signoz-setting-up-observability

name	signoz-setting-up-observability
description	Run the full end-to-end observability setup for a service after its telemetry is already flowing into SigNoz — sequence SLI/SLO capture, data exploration (RED/USE), focused dashboards, saved Explorer views, burn-rate and absent-data alerts, and a tuning loop into one opinionated, SLO-aware workflow. Make sure to use this skill whenever the user says "set up observability after ingestion", "now that data is flowing, give me dashboards and alerts", "onboard this service to SigNoz end-to-end", "I want the full monitoring setup for X", or asks to go from raw telemetry to a complete dashboard + alerts + views package — even if they don't say "observability" explicitly. This is the orchestration layer: for a single artifact (just a dashboard, just one alert, just a saved view, or one static threshold alert, or a one-off query) use signoz-creating-dashboards, signoz-creating-alerts, signoz-managing-views, or signoz-generating-queries directly.
argument-hint	<service name + what to set up (dashboard / alerts / views)>

Setting Up Observability After Ingestion

Take a service from "telemetry is flowing into SigNoz" to "I have a dashboard, alerts, saved views, and a tuning loop." This skill is an orchestration layer, not a standalone reference — the mechanical work lives in the sibling SigNoz skills and the MCP tools/resources hold the current payload schemas. This skill sequences them and supplies the judgment calls (scope, SLOs, thresholds, what to skip) that no single skill owns; it deliberately does not restate payload schemas or field rules, so read the relevant signoz://… resource before composing any payload.

Use this when traces, logs, or metrics are already landing in SigNoz and you want an opinionated, SLO-aware setup — not when you're still wiring up the SDK. Audience: two consumers — an autonomous AI SRE agent that runs without a human in the loop (e.g. an in-product SigNoz assistant), and an engineer at a Claude Code / Codex / Cursor prompt. Both follow the same flow; where a step needs a human decision (Phase 1 triage, Phase 5 sign-off), the host decides how to surface it, and an autonomous host fills the gap from upstream context instead of blocking.

Prerequisites

This skill calls SigNoz MCP server tools (signoz_list_services, signoz_list_dashboards, signoz_get_field_keys, signoz_execute_builder_query, signoz_create_dashboard, signoz_create_alert, signoz_create_view, etc.) and delegates to sibling skills. Before running the workflow, confirm the signoz_* tools are available. If they are not, the SigNoz MCP server is not installed or configured — run signoz-mcp-setup first. Do not fall back to raw HTTP calls or fabricate payloads.

When to use

Use this skill when the user wants the whole post-ingestion setup: SLI/SLO → exploration → dashboard → views → alerts → tuning, in any combination of those deliverables, sequenced as one workflow.

Do NOT use this skill when the user wants a single artifact — hand off directly:

• Just a dashboard → signoz-creating-dashboards.
• Just one static / threshold alert or notification rule → signoz-creating-alerts. (But SLO / burn-rate / error-budget alerting — even with no dashboard — stays here: it needs the SLI/SLO capture and burn-rate judgment in the phases below.)
• Just a saved Explorer view → signoz-managing-views.
• A one-off exploratory query → signoz-generating-queries.
• A concept/doc lookup (SRE methods, OTel conventions, burn-rate math) → signoz-searching-docs / signoz_search_docs.

Each phase below points to the skill that does the mechanical work; this skill owns the order and the decisions.

Phase 1 — Triage scope (don't skip)

Before touching any tools, get these answers. Each unblocks a downstream phase.

1. What does this service do, and does it call external services? One or two sentences. This shapes the SLI (what "working" means) and flags downstream dependencies worth monitoring — if it calls a DB, cache, queue, or third-party API, plan to watch the client-span latency and error rate to each dependency, since a healthy service fronting a sick dependency still fails users.
2. What deliverables? dashboard / alerts / saved views — any combination.
3. Infra monitoring too? (optional — ask) If the service runs on infra you own (k8s pods, VMs, containers) shipping telemetry under the same resource attributes, you can add USE/infra panels (CPU, memory, restarts, network) with no extra instrumentation — see Phase 3. Skip if a platform team already owns the host/cluster dashboard.
4. Which signals are emitted? traces / logs / metrics. Drives which signoz_get_field_keys calls you make.
5. What's the canonical resource filter? Usually service.name. Confirm the exact value before querying — don't guess.
6. What does success look like for users of this service? (Sets up Phase 2.) Even a one-line answer ("requests under 1s with no 5xx") is enough — it becomes the SLI.
7. Who owns it, and is there a runbook? Owning team, service tier, and the prod/staging scope — these become alert labels and annotations in Phase 8. Capture a real runbook URL if one exists; never invent one.

