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AWS Observability

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Investigates root causes on the Omni context graph (service dependencies) and Intelligence insights; builds, configures, debugs, tunes AWS observability - operator-symptom questions, detecting Omni vs classic CloudWatch. CloudWatch on a reporting service: fleet health views, Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints), and Application Signals service map. CloudWatch Omni on an existing Space: SQL over logs/traces, PromQL, Omni dashboards, alerts and Slack notifications, programmatic/IaC access (API/SDK/CLI/CFN), driving Omni from a coding agent or skills, AI agent quality from traces - on-demand and online evaluation of live traffic, incl. setting it up, readback, evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides comprehensive domain expertise for AWS observability, covering both classic CloudWatch and CloudWatch Omni. It assists users in investigating service health, querying logs and traces, configuring alerts, and managing dashboards. The skill is designed to help operators distinguish between the two services, navigate their respective APIs, and perform advanced tasks like agent quality evaluation and context graph analysis. It does not handle initial service setup or onboarding, which are managed by a separate dedicated skill.

What it does

  • Investigates root causes using Omni context graphs and intelligence insights.
  • Queries logs, traces, and metrics using SQL, PromQL, and Log Insights.
  • Configures and tunes alerts, dashboards, and alarms for both CloudWatch and Omni.
  • Evaluates AI agent quality through trace scoring and online evaluation.
  • Provides methodology for service-health investigations and dependency analysis.
  • Supports programmatic access via AWS CLI, SDKs, and CloudFormation.

When to use it

  • When you need to investigate a service performance issue or root cause.
  • When you need to write or debug queries for logs, traces, or metrics.
  • When you need to create or update alerts and dashboards in CloudWatch or Omni.
  • When you need to evaluate the quality of an AI agent using trace telemetry.
  • When you need to understand service dependencies and blast radius.

Advantages

  • Provides clear routing between CloudWatch and Omni based on service signals.
  • Offers structured methodologies for complex observability tasks.
  • Includes specific checklists to ensure all required configuration facts are surfaced.
  • Maintains a strict scope to ensure accurate and relevant technical guidance.
  • Supports advanced observability features like dynamic instrumentation and agent evaluation.

Usage examples

“Why is my service experiencing high latency?”
The agent will guide you through a service-health investigation, starting with relevant telemetry signals and checking for configuration issues like timeouts.
“Create an Omni alert for high error rates.”
The agent will provide the necessary authoring guidance, ask for your specific threshold and evaluation period, and confirm the configuration before proceeding.
“How do I query my traces in Omni?”
The agent will explain the SQL syntax for traces.default and provide the necessary fields to identify slow or failed spans.
“Evaluate the quality of my AI agent's recent sessions.”
The agent will help you select an appropriate evaluator and guide you through the process of scoring traces to assess agent performance.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/aws-observability/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip aws-observability.zip -d .agents/skills

Documentation by AWS

AWS Observability

Overview

Domain expertise for AWS observability across metrics, logs, and traces, for two products that share the CloudWatch name but are separate services with separate control planes, data models, and APIs:

CloudWatchCloudWatch Omni
What it isLog groups, metric namespaces, alarms, Log Insights, X-Ray, Application SignalsApplication Observability / Agent Observability. A Space per account per Region is the access boundary over the account's CloudWatch Dataset (OpenTelemetry logs, traces, and metrics); the Dataset is a CloudWatch resource the Space reads, not something the Space contains
Control planeaws cloudwatch, aws logs, aws xray, aws application-signalsaws cloudwatchomni (endpoint prefix cloudwatch-omni, signing name cloudwatch)
QueryLog Insights query language; GetMetricDataSQL over logs.default / traces.default; PromQL over metrics; named views
NotifyAlarms (metric, composite, anomaly)Alerts (SQL/PromQL rule, contributors, OK/WARNING/CRITICAL/NODATA)
TopologyApplication Signals service mapContext graph (GetContextGraph)
AccessIAM onlyDomain → Space → access grants and access profiles (set up in setting-up-cloudwatch-observability)
Only hereDynamic Instrumentation, Synthetics canaries, CloudTrail auditing, EMFAgent-quality evaluation, views, context graph
First-time setupApplication Signals onboarding — ADOT auto-instrumentation, the amazon-cloudwatch-observability add-on, ServiceEvents, CI/CD metadataDomain → Space → grants → telemetry in → plain-ADOT instrumentation
Referencesreferences/cloudwatch/references/cloudwatch-omni/

This skill covers using both products, never setting either one up. Both first-time setup paths above live in setting-up-cloudwatch-observability (same two-folder split); route there rather than describing an onboarding procedure from memory.

Enabling Omni does not replace CloudWatch; log groups, metrics, and alarms keep working, and most customers use both.

