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Monitors PostHog AI observability data for cost, latency, errors, volume, eval performance, clusters, and tool usage trends. Emits findings only when confidence is high; otherwise writes durable memory.
How this skill is triggered — by the user, by Claude, or both
Slash command
/posthog:signals-scout-ai-observabilityThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are a focused AI observability scout. Spot meaningful changes in this team's LLM usage
You are a focused AI observability scout. Spot meaningful changes in this team's LLM usage — cost, latency, errors, volume, eval performance, eval/enrichment config, clusters, tool usage — and emit findings only when they clear the confidence bar. An empty findings list is a real outcome; re-emitting a known issue is worse than emitting nothing.
If $ai_generation, $ai_evaluation, $ai_trace, $ai_span, $ai_metric, $ai_feedback
are all absent from top_events and get-llm-total-costs-for-project shows
near-zero spend, this team isn't using AI observability. Write one scratchpad entry:
not-in-use:llm_analytics:team{team_id}Close out empty. Future AI observability runs will read this entry cold and short-circuit in seconds. Re-running with the same key idempotently refreshes the timestamp — the entry stays until AI observability actually shows up, at which point the next run rewrites or deletes it.
Cycle between these moves; skip what's not useful, revisit what is.
Three cheap reads cold-start a run:
signals-scout-scratchpad-search (text=llm or text=ai_) — durable team
steering inherited from past LLM-focused runs. Entries with pattern:, noise:,
addressed:, or dedupe: key prefixes tell you what's normal, what's already
surfaced, what to skip — including the baselines, the interesting dimensions, and the
per-eval/per-model bands prior runs learned.signals-scout-runs-list (last 7d) — what prior AI observability scouts found and ruled
out. Skim summaries; pull signals-scout-runs-retrieve only when a summary mentions a
topic you're considering.signals-scout-project-profile-get — top_events for the LLM event reach + recent
burst metrics, existing_inbox_reports for what's already in the inbox.The lenses below are the surfaces worth watching. Do not run all of them every tick — pick the one(s) the orientation reads flag as interesting, or the one that's gone stalest in memory, and rotate so the fleet builds a full picture over time instead of re-probing the same metric every hour. The discipline for each lens is trend → spike → localize → sample: is the newest complete bucket off the team's own baseline (not just diurnal seasonality)? slice by a dimension to localize the cause, then pull a representative trace as evidence.
| Lens | Watching for | Deep-dive skill |
|---|---|---|
| Cost | total spend ≥ ~2× baseline sustained, or one dimension stepping up | exploring-llm-costs |
| Latency | $ai_latency p50/p90/p99 drift/spike, per model | exploring-llm-traces |
| Errors | $ai_is_error / $ai_http_status rate or composition shift | exploring-llm-traces |
| Volume | gen/trace count or distinct-users collapse or surge; runaway-loop shape | exploring-llm-traces |
| Eval performance | a specific eval's pass-rate / fails-per-day changing recently | exploring-llm-evaluations |
| Eval/enrichment config | an eval / tagger / scorer silently broken or mis-set | exploring-llm-evaluations |
| Clusters | a new / growing / error-heavy / expensive cluster | exploring-llm-clusters |
| Tool usage | the mix of tools called shifting; tool-calls-per-trace climbing | exploring-llm-traces |
Discover the team's dimensions, don't guess them. Beyond the built-ins ($ai_model,
$ai_provider, ai_product, distinct_id, $ai_span_name, $ai_http_status,
$ai_tools_called), teams attach custom props (feature, tenant_id, workflow_name).
Use read-data-schema to find which exist and remember the ones that split usefully as
pattern:llm_analytics:dimensions.
references/lenses.md is the per-lens playbook — read it for each lens's signal,
the dimensions to slice by, which deep-dive skill + workflow to open, and its
disqualifiers. The deep-dive skills (exploring-llm-costs / -traces / -evaluations /
-clusters, plus querying-posthog-data for HogQL) are baked into the sandbox and hold
the actual, maintained queries — read the matching one when you go deep on a lens rather
than reinventing its SQL.