Offer "explore data first" as an explicit option.

Phase 1.5 — Inventory what already exists

Each create sub-skill runs its own rigorous duplicate-check when invoked (signoz-creating-dashboards lists dashboards; signoz-creating-alerts paginates alert rules) — don't re-implement that here. The orchestration value is a single up-front sweep so you sequence around what exists instead of hitting collisions mid-build:

• Is the service reporting, and under what exact name? (signoz_list_services.) This name is the resource filter every later phase reuses — pin it before anything else.
• Does a dashboard / alert / saved view already exist for it? If so, plan to extend it — route to signoz-modifying-dashboards, signoz-managing-views, or signoz_update_alert — rather than standing up a parallel one. signoz-explaining-dashboards / signoz-explaining-alerts summarize anything you're inheriting.
• Is there a notification channel to reuse? (Carried into Phase 5.)

Learn just enough to decide extend-vs-create per artifact; leave the exhaustive listing to the sub-skills.

Phase 2 — Capture the SLI/SLO before designing anything

This is the gating artifact. Without it, alerts become "the metric crossed a line" instead of "the user-visible error budget is being burnt fast enough to matter."

Collect:

• SLI formula — good events / valid events. E.g. successful checkout requests / total checkout requests (excluding health checks, synthetic probes, scanner traffic, and non-user long-poll endpoints).
• SLO target — e.g. 99.5% over a 30-day rolling window.
• Exclusions — what doesn't count as a valid event (synthetic probes, scanner traffic, streaming endpoints).
• Error budget remaining — derived from target. Drives burn-rate alert math.

Carry these forward — they feed Phase 8 directly. (SLO/burn-rate background: signoz-searching-docs.)

Phase 3 — Explore what's actually there (RED + USE coverage)

signoz-generating-queries owns the exploration itself — the discover-before-querying rule, every discovery call (signoz_list_metrics, signoz_get_field_keys, signoz_get_field_values, signoz_get_service_top_operations), tool choice, and the no-data distinction. Drive it to inventory the data; don't restate its query mechanics here. (signoz-searching-docs covers the RED/USE methods and SigNoz field semantics if you need a refresher.)

The orchestration job is to map what it finds onto RED (Request rate, Errors, Duration) and USE (Utilization, Saturation, Errors), spot the gaps, and make the judgment calls generating-queries doesn't:

Coverage checklist (RED for services, USE for infra/resources):

• Rate — request/call rate per operation
• Errors — error count or rate per operation
• Duration — p50/p95/p99 latency from spans or histograms
• Utilization — CPU, memory, disk, queue depth, pool occupancy (only if you own the infra)
• Saturation — request queue, GC pause, connection pool wait
• Errors at the resource layer — disk failures, evictions, OOM kills

A missing signal (e.g. no error count) is an instrumentation gap — flag it before building around what's there.

Span metrics back alerts, not just panels. When generating-queries surfaces SigNoz's trace→metrics output (signoz_calls_total, signoz_latency_*, DB/external-call span metrics), prefer it as the RED source for both the dashboard and the burn-rate alerts: it's pre-aggregated and cheap, whereas raw trace aggregation over a long alert window is expensive and an unstable alert source. (Confirm exact metric/label names via discovery — don't hardcode.)