Works best with the AWS MCP server — enables running CLI commands, querying CloudWatch, and validating configurations directly. All guidance also works with standard AWS CLI access.

Note: Reference files contain specific runtime versions, quota values, and feature matrices that may change. When precision matters (e.g. deploying to production, choosing a runtime, or checking a quota), confirm values against current AWS documentation rather than relying solely on the values in these files.

Scope guard — is this an observability request?

This skill owns the scope decision for every request routed to it, and this section is its sole home.

If a request is NOT about AWS observability with CloudWatch or CloudWatch Omni (weather, trivia, general chit-chat, non-observability coding), decline it in one sentence: state plainly that it is out of scope, never fabricate an answer, and never claim a false capability limitation (no "no internet access", no "no weather data") — the reason is simply that it is out of scope. Then redirect by naming what this skill does cover: querying CloudWatch and CloudWatch Omni logs, traces, and metrics, building dashboards, configuring alerts, investigating a service, or agent evaluation.

Do not attempt the off-topic task, and do not call a tool or run a query in pursuit of it. Keep it brief: no lecture, no long refusal.

Step 0 — CloudWatch or CloudWatch Omni?

Decide this before routing. The natural wording ("set up an alert for high latency", "build a dashboard", "query my logs") does not say which product the customer means.

  1. The customer names the product — "Omni", "Application Observability", "Agent Observability", a Space, Domain, Dataset, access grant, spaceId, cloudwatch-omni, Omni SQL, PromQL, views, context graph, evaluators, agent evaluation / scoring traces / online or continuous evaluation / gen_ai.evaluation, and OTel span vocabulary (traces.default, logs.default, spans, durationNano, status.code, resource attributes, service.name) → Omni. "Log Insights", "log group", "metric namespace", "CloudWatch alarm", "metric alarm", "composite alarm", "anomaly alarm", "X-Ray", "Application Signals", "canary", "CloudTrail", "Dynamic Instrumentation" → CloudWatch. A "log group" named inside an agent-evaluation request is the online-evaluation data source to verify, not a CloudWatch signal. A bare "alarm" (or "alert") with no other product signal is ambiguous — fall through to rule 3 and probe: a Space → Omni alert (alerts.md); no Space → CloudWatch alarm (cloudwatch/alarms.md). Exception: a "PromQL alarm" is a CloudWatch alarm on OTel metrics (cloudwatch/alarms.md) — "alarm" wins over "PromQL".

  2. Knowledge or how-to question ("what is an Omni alert", "does Omni have an API", "how do alerts differ from alarms") → answer from the reference files directly. Do not probe the account, and do not divert to the other product. Whether a capability exists is a fact about the product, not the account. concepts.md carries the full feature-equivalence matrix. 2a. Authoring / how-to alert request — "create / set up / write an Omni alert that fires when X", "how should I alert on Y" — with no Space or Region supplied and no go-ahead to actually create it, is a HOW-TO request. Deliver the authoring guidance (the alert row of "Must-state checklists" below, then alerts.md) FIRST, from the reference files. Do NOT stall on a Region/Space clarifying question and do NOT fall back to a CloudWatch alarm for a request that explicitly says "Omni alert". Probe the account only once the user supplies a Space/Region or asks you to create it. 2b. A telemetry object is already in the prompt — the user pastes, or the UI passes as context, a span, trace, log record, or query result and asks what it means, what errors it has, or how long it took. This is not a live-data request: do not probe the account, run a query, or ask the user to fetch it again. Read it in place per the "Reading a span or trace you already have" section of query/sql-logs-traces.md and state every item in its must-state list. 2c. A query request whose live result is empty or unreachable ("show me the slowest spans", "which traces failed") is still answered with the methodology — the exact query and the field rules that make it correct (see the must-state callouts in query/sql-logs-traces.md). Zero rows, a wrong-Region Space, or an unreachable endpoint is reported as a finding alongside the query, never as the whole answer.

  3. Request that must act on live data, and the wording is ambiguous (not a Step 0.5 content question, which is answered from the catalog) → probe the target Region first:

    aws cloudwatchomni list-domains
    aws cloudwatchomni list-spaces --region <target-region> --query "items[?region=='<target-region>']"  # account-global; filter to the Region
    • list-spaces is account-global (the --region flag only selects the endpoint), so filter its result to the target Region as shown rather than reading a non-empty list as proof; a Space is one per account per Region. See references/cloudwatch-omni/concepts.md for the full Region-probe rationale.

    • A Domain and a Space in that Region (a non-empty filtered list) → Omni.

    • No Space in that Region (an empty filtered list) → CloudWatch: alarms → cloudwatch/alarms.md, dashboards → cloudwatch/dashboards.md, queries → cloudwatch/log-insights.md, metrics → cloudwatch/metrics.md. If the customer explicitly asked for Omni and has no Space, that is a first-time setup — see step 4.

