triaging-web-analytics-support
Traiter les tickets de support d'analyse web de bout en bout : énumérer les tickets ouverts depuis le produit de conversations in-app et leurs répliques Zendesk, classer chacun dans une…
npx skills add https://github.com/posthog/posthog --skill triaging-web-analytics-supportTriaging web analytics support tickets
The job: turn a pile of open support tickets into (a) reply drafts grounded in code or data, and (b) draft PRs for real bugs. Most reported "bugs" are explainable semantics; most real bugs show up in error tracking or raw data before they show up in the code. Diagnose before writing code, and always determine which layer a symptom lives in before proposing a fix.
1. Enumerate the queue
Tickets live in the conversations product and are queryable via the PostHog MCP execute-sql tool against system.support_tickets (project 2, US).
Zendesk mirrors carry full comment history in the data warehouse.
See references/ticket-queries.md for ready-to-run SQL: open-ticket scans, keyword filters, full Zendesk comment extraction (the child_events JSON pattern), and resolving a requester email to an org/team across US and EU regions.
Slack channel #support-web-analytics mirrors new Zendesk tickets; the in-app ticket link in each message carries the conversations UUID.
2. Classify the shape, then run its playbook
Detailed walk-throughs with worked examples are in references/diagnostic-playbooks.md. The shapes:
| Shape | Trigger phrases | First move |
|---|---|---|
| Frontend crash | "everything crashes", exception ID, stack trace | Error tracking lookup; sourcemapped frames name the file. Check both US and EU projects |
| Two numbers don't match | "two different bounce rates", "insight X disagrees with tile Y" | Semantics first, not code: event-level vs session-entry scoping, "landing vs containing", any-event vs entry-event filters explain most of these |
| Count drop over time | "pageviews declined", "tracking loss" | Layer split: raw stored counts vs query-side exclusion. $pageview vs $pageleave ratio, UA segmentation, SDK version pin. Bot-shaped traffic disappearing is common and is not a PostHog bug |
| Tracker not loading / undercounts competitor | "numbers lower than ", GTM, consent, ad blockers | Runtime loading audit with Playwright against their live site: load method, first-request timing, blocklist simulation. See references/loading-audit.md |
| Ad-platform integration error | "can't re-add source", OAuth errors, "no conversions" | Source re-creation paths, OAuth failure modes (for example Microsoft AADSTS650052), attribution join keys (exact campaign name + normalized source, both UTMs required for the fallback) |
| Channel type misclassification | "shows as Direct", "wrong channel" | posthog/models/channel_type/channel_definitions.json + the decision tree in posthog/hogql/database/schema/channel_type.py; unknown source + stripped referrer falls through to Direct |
Two cross-cutting rules:
- Determine the layer before the fix. Capture → ingestion → stored events → query-time classification → UI. A drop in raw
count()can't be caused by query-time bot exclusion; a classification change can't alter stored counts. State which layer the evidence points at. - Check for prior art before building. Search open issues/PRs and the channel history; several recurring asks (self-referral exclusion, AI channel type, OAuth error surfacing) have open issues with context that changes the right response.
3. Produce artifacts
- Reply drafts: ground every claim in a file:line, a query result, or a doc link. Offer the customer the aligned filter/property instead of only explaining why they're "wrong" (for example: session
$entry_utm_campaigninstead of eventutm_campaign). - Fix PRs: one worktree + branch per fix, conventional commit, draft PR using the repo template. Public-repo safety: describe bugs generically; never include customer names, Zendesk numbers, or customer traffic volumes. Slack/ticket links behind auth are acceptable as origin context.
- Session note: keep a running triage note (
.notes/) with one section per ticket and an explicit "action left" marker per ticket, so a human can pick up the queue.
4. Verification tools
- Runtime loading audits and traffic simulation: references/loading-audit.md.
- Production query-side checks (per-team event series, UA splits, ingestion warnings): the
querying-production-databases-via-metabaseskill covers prod-us and prod-eu access. - Error tracking: MCP
query-error-tracking-issues-list/query-error-tracking-issue-eventswithverbosity: stackgives sourcemapped frames.