Render any counter by the volume you just measured, not by reflex. Not throughput-specific — it applies to every counter you'll graph (request rate, error counts, restarts, OOM kills, evictions), and low-volume errors and resource-layer events are the usual victims, not just requests. Per-second rate suits steady high-volume signals; where Phase 3 shows the count is low, bursty, or human-paced, a /sec graph renders as unreadable tiny decimals (0.03/s). Use the symptom, not a hard threshold: if per-second shows as fractions, pass that to Phase 6 as intent — render a per-interval increase count over a wider window (24h–7d), not a rate. (Gauges — CPU, memory, queue depth — are already absolute; this is a counter concern.) It's a deliberate tradeoff (increase rescales its y-axis with the selected range), so make it a conscious call, never a blanket default either way. signoz-creating-dashboards owns the rate-vs-increase mechanic.

Verify the infra→service join (the USE half). Don't assume service.name is present on host/container/k8s metrics — they often carry only host.name / k8s.pod.name / k8s.node.name, and k8s.deployment.name may differ from service.name. Have generating-queries confirm which identity attribute actually joins the infra metric to this workload, then scope USE panels by that confirmed attribute. If nothing joins, ask the user for the workload selector rather than guessing.

Discover downstream topology from traces, not the human's memory (Phase 1 Q1). The gotcha worth carrying: client spans are kind_string = 'Client' in the SigNoz v5 schema — not kind, not SPAN_KIND_CLIENT — so verify the field/value, then group by the populated dependency attribute (external_http_url / http_host for HTTP, db_name / db_operation for databases) to surface every downstream the service actually calls, including ones the human forgot.

Multi-tenant note — if the service serves multiple tenants/regions, look for a stable non-PII tenant/region attribute (e.g. tenant.id, region, deployment.environment) — avoid raw URLs or user identifiers. Per-tenant breakdowns belong on the dashboard; aggregate-only views hide single-tenant outages.

SLI hygiene — confirm the events your SLI counts carry the right labels. For a broad denominator ("all requests to the service"), scanner traffic on /.env*, /.git* is real but should be excluded — it's not user traffic. The exclusion predicate should only subtract scanners and stay neutral otherwise, so use the bare negation — <url_or_route_field> NOT REGEXP '/\\.(env|git|aws|ssh|well-known)' — not EXISTS AND …. SigNoz negative operators also match rows where the field is absent, so fieldless valid events (non-HTTP spans, internal ops) stay counted; let the SLI's own scope filter (service.name, http.route) define the event universe, not the scanner predicate. (Pair EXISTS with a negation only for the opposite intent — excluding a specific value among rows that have the field, e.g. service.name EXISTS AND service.name != 'redis'.) A well-scoped SLI already excludes scanner paths, so this mainly bites service-wide ratios. Keep a separate security view if you care about scanners.

Phase 4 — Classify labels by cardinality and cost

Before wiring labels into queries, classify each. Wrong classification is the #1 source of metric-bill explosions and dashboard unreadability.

Class	Cardinality	Where it goes
Routing	<20	Alert label, inhibition rule, notification policy (e.g. severity, team)
Variable	<100, human-readable	Dashboard variable (e.g. environment, tenant)
Breakdown	<500 typically	groupBy on graphs / tables (e.g. gen_ai.tool.name)
Trace/log-only	Unbounded	Only on traces or logs — never on metric series

Never let raw URLs, query strings, user IDs, session IDs, or request IDs end up as metric labels — they explode cardinality and the bill. Those belong on traces/logs.

Cardinality matters more for cost than for display performance — SigNoz/ClickHouse handles 100s of variable values fine; the limit is human readability of the dropdown.

Phase 5 — Confirm channels, burn-rate windows, recovery thresholds

Lock in before creating alerts:

1. Notification routing. Decide which channel each severity tier routes to (e.g. critical → PagerDuty, warning → Slack). Reuse an existing channel from signoz_list_notification_channels where one fits; never invent a name. The actual channel resolution/creation and secret handling are owned by signoz-creating-alerts (Phase 8) — here you only lock the tier→channel mapping.

2. Burn-rate windows. Common starting points for a 99.9% SLO over a 30-day budget:

   Severity	Burn rate	Long window	Short window	Routing
   Page-now critical	14.4×	1h	5m	PagerDuty
   Slow burn warning	6×	6h	30m	Slack
   Ticket-level low	1×	3d	6h	Ticket queue

   The burn-rate multipliers are SLO-independent — for a different target, compute each absolute threshold inline as error-rate = burn rate × (1 − SLO) (e.g. 14.4× → 1.44% at 99.9%). signoz-searching-docs explains the burn-rate method; it can't compute your numbers.