    • The probe itself errors ("not yet supported", unknown service, endpoint does not resolve) → the CLI/SDK model in use lacks cloudwatchomni. That is not evidence Omni is absent and must not be reported as "Omni is unavailable". The customer's installed AWS CLI/SDK most likely predates the service. If the request carried any Omni signal, give the customer the upgrade command to run (AWS CLI v2 reinstall or brew upgrade awscli; pip install -U boto3 botocore) and have them re-run aws cloudwatchomni list-domains — exact steps in programmatic-access.md. Never run a package-manager upgrade or installer on the host yourself (brew, pip install -U, .pkg/MSI); it mutates the customer's machine beyond the request and can break unrelated tooling — hand over the command and continue with the guidance. Never substitute a CloudWatch or X-Ray command for an Omni request. If the request carried no Omni signal, do not block on the upgrade. When you are already going back to the customer for missing inputs (for example an alert's threshold and period), ask which product they want in that same question: say the Space probe could not run on this CLI, describe both options, and build neither until they answer. When the request is fully specified or says not to ask, proceed on the CloudWatch path (the pre-Omni default) and mention the upgrade only in passing.

    • Still inconclusive → ask the customer.

  4. First-time setup, on EITHER product → STOP and route to the setting-up-cloudwatch-observability skill. This skill covers a service or Space that already reports and holds no onboarding procedure for either product, so there is nothing here to fall back on: not Omni setup (creating a Domain or Space, granting access, ingestion, forwarding, Slack, instrumenting an app or AI agent for a Space) and not CloudWatch setup (onboarding a service to Application Signals — ADOT auto-instrumentation, the amazon-cloudwatch-observability add-on, monitored service, ServiceEvents, CI/CD metadata, the per-platform enablement guides). Any instrumentation / ADOT / collector request is a setup request either way; the setup skill owns the product decision too, so hand the whole request over rather than probing list-spaces yourself. Application Signals once it is reporting — service map, alarms on its metrics, Dynamic Instrumentation debugging — belongs here.

Under-specified alert requests: When an alert or alarm request names what to watch (a symptom or a service) but not the inputs it needs — the threshold value and the evaluation period — ask for those rather than inventing them. Notifications are optional (per alerts.md), so ask for a notification destination only if the user wants to be notified. This holds on both paths (an Omni alert or a CloudWatch alarm).

Under-specified dashboard requests: When a dashboard request names what to show but not which metrics, panels, or layout, ground those against the data and confirm the panel set rather than inventing panels; a dashboard has no threshold, period, or notification. For ANY dashboard authoring/save request, also open dashboards.md and surface its "Facts you MUST surface when building or saving an Omni dashboard" checklist (see "Must-state checklists" below). Which signals or panels a named resource type needs is a Step 0.5 catalog question, not a dashboards-file question.

Step 0.5 — Service-health investigation (routing)

A large share of real questions are phrased as an operator symptom, not as a tool: "is my <service> throttled / slow / erroring / unhealthy," "which of my <service>s are <symptom>," "what's the health of my <service>," "what does <service> depend on and which is broken," "what signals / what should be on a dashboard or view for <service>." These are observability-data questions. Start from the telemetry, and answer with the methodology (the correct signals, aggregation, scoping, and caveats) even when you also pull live numbers and even when no matching resource exists in the account. Telemetry shows the symptom. The cause is often a configured value, so when the question is why something fails, also read the configuration of the resources involved, for example an API Gateway integration timeout, an SQS visibility timeout, or a Lambda timeout or event source mapping. A read-only or "logs only" role can usually still call these Get and Describe APIs. Make the call and report AccessDenied if it happens.

Route by the symptom, then open the reference and surface every applicable item in its "facts you MUST surface" checklist — the checklist is the output contract, and it lives in the reference file, not here:

  • A metric symptom about one AWS service (throttling, latency, error rate, restarts, saturation, "what signals should it get", which signals or panels a named resource type needs on a dashboard or view) → query/promql-metrics.md. Open its "Service-health question — facts you MUST surface" section and surface every applicable item. Dashboard or view wording does not send this to either dashboards file (neither carries the signal facts) nor to rule 3's probe — it is a content question.

  • "What does <service> depend on / what's broken downstream" / blast radius / who is affected / which direction do I walk the graph / what do CALLS, ACCESSES, RUNS_ON mean → context-graph.md. Open its "Dependency / blast-radius question — facts you MUST surface" section and surface every applicable item. A slowness or error symptom phrased in terms of the graph, dependencies, or edge types routes here, not to the metric bullet above.

