrl":2864,"updatedAt":3018},"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},[3015,3016,3017],{"name":24,"slug":25,"type":15},{"name":2973,"slug":2974,"type":15},{"name":9,"slug":8,"type":15},"2026-06-08T08:08:29.624498",231,{"items":3021,"total":3123},[3022,3035,3049,3064,3077,3089,3107],{"slug":3023,"name":3023,"fn":3024,"description":3025,"org":3026,"tags":3027,"stars":26,"repoUrl":27,"updatedAt":3034},"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},[3028,3029,3032,3033],{"name":24,"slug":25,"type":15},{"name":3030,"slug":3031,"type":15},"Design","design",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"2026-04-06T18:44:38.291781",{"slug":3036,"name":3036,"fn":3037,"description":3038,"org":3039,"tags":3040,"stars":26,"repoUrl":27,"updatedAt":3048},"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},[3041,3042,3043,3044,3045],{"name":24,"slug":25,"type":15},{"name":2961,"slug":2962,"type":15},{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},{"name":3046,"slug":3047,"type":15},"UX Design","ux-design","2026-06-05T07:40:43.37798",{"slug":3050,"name":3050,"fn":3051,"description":3052,"org":3053,"tags":3054,"stars":26,"repoUrl":27,"updatedAt":3063},"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},[3055,3056,3059,3060],{"name":2874,"slug":2875,"type":15},{"name":3057,"slug":3058,"type":15},"Feature Flags","feature-flags",{"name":9,"slug":8,"type":15},{"name":3061,"slug":3062,"type":15},"QA","qa","2026-04-06T18:44:30.657553",{"slug":3065,"name":3065,"fn":3066,"description":3067,"org":3068,"tags":3069,"stars":26,"repoUrl":27,"updatedAt":3076},"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},[3070,3073,3074,3075],{"name":3071,"slug":3072,"type":15},"Agents","agents",{"name":2942,"slug":2943,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-28T05:33:45.509154",{"slug":3078,"name":3078,"fn":3079,"description":3080,"org":3081,"tags":3082,"stars":26,"repoUrl":27,"updatedAt":3088},"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},[3083,3084,3087],{"name":24,"slug":25,"type":15},{"name":3085,"slug":3086,"type":15},"Dashboards","dashboards",{"name":2945,"slug":2946,"type":15},"2026-07-21T06:07:38.060598",{"slug":3090,"name":3090,"fn":3091,"description":3092,"org":3093,"tags":3094,"stars":26,"repoUrl":27,"updatedAt":3106},"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},[3095,3098,3101,3104,3105],{"name":3096,"slug":3097,"type":15},"Deployment","deployment",{"name":3099,"slug":3100,"type":15},"Git","git",{"name":3102,"slug":3103,"type":15},"GitHub","github",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-06-28T07:46:59.53536",{"slug":3108,"name":3108,"fn":3109,"description":3110,"org":3111,"tags":3112,"stars":26,"repoUrl":27,"updatedAt":3122},"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},[3113,3114,3117,3120,3121],{"name":24,"slug":25,"type":15},{"name":3115,"slug":3116,"type":15},"Charts","charts",{"name":3118,"slug":3119,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":3003,"slug":3004,"type":15},"2026-06-18T08:18:57.960157",56]