   Both windows must trip simultaneously for the alert to fire: the long window suppresses brief spikes; the short window gives fast recovery so a resolved incident stops paging quickly. (The 6× row is a page in the canonical SRE table — routing it to Slack here is a deliberate severity downgrade; adjust to your on-call norms.)

3. Recovery thresholds (hysteresis). Decide that every numeric alert recovers below its firing target (e.g. fire at 80%, recover at 70%) to prevent boundary flapping — signoz-creating-alerts carries this into the recoveryTarget field.

Surface these as a clarification with concrete proposed values, using the host application's question/UI mechanism (a structured clarification tool, inline tags, an interactive prompt — follow the host's rules). Never ask open-ended "what threshold do you want?" — propose, let the user adjust. Get explicit sign-off on scope and thresholds before building. Flag baselines that affect thresholds ("current error rate is 5.1%, so a static 5% alert will be noisy — proposing a burn-rate alert instead").

Phase 6 — Build the dashboard(s)

Hand off to signoz-creating-dashboards — it owns templates, the layout/variable/OTel-naming defaults, the v5 schema and its gotchas, the no-data probe, and the mandatory per-panel dry-run (signoz-modifying-dashboards for later edits). Do not restate panel JSON or grid mechanics here; pass intent and let that skill build.

Prefer several focused dashboards over one large one. Since signoz-creating-dashboards builds one dashboard per call — and its template catalog is already organized per technology — the orchestration decision is which set of boards to compose. Default to splitting by category/audience and invoking the sub-skill once per board:

• Service / RED (APM): KPI tiles + request rate, errors, p50/p95/p99. The primary on-call board.
• Infra / USE: CPU, memory, restarts, network — only if infra monitoring was opted into (Phase 1 Q3), scoped by the attribute confirmed in Phase 3. Usually a stock template (hostmetrics, k8s) imported as its own board.
• Downstream dependencies: client-span p99 + error rate per dependency — only if the service has external deps (Phase 1 Q1), grouped by the attribute found in Phase 3 (external_http_url / db_name).
• Business / SLO: the SLI ratio and error budget, with a per-tenant breakdown if multi-tenant (Phase 3) — aggregate-only panels hide single-tenant outages.

Splitting keeps each board fast, single-purpose, and ownable by the right audience (SRE vs app dev vs platform). Collapse into one board only when the split would otherwise yield several near-empty ones — a small service's handful of panels reads better as rows on one board than as four 2-panel dashboards. Each board still passes the Phase 1.5 extend-vs-create check, so you reuse an existing board rather than adding a parallel one.

Quality bar (every board you create): production config for a 3am responder — each panel earns a "what to watch for" description and a drill-down, and each dashboard earns a top-of-page "start here". Pass this as intent so signoz-creating-dashboards applies it.

Phase 7 — Saved Explorer views

Hand off to signoz-managing-views for the create mechanics (it owns the view payload and the extraData/selectColumns shape). The orchestration value is which views to stand up — cheaper than panels and high-leverage mid-incident:

• ERROR/FATAL logs for the service.
• API request list — the Traces list view over server spans (kind_string = 'Server', verify per Phase 3): a per-request table surfacing service / operation (or http.route) / http.response.status_code / duration / trace_id. The fastest "what are my endpoints doing right now" surface — sortable by latency or status, one click to the full trace, and the broad view that the failing/slow-span views below are just filtered subsets of.
• Failing spans for the canonical operation (has_error = true).
• Slow spans (duration_nano > <threshold>).
• Failed sub-flows (e.g. OAuth requests with errors).
• Correlation drill-downs — error logs carrying trace_id (a log spike jumps straight to the trace) and slow/error traces that link back to their logs.

Two cross-phase constraints to pass along: every view carries the same environment/tenant filter as the dashboard (so prod and staging rows never mix), and the selected columns pass the same PII / no-identifiers gate as Phase 4.