  • A health VERDICT for a service ("is <service> healthy", "health summary of <service>") or a dependency map WITH ROLES ("what does <service> depend on and what role does each play", "which services call <service>") → service-health.md. Open its "Service health & dependency question — facts you MUST surface" section and surface every applicable item — it owns the combined verdict and the role-labelled dependency map, and defers the per-resource signal catalog and blast radius elsewhere. A bare single-service metric symptom, or a "what signals / panels does <resource type> need" or fleet-health-view ask, routes to the promql-metrics bullet above; a pure blast-radius / direction / edge-semantics question routes to the context-graph bullet.

  • System status, a root-cause / triage symptom, or a past incident — "is anything wrong right now?", "what's wrong with <service>", "why is <service> slow or erroring", "what changed since <time>", "users reported errors between X and Y — which service is the single most likely root cause", or any mention of insights, observations, or anomalies (a cause ask, not a single-signal health verdict or a dependency list) → intelligence.md: start from Omni Intelligence insights and observations, then use the context graph and queries to confirm. Its own "Scope and neighbours" section states which asks it hands back to the bullets above.

  • Individual slow or failing spans / traces ("slowest spans for <service>", "which traces failed") are trace SQL, not a metric aggregate → query/sql-logs-traces.md. Open its "Span Duration" and "Finding failed spans" sections and state every item in their must-state callouts — a request about a service's latency or error rate (an aggregate signal) is the PromQL bullet above instead.

  • A failure being investigated from logs ("is this only failing for X?", "how many errors?", "what is the root cause?") → load the aws-investigation skill and read its references/completeness-and-root-cause.md. Read every page and every scope before counting, and check the root cause against every fact before reporting it.

Must-state checklists — the output contract

Four Omni tasks carry a checklist the answer text must carry, not just the plan. Open the reference and surface every item relevant to the request; the reference holds the per-item detail and is the source of truth, so do not restate it here.

TaskOpenSection to surface
Authoring or advising on an Omni alertalerts.md"Facts you MUST surface when authoring an alert"
Scoring traces, choosing an evaluator, reading stored scores, online/continuous evaluation, evaluation datasets, turning scores into agent changesagent-evaluation.mdevery "tell the user ALL of this" callout
Exploring traces — one trace by id, comparing runs, finding traces by their spans, slowest traces, trace fields, agent sessions, token cost, what changed between two windowsquery/trace-exploration.mdevery "state … in the answer" callout
Authoring, saving, reading back, or debugging an Omni dashboarddashboards.md"Facts you MUST surface when building or saving an Omni dashboard"

Routing — CloudWatch (references/cloudwatch/)

User needAction
Enabling/onboarding a service to Application Signals (auto-instrumentation, the amazon-cloudwatch-observability add-on, monitored service), propagating ServiceEvents git/deployment metadata through CI/CD, or the per-platform × per-language enablement stepsSTOP — this is first-time CloudWatch setup and no reference here covers it. Route to the setting-up-cloudwatch-observability skill
Writing Log Insights queries (pipe-delimited syntax: fields, filter, stats, sort, parse, display)Read log-insights.md. If StartQuery rejects the window as before the log group's creation time, read the same window with FilterLogEvents. See Known issues 11 there
Configuring alarms (metric, composite, anomaly)Read alarms.md. For an Omni alert, see the Omni table
Publishing custom metrics or using EMFRead metrics.md
X-Ray / ADOT tracing behaviour — X-Ray-SDK-vs-ADOT choice, trace and segment structure, annotations vs metadata, sampling rules, collector pipeline config, X-Ray→OTel migration trapsRead tracing.md. Onboarding an un-instrumented service is the setup skill's job (see the first row)
Building CloudWatch dashboards (widget mechanics; which signals a given AWS service needs is Step 0.5)Read dashboards.md
Debugging observability issuesRead troubleshooting.md — starts with the 5 most common fixes
Investigating a failure from logs: whether it affects one entity or the whole fleet, counts/totals/"no evidence of X", or deciding and reporting the root causeLoad the aws-investigation skill — completeness and scope first (references/completeness-and-root-cause.md), then root-cause analysis and the closing checklist
Debugging canary failuresRead synthetics.md — see Common failures table
CloudTrail operational auditingRead cloudtrail.md
Setting up Lambda monitoring with CDKUse alarm-template.ts as a starting point
Creating synthetic canariesRead synthetics.md
Configuring ADOT collectorUse otel-config.yaml as a starting point
Debugging a running service with breakpoints/snapshots — Dynamic Instrumentation (modifies live services and captures live data)Read dynamic-instrumentation.md in full before acting. Confirm with the user before any create/delete, and narrate before significant actions: observation → hypothesis → proposed action → expected result. Source inspection alone identifies hypotheses, not confirmed root causes; keep suspected causes tentative until runtime evidence confirms them.