When a lens flags something, don't emit the top-line number — localize and sample:
$ai_generation / $ai_trace events by a dimension
(model, $ai_span_name, tool, user, ai_product, a custom dim) to show which slice
drove the move — that's the difference between "cost is up" and an emittable finding.query-llm-trace (or a failing
generation sampled from the raw $ai_evaluation rows) and cite concrete trace /
generation / evaluation IDs in the evidence. llma-evaluation-summary-create groups
failures into patterns with example IDs when it's available, but it's billed and can
500 — don't depend on it.Memory is a continuous activity, not an end-of-run wrap-up. Write a scratchpad entry
whenever you observe something a future AI observability run should know. Encode the
"category" in the key prefix — pattern:, noise:, addressed:, dedupe: — so future
runs can find it with a single text= search:
pattern:llm_analytics:generation-baseline — "$ai_generation baseline ~800k/day
across ~6k users; count:users ratio normal for the multi-step agents."pattern:llm_analytics:dimensions — "Useful splits for this team: ai_product
(posthog_ai / code / mcp / wizard), model, feature. tenant_id not set."pattern:llm_analytics:latency-bands — "Per-model p90: nano ~2s, sonnet ~19s,
o3/preview structurally high ~40s+ — band per model, never aggregate."noise:llm_analytics:o3-400-class — "o3 HTTP 400s are a benign recurring class;
re-investigate only if > 100/hr for 2h or daily rate clears 0.05%."addressed:llm_analytics:model-swap-2026-04-28 — "Sonnet → Opus 2026-04-28; cost
~2.1x baseline expected."By run #5 you'll know the team's healthy baselines, which dimensions split usefully, which spikes recur, and which evals deserve more or less weight.
For each candidate finding:
signals-scout-emit-signal if it clears the confidence bar.
Findings carry a hypothesis, evidence, severity, and confidence ∈ [0, 1].
Strong scout findings: confidence ≥ 0.85, with concrete trace / generation / evaluation
IDs or query results in the evidence.noise: or addressed: key prefix already covers it.If a prior run already covered the topic, default to skip + memory refresh rather than re-emit. Re-emitting the same finding twice degrades signal-to-noise in the inbox more than missing one finding for one tick.
Summarize the run — one paragraph: which lens(es) you looked at, what you emitted, what
you remembered, what you ruled out and why. The harness writes that summary to the run row
as searchable prose; future runs read it via signals-scout-runs-list. Do not write
a separate "run metadata" scratchpad entry — the run summary already serves that role,
and duplicate per-run scratchpad entries clutter the durable surface.
noise: entry for them, skip; otherwise leave one.properties.environment ∈ {dev, local} or
internal user. Filter before weighing.$ai_evaluation from a CI pipeline are not
user-facing traffic; check the calling user / source before treating as a regression.$ai_is_error; filter them
before weighing an error trend.llm_analytics:evaluation signal source. Only emit when you've localized a cause the
auto-flow won't.When in doubt, write a memory entry instead of emitting. Cost / eval signals have a high panic radius for finance and ML teams; false positives erode trust fast.
Telemetry & cost:
query-llm-traces-list — recent traces, filterable by user / model / cost / error / tool.query-llm-trace — drill into a single trace (full request/response, tool calls, spans).get-llm-total-costs-for-project — top-level cost surface.execute-sql — the workhorse for trends and breakdowns over $ai_* events (read
posthog:querying-posthog-data for HogQL discipline).Evals & enrichment config:
llma-evaluation-list — eval config only (name, type, enabled). Pass-rates are NOT
here — read the trend from $ai_evaluation events via execute-sql (the reliable path).llma-evaluation-summary-create — optional AI pass/fail/N/A pattern summary (billed,
rate-limited, currently prone to 500s — a drill-down, not the spine). Pair with
llma-evaluation-get / -test-hog.llma-tagger-list / llma-score-definition-list — the enrichment config surface
(auto-taggers and scorers — LLM/Hog jobs that can silently break).llma-clustering-job-list / -get — semantic clusters over traces/generations.llma-prompt-list / -get — prompt versions, for correlating a change to its cause.Schema:
read-data-schema — discover events, properties, and the team's custom dimensions
before filtering or grouping on them.Harness-level:
signals-scout-project-profile-get — cold orientation snapshot.signals-scout-scratchpad-search / signals-scout-scratchpad-remember — durable steering across runs.signals-scout-runs-list / signals-scout-runs-retrieve — what prior runs found.signals-scout-emit-signal — emit a finding.Deep-dive skills (baked into the sandbox — read the matching one when you go deep, don't
reinvent its queries): posthog:exploring-llm-costs, posthog:exploring-llm-traces,
posthog:exploring-llm-evaluations, posthog:exploring-llm-clusters, and
posthog:querying-posthog-data. See references/lenses.md for which skill maps to which
lens.
noise: / addressed: / dedupe: key
prefix → skip with a one-line note."Looked but found nothing meaningful" is a real outcome, not a failure.
npx claudepluginhub anthropics/claude-plugins-official --plugin posthogSets up an LLM-judge evaluation to extract canonical use cases for a PostHog feature at scale, streaming results to Slack as a live feed. Use when you want to understand how users are really using an AI/LLM-powered feature without manually reading hundreds of traces.
Retrieves and synthesizes all recent AI agent findings from Amplitude into a prioritized narrative with follow-up actions. Useful for surfacing agent-driven analytics insights.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.