Phase 8 — Alerts

signoz-creating-alerts owns every per-alert mechanic — channel resolution (create-first with a test message, name-not-UUID, secret handling), severity defaults, op/matchType, units, annotations, evalWindow/recoveryTarget, anomaly-rule shape, and the mandatory dry-run. Drive it once per alert and don't restate any of that here. (signoz-explaining-alerts reads a rule back; signoz-investigating-alerts does RCA once one fires.)

What this orchestration layer adds on top of that skill:

• SLI-bound alerts are burn-rate pairs, not static thresholds. A static threshold on a ratio flaps; the burn-rate pair is the default for anything tied to the Phase 2 SLO.
• The burn-rate recipe (the one thing signoz-creating-alerts can't derive without the SLO). Hand it the inputs, not a rule shape: the bad/total event definition (prefer the Phase-3 signoz_calls_total error/total split over raw trace aggregation), the long+short window lengths from Phase 5, and the firing target burnRate × (1 − SLO). Two judgment calls travel with those inputs — the long and short windows must be evaluated as one rule that ANDs both (two independent rules page on every short spike; if the SigNoz version can't express the short-window confirmation in a single rule, ship the long window alone and say so), and if no clean bad/total split exists in the ingested data, burn-rate isn't buildable yet — fall back to a baseline-tuned threshold rather than emit a broken rule. Let signoz-creating-alerts + signoz://alert/* pick the version-correct rule shape. Note that creating-alerts' "would have fired N times in the last 1h" calibration can't grade a 6h/3d burn-rate window — don't read a "fired 0 times" there as tuned; the Phase 9 re-tune-after-a-week loop is the real calibration.
• Burn-rate only covers ratio SLIs — schedule the non-ratio failure modes too. A service that stops emitting, or whose traffic collapses, never burns error budget (the SLI ratio is undefined at zero volume), so the burn-rate pair pages no one. A complete setup adds a telemetry-presence / absent-data alert on the primary signal, and treats traffic/throughput as its own signal. Hand these intents to signoz-creating-alerts — it owns the rule shape (absent-data, anomaly, threshold) and the healthy-zero handling; the orchestration decision is only that they belong in the package.
• Batch + sequence. Resolve/confirm the channel (Phase 5 mapping) before any rule references it, then create the alerts as a batch grouped by routing tier — not one rule per metric.
• Manual-TODO boundary. Inhibition rules, org-level notification policies (usePolicy: true only opts into one), and maintenance windows are configured in the SigNoz UI / Terraform — these tools don't create them. Flag as TODO; never report them done.

Phase 9 — Hand off

Hand back to the human:

• Dashboard ID(s), view IDs, alert IDs. Don't synthesize frontend URLs — return IDs and let the UI resolve them.
• Open questions you couldn't resolve — hand them over as explicit TODOs.
• Baselines that may need tuning (e.g. "tool error rate is 5.1% today; burn-rate alert tuned for 99% SLO will fire occasionally — re-tune after a week of data").

What to avoid

• Inventing channel names or webhook URLs. Ask.
• Static thresholds for SLI-bound alerts. Burn-rate pairs are the modern default; static thresholds for ratios produce flap.
• Wrong-sizing dashboards. Don't cram 18 panels where 12 will do, and don't scatter four 2-panel boards where one would read better — split by category, then right-size each. Sprawl cuts both ways.
• Skipping dry-runs to save time. A 500 at create time is harder to debug than at dry-run time.
• One alert per metric. Group by routing tier, not per-metric.
• High-cardinality labels on metric series. They detonate the bill. Move to traces/logs.
• Synthesizing frontend URLs. Tools return IDs. Return IDs.
• Including scanner traffic in SLI denominators. Real but irrelevant. Exclude.
• Treating exploration findings as throwaway. Baselines, cardinalities, and error counts set your thresholds and tuning baseline — don't drop them. Persist them where they survive any host: bake the numbers into the artifacts (panel/dashboard descriptions, alert annotations) and the Phase 9 hand-off, all of which live in SigNoz. Don't rely on a local notes file.
• Letting AI-written instrumentation ship unaudited. Spot-check what is actually emitted before designing dashboards around it.