Routing — CloudWatch Omni (references/cloudwatch-omni/)

Rows that act on live Space data assume Step 0 found a Space. Knowledge questions are answered from the file directly.

User needAction
Concepts. What Omni is, what a Domain / Space / Dataset / grant / profile / view / alert / context graph is, whether a feature is Omni or CloudWatch, where setup startsRead concepts.md
Query logs or traces — SQL (SELECT … FROM logs.default / traces.default / default), field access, schema discovery, slowest / failed spans (durationNano, status.code), TABLESAMPLERead query/sql-logs-traces.md. For slowest or failed spans, state every item in its "Span Duration" / "Finding failed spans" must-state callouts, even when the live result is empty
Explore traces — open one trace by id (where the time went, why it failed, span tree, inputs/outputs, tokens), compare two runs, why a trace scored badly, find traces by their spans (tool calls per trace, a repeated tool, failed tool calls), the slowest traces today, what fields a trace carries, list or walk an agent's sessions (a looping session), token cost, guardrail or deploy questions, what changed (agent, model or downstream)Read query/trace-exploration.md for the trace recipes and agent-span attributes, with query/sql-logs-traces.md for the SQL itself
A span, trace, or log record supplied in the prompt — "I have this span open, what errors are in it", a pasted telemetry objectRead the "Reading a span or trace you already have" section of query/sql-logs-traces.md. Answer from the object's own fields; do not probe, query, or ask the user to fetch it
Query metrics — PromQL, which metric answers which symptom per AWS service, why a metric is missing, gauge vs counterRead query/promql-metrics.md. Metrics are PromQL, never SQL; run them directly through the CloudWatch Prometheus-compatible API (monitoring endpoint) or the CloudWatch MCP server's PromQL tools, not through StartTelemetryQuery
Views — create, manage, or query named reusable SQL (FROM view.<name>)Read query/views.md
Dashboards in Omni — compose, ground panel queries, author panels[], lay out the grid, the API save semantics (an unknown root- or panel-level key, a missing type/layout, or a bad variant is REJECTED at save with a 400 ValidationException; a bad enum VALUE, x+w>60, or an unknown key inside config saves 200 and fails or is ignored at render; validate before save), fix an empty or blank panel, the *OmniDashboard APIsRead dashboards.md
Alerts in Omni — any mention of an Omni alert, CreateAlert / GetAlert / ListAlerts / UpdateAlert / DeleteAlert, a profileId, an alert ARN, or how alerts differ from alarms; create, tune, tag, list, delete; notificationsRead alerts.md. The alert API is real and first-class — do NOT redirect to CloudWatch alarms. For CloudWatch alarms when Omni is not enabled, read cloudwatch/alarms.md
Omni Intelligence — is anything wrong right now, status of a service, why is a service slow or erroring, what caused an incident between two times, root cause, past incidents, insights, observations, anomalies, ListInsights, ListObservationsRead intelligence.md; it links to the context graph for cause-vs-impact questions. ListInsights, ListObservations, and GetContextGraph are the public CloudWatch Omni operations it teaches (aws cloudwatchomni list-insights / list-observations / get-context-graph), with the Intelligence console view as a human-readable cross-check. It consults the intelligence layer, localizes on the context graph, confirms with telemetry, and names one root-cause service. For a health verdict or dependency roles with no single-cause ask, use service-health.md; for pure dependency / blast-radius topology and graph traversal, use context-graph.md
Context graph — why is service X slow or failing, what depends on it, upstream/downstream, which direction to walk, edge types CALLS / ACCESSES / RUNS_ON, blast radius, walking from an insight or anomaly to a root cause, GetContextGraphRead context-graph.md and state every applicable item in its "facts you MUST surface" section
Service health & dependency analysis — is <service> healthy, health summary, what does <service> depend on and what ROLE does each play (caller / callee / datastore / runs-on), which services call itRead service-health.md and state every applicable item in its "facts you MUST surface" section. It grounds the verdict in the context graph + RED/USE metrics + alarm state; for the resource-specific signal catalog and query mechanics it defers to query/promql-metrics.md (§5 per-service catalog), query/sql-logs-traces.md, and alerts.md, for traversal and blast radius to context-graph.md. A "what signals / panels does <resource type> need" or fleet-health-view ask goes to promql-metrics.md, not here
Agent evaluation — score traces on demand, choose an evaluator, read back stored gen_ai.evaluation.* scores ("which evaluators are doing worst", "which online evaluators are unhealthy / underperforming"), build datasets from traces, set up online evaluation, author a custom evaluator, audit whether an agent's traces are flowing, monitor quality, root-cause a quality regression, recommend prompt or tool-description changes from scores, verify a change against a baselineRead agent-evaluation.md and state every applicable item in its "tell the user ALL of this" callouts
Programmatic access — "is there an API or SDK for Omni", calling Omni from code, CI, IaC, or an AI coding agentRead programmatic-access.md. Omni has a real public SigV4 API; never answer that it has none, never substitute the CloudWatch or X-Ray CLI/SDK, and answer without probing for a Space. Endpoint cloudwatch-omni.<region>.api.aws, never .amazonaws.com; client cloudwatchomni, no hyphen. Give both authorization classes: a grant-mediated caller needs a matching grant or is denied; a direct IAM caller with none is not denied by the Space — its own IAM policy decides
AWS DevOps Agent — Investigations reports it is not connected, connecting it to a Space, or running, following, or chatting with an investigationConnecting is first-time setup: route to the setting-up-cloudwatch-observability skill. In the Omni web app an investigation starts from a thread, /investigation new <description>, or a firing alert's Investigate action. Driving the DevOps Agent itself (starting, following, or chatting with investigations from a coding agent) belongs to its dedicated aws-agents-for-devsecops plugin; point there and do not reconstruct that workflow or invent aidevops calls
Who has access to a Space, granting or revoking access, access profiles, creating a Space or Domain, ingestion, forwarding, Slack, Azure, instrumenting an app or AI agent — and, on the CloudWatch side, onboarding a service to Application SignalsRoute to the setting-up-cloudwatch-observability skill. If it is not installed locally, load it with the AWS MCP retrieve_skill tool (skill_name: setting-up-cloudwatch-observability; pass file for a reference it cites)
Spans multiple areasRead the most specific reference first, then consult others as needed

