skills/triaging-error-issues/SKILL.md
Triage PostHog error tracking issues during a daily or on-call review. Use when the user asks "what's broken?", "what new errors do we have?", "show me top errors today", "what should I look at this morning", or wants a prioritized list of active issues to work on. Surfaces new and high-impact issues, ranks by users affected and recency, points at linked replays, and proposes next actions (investigate, assign, suppress, merge).
npx skillsauth add posthog/ai-plugin triaging-error-issuesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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When a user asks "what's broken?" or wants a daily error review, the goal is a short prioritized list of issues worth a human's attention — not a dump of every active issue. Most projects have hundreds of active issues; the few that matter are usually new (first seen in the last 24-48h), spiking, or affecting many distinct users.
| Tool | Purpose |
| ------------------------------------------- | ------------------------------------------------------------------------- |
| posthog:query-error-tracking-issues-list | List + rank issues with aggregate metrics (occurrences, users, sessions) |
| posthog:query-error-tracking-issue | Compact details for a single issue (status, assignee, top frame, release) |
| posthog:query-error-tracking-issue-events | Sampled $exception events with stack, URL, browser, and $session_id |
| posthog:query-session-recordings-list | Find replays of users hitting an issue |
| posthog:inbox-reports-list | Pre-curated actionable signals if the project uses Inbox |
Read the time window from the user's wording. Defaults if unspecified:
dateRange: { date_from: "-24h" }-7d-24hPick what "matters" means:
orderBy: "first_seen", orderDirection: "DESC", tight window.
Catches regressions introduced by recent deploys.orderBy: "users" ranks by distinct users affected. Better than
raw occurrences for severity (one bot loop produces many occurrences but one user).orderBy: "occurrences" over a short window vs a longer baseline
to spot spikes.Start narrow and widen if too few issues come back:
posthog:query-error-tracking-issues-list
{
"status": "active",
"orderBy": "users",
"orderDirection": "DESC",
"dateRange": { "date_from": "-24h" },
"limit": 20,
"volumeResolution": 24
}
Match volumeResolution to the window (24 buckets for -24h, 14 for -14d, etc.)
so each row's sparkline has enough resolution to show a spike vs flat steady state.
A single bucket only gives a total, not a shape.
For new-issues-only, run a parallel query with orderBy: "first_seen":
{
"status": "active",
"orderBy": "first_seen",
"orderDirection": "DESC",
"dateRange": { "date_from": "-24h" },
"limit": 10
}
If a project mixes browser and server SDKs, the top-by-users list is usually drowned
by server-side errors (each invocation often gets a fresh distinct_id). Narrow with
the library filter — values match the SDK's $lib, not the npm package name, examples:
web — posthog-js (browser)posthog-node, posthog-python, posthog-ruby, posthog-go, posthog-php, posthog-java, posthog-elixir — server SDKsposthog-edge — Cloudflare Workers / edge runtimeposthog-ios, posthog-android, posthog-react-native, posthog-flutter — mobileThe list will include known noise. Before presenting, drop or call out:
suppressing-noisy-errors) instead of triage.If unsure whether an issue is new vs. recurring, compare first_seen to the start
of the window:
first_seen inside the window → new, worth attentionfirst_seen weeks ago but spiking now → regression worth attentionfirst_seen weeks ago, flat volume → background noiseFor the top 3-5 candidates, pull a sample exception so the summary includes a stack
frame and URL, not just a title. Use posthog:query-error-tracking-issue-events rather than
raw SQL — it returns normalized fields ($exception_types, $exception_values,
$current_url, browser/OS, $session_id) and defaults to onlyAppFrames: true to
strip vendor noise from the stack:
posthog:query-error-tracking-issue-events
{
"issueId": "<issue_id>",
"limit": 1,
"include": ["exception", "stacktrace", "environment", "navigation", "correlation"]
}
If the user wants to see what users were doing, hand off to finding-replay-for-issue
to pick the best linked recording. Don't fetch replays for every triaged issue — only
the ones the user asks to dig into.
Lead with a one-line headline ("3 new issues in last 24h, 1 spike, 5 active high-impact"). Then a short table sorted by your chosen signal:
| Issue | First seen | Users | Sessions | Sample message | Suggested action |
| ----- | ---------- | ----- | -------- | --------------------------------- | -------------------------- |
| ... | 2h ago | 142 | 198 | TypeError ... at checkout.js:42 | Investigate |
| ... | spike | 67 | 89 | Network request failed | Watch — likely transient |
| ... | 3d ago | 12 | 12 | chrome-extension:// timeout | Suppress (extension noise) |
For each, suggest one of: investigate (investigating-error-issue), assign
(error-tracking-issues-partial-update), suppress (suppressing-noisy-errors),
merge (grouping-noisy-errors), or resolve if it's already known fixed.
properties.$lib_version (or properties.$exception_releases when the
SDK is configured to populate it), present them grouped — a rollback decision
rests on the cluster, not any one issue.sessions or occurrences.posthog:inbox-reports-list), check it first — PostHog
may have already curated the most actionable issues so you avoid re-deriving them._posthogUrl (returned on every issue row) so the user can jump
straight to the issue page if they want to drill down themselves. If you build the
link yourself, use the full /project/<project_id>/error_tracking/<id> path, never
a bare /error_tracking/<id>.data-ai
Signals scout for PostHog Tasks, the agent work items a project runs. Two lenses: delivery health (runs failing, clustered by repository and error class, and retry storms) every run, and on a slower rotation demand (recurring asks across human-authored tasks that point at a product gap). Skips the scout fleet's own run rows.
devops
Signals scout for the PostHog Conversations (support inbox) product. Watches the `$conversation_*` ticket-lifecycle events for support-delivery regressions — SLA breach-rate steps, first-response latency blowouts, backlog inflow-vs-resolution imbalance, and channel / assignment concentration — and files each dated regression as a report. Complements the per-ticket product-feedback signals the emission pipeline already fires; does not re-surface individual ticket content.
development
Populates and maintains a project's data catalog (semantic layer): canonical metrics, trust marks (certifications) on warehouse tables/views, and reviewed table relationships. Use when asked to set up / seed / bootstrap the data catalog or semantic layer, to catalog a project's metrics, to certify or deprecate data sources, to propose or review table joins, or to work through the proposal review queue. To *use* an existing catalog to answer a business-number question, see querying-posthog-data instead. Trigger terms: data catalog, semantic layer, canonical metric, certify table, deprecate source, relationship proposal, metric drift, review queue.
tools
Investigate logs in a PostHog project: verify a service or deployment is healthy, explain an error spike, triage an incident, or understand what a log stream is saying. Use when the user asks to "check the logs", asks whether a service, deploy, release, or change is working or broke anything, asks why errors are up or what changed, or wants the root cause of failures visible in logs. Routes the logs MCP tools (services overview, pattern mining, before/after pattern diffing, bucketed counts, facets, raw rows) so investigations start from summaries instead of raw rows or hand-written SQL over the logs table.