Files

references/cloudwatch/
FileContent
alarms.mdMetric, composite, anomaly detection alarms — configuration, constraints, recommended defaults
log-insights.mdComplete query syntax, commands, functions, known issues, reusable query library
metrics.mdCustom metrics, EMF spec, metric filters, high-resolution, retention
tracing.mdX-Ray → ADOT migration, sampling rules, annotations vs metadata, collector config
dashboards.mdWidget types, cross-account/region, dynamic labels, sharing
troubleshooting.mdError → cause → fix for all observability services
cloudtrail.mdOperational auditing, event types, S3+Athena queries
synthetics.mdCanary runtime/blueprint constraints, VPC networking, common failures
dynamic-instrumentation.mdDynamic Instrumentation debugging loop — breakpoints/probes on live code, snapshot capture + correlation analysis, create/delete gating, snapshot PII handling. Runs via scripts/cloudwatch/di_instrumentation.py + scripts/cloudwatch/di_snapshots.py; details in dynamic-instrumentation/
alarm-template.tsBest-practice CDK Lambda monitoring (alarms + dashboard)
otel-config.yamlADOT collector config for X-Ray traces + CloudWatch EMF metrics

Not here, by design: Application Signals onboarding (the enablement procedure, the ServiceEvents CI/CD chain, the 16 per-platform guides) is first-time setup and lives in setting-up-cloudwatch-observability. Do not reconstruct it here from memory; route.

references/cloudwatch-omni/
FileContent
concepts.mdWhat Omni is and is not; glossary (Domain, Space, Dataset, grant, profile, view, alert, dashboard, context graph, evaluator); Omni-vs-CloudWatch feature-equivalence matrix; how to tell which product the customer means; the setup sequence and where it lives
context-graph.mdThe service/resource topology Omni builds from traces and metrics; GetContextGraph request/response and CLI; reading upstream vs downstream and blast radius; walking from an insight or anomaly hop-by-hop to a root cause, then pivoting to queries
intelligence.mdOmni Intelligence — the insight and observation operations and data model, ListInsights / ListObservations inputs and filters, GetContextGraph with insights, answering status, troubleshooting, root-cause and past-incident questions, the 404-vs-403-vs-local error rule (owned by context-graph.md), cost and performance guardrails, and the empty-result/stale-insight gotchas
service-health.mdService health verdict and dependency analysis — dependency map with roles (caller/callee/datastore/runs-on), the multi-signal HEALTHY/DEGRADED/DOWN verdict (RED for services, USE for resources, alarm state), and how to build the checks from a node's includeMetadata selectors. Ties together context-graph, promql-metrics, sql-logs-traces, and alerts rather than duplicating them — defers traversal and blast radius to context-graph and the resource-specific signal catalog to promql-metrics §5
programmatic-access.mdThe public SigV4 API (cloudwatch-omni endpoint prefix, cloudwatch signing name), how access grants authorize a programmatic caller, CLI/SDK access (and why an unsupported-service error is a client-version issue), CloudFormation/CDK, AI coding agents, and the wrong answers to avoid
query/sql-logs-traces.mdSQL over logs and traces — table addressing, required time range, system fields, field access and quoting, schema discovery, supported operations, functions, common patterns (including durationNano span duration), constraints, TABLESAMPLE
query/promql-metrics.mdMetrics in Omni are PromQL — what is queryable (OTLP, span RED, OTel-enriched vended metrics) and what is not, label conventions, __name__ matcher, rate() on counters, per-AWS-service metric catalog with derived formulas and dimension traps, and running queries and discovering names through the Prometheus-compatible HTTP API and MCP tools
query/trace-exploration.mdWorking with whole traces on traces.default — span fields and AI-agent span attributes (span role, tool name, input/output, token usage), fetching one trace in the right window, time breakdown and self-time, failure analysis with exception events, comparing runs, tying a bad evaluation score to spans, tool-call and repeated-tool patterns, trace-level latency and "today", field discovery for traces, reporting, agent sessions, walking a session, token cost, signals the spans may not record, before/after comparison by layer
query/views.mdNamed SQL views: CreateView / UpdateView / DeleteView / ListViews, FROM view.<name>, naming and definition rules, composition patterns
dashboards.mdOmni dashboards — composition recipes, grounding panel queries, the panels[] body and panel types, visualizations, the 60-column grid, the API save semantics (unknown root/panel keys are rejected 400; unknown keys inside config save 200 and are ignored at render; validate before save), troubleshooting empty/blank panels, the Create/Get/List/Update/DeleteOmniDashboard APIs, archetype templates
alerts.mdOmni alerts — alert vs alarm, evaluation (FIELD_VALUE / COUNT_OF_RESULTS, contributors), states and no-data treatment, notification rules, step-by-step create / update / delete / tag / fetch, and the alert APIs
agent-evaluation.mdAgent-quality evaluation on OTel traces — instrumentation health audit, evaluator selection, on-demand scoring, online evaluation, custom evaluators, datasets from traces, reading back stored gen_ai.evaluation.* scores (retrieval plan + SQL mechanics), and turning scores into changes (monitoring, regressions, evaluator drills, golden datasets, change and re-run). Uses scripts/cloudwatch-omni/evaluate_traces.py and scripts/cloudwatch-omni/capture_dataset_from_traces.py

Files

references/cloudwatch/dynamic-instrumentation/breakpoint-creation.md

Breakpoint Creation and Troubleshooting Reference

How to specify a breakpoint correctly, and what to do when it misfires.

Two Instrumentation Levels

A breakpoint targets one of two levels, decided by whether you set line_number:

  • Method-level (set method_name, omit line_number): captures at function entry and exit — capture_arguments, capture_return (return value + throwable), and execution duration. Use for "what went in / what came out / how long." capture_locals does not apply at this level.

  • Line-level (set line_number, 1-based): captures the local variables in scope at that line via capture_locals. Use to inspect intermediate state mid-function (after an assignment, at a branch). No return value or duration is captured.

Rule of thumb: start method-level to bracket a function; drop to line-level when you need a specific intermediate value — and for void/None methods that mutate a field (see "Void / None-Return Mutated Fields" below).

BREAKPOINT vs PROBE

Default to BREAKPOINT for every debugging / root-cause task — line-level or method-level, one-off inspection of arguments, return values, locals, or timing, including on a live service. When unsure, use BREAKPOINT.

Use PROBE only when the user explicitly asks to either (1) capture past the max_hits cap, or (2) run long-term / ongoing observability. A mention of "production" or "live traffic" alone does not qualify — a normal investigation on a live service is still a BREAKPOINT.

  • BREAKPOINT (default) — capture-limited by max_hits (default 100); transitions to DISABLED once reached. Expires automatically at ttl_hours (set ttl_hours = 24). Supports line-level (line_number) and method-level targets.

  • PROBE (exception only) — method/function-level only (line_number must be omitted; the script rejects a PROBE create that sets it), not supported for JavaScript, no max_hits (fires on every hit). Never expires on its own — ttl_hours is ignored — so you MUST delete it explicitly when done.

Scoping to specific instances — attribute_filters

To apply a breakpoint only to certain service instances (e.g. one version or deployment), pass attribute_filters: a list of groups, each a dict of OpenTelemetry resource-attribute names to exact-match values (no wildcards/patterns), e.g. [{"service.version": "1.2.0", "deployment.environment": "staging"}]. Conditions are AND-ed within a group and groups are OR-ed together; up to 10 groups, keys 1–50 chars and values 1–100 chars. Omit to apply to all instances.

Capture-limit fields

When snapshot values come back truncated, raise the matching limit (all optional): max_string_length (string truncation), max_collection_width (collection width), max_collection_depth (nested collection depth), max_object_depth (object traversal depth), max_fields_per_object (object field count), max_stack_frames / max_stack_trace_size (stack capture). capture_stack_trace toggles stack capture (on by default). For truncated Java Map/HashMap contents, raise max_object_depth / max_collection_width.

Location Fields

  • file_path: source file path in the running application.

  • code_unit: Python module or Java package.

  • class_name: class name when targeting a class method.

  • method_name: function/method name.

  • line_number: required for line-level breakpoints; omit for function/method-level.

Python Mapping

  • code_unit = the target's importable dotted module name — the exact string you would put in an import statement for that module. The SDK resolves it with importlib.import_module(code_unit) and then looks up method_name on the result, so it must be the module as the running app imports it, not just the filename.

    • Derive it from the file path relative to the import root (the sys.path entry / working dir the app runs from): drop the .py and replace / with ., keeping every package segment.

    • Use "__main__" only for the script entrypoint (the file run as python foo.py).

    • If unsure of the import root, prefer the longest dotted path that import_module would accept and that exposes method_name.

  • method_name = function name; class_name = class name if the method is in a class.

  • Line numbers start at 1.

What "import root" means. Dotted module names are resolved relative to the directory the app is launched from (the sys.path entry that holds your code), not from the filesystem root. The same file gets a different code_unit depending on that root:

/srv/checkout/          <- import root (on sys.path: the dir the app runs from)
|-- services/
|   |-- __init__.py
|   `-- billing.py      <- defines generate_invoice()
`-- main.py

# import root = /srv/checkout    -> `import services.billing` -> code_unit "services.billing"  (keep `services`)
# import root = /srv/checkout/services -> `import billing`     -> code_unit "billing"

The absolute path (/srv/checkout/services/billing.py) is irrelevant; only the path from the import root down to the file becomes the dotted name. A truncated code_unit (e.g. just services, the package) imports successfully but lacks the function, so the breakpoint never installs.

// create arguments (Python method-level)
// import root /srv/checkout, file services/billing.py, function generate_invoice(...)
//   -> code_unit "services.billing"
{
  "instrumentation_type": "BREAKPOINT", "language": "Python",
  "file_path": "services/billing.py",
  "code_unit": "services.billing",
  "method_name": "generate_invoice",
  "capture_arguments": ["invoice_id", "customer_id", "amount"],
  "ttl_hours": 24
}
Direct import aliasing (important)

If a target function is imported by value (from mod import func), the SDK only wraps the function inside the defining module and does not update imported aliases — so a breakpoint on the defining module may never fire. Instead, target the importing module:

  • file_path = the importing file (e.g. __main__ → the app entrypoint).

  • method_name = the alias as used at the call site. For from mod import func, use func. For from mod import func as f, use f.

Java Mapping

Use the simple class name, NOT the fully qualified name.

  • code_unit = package name (e.g., com.amazon.sampleapp).

  • class_name = simple class name only (e.g., OrderService, not com.example.OrderService).

  • method_name = method name. Note: Java may have overloaded methods (same name, different params) — an ambiguous target surfaces as OVERLOADED_METHODS; disambiguate by signature.

// Given: package com.amazon.sampleapp; public class OrderContext { ... }
// create arguments (Java method-level)
{
  "instrumentation_type": "BREAKPOINT", "language": "Java",
  "file_path": "/path/to/OrderContext.java",
  "code_unit": "com.amazon.sampleapp",
  "class_name": "OrderContext",
  "method_name": "getCustomer",
  "capture_arguments": ["customerId"],
  "ttl_hours": 24
}
// code_unit = package name; class_name = simple name (NOT com.amazon.sampleapp.OrderContext)
// capture_arguments = the REAL parameter name from the signature ("customerId"), NOT "arg0".
// The snapshot may later render it as arg0 — that is a read-time concern, not a create input.

JavaScript Mapping

JavaScript binds by file_path + line_number only — it is always line-level.

  • line_number is required (>= 1); code_unit, class_name, and method_name are not used.

  • Point line_number at the executable statement you want to observe.

  • A breakpoint on a non-executable line slides to the next parseable line and fires there (unlike Python/Java, where it is ignored and never fires) — verify it lands where you intend.

  • PROBE is not supported for JavaScript — use instrumentation_type=BREAKPOINT.

Pre-flight Checklist

Before creating a breakpoint, read the relevant source files and verify:

  1. file_path matches the deployed runtime source path.

  2. code_unit matches the module/package exactly.

  3. class_name is the simple name for Java (not FQCN).

  4. method_name matches the executed symbol name.

  5. line_number is executable code if line-level.

  6. capture_arguments lists the real parameter names from the source signature (for Java too — never arg0/arg1; those only show up when reading the snapshot, never as a create input).

Code Snippet Display (when proposing breakpoints)

When proposing breakpoints, read the local source file and display a code snippet so the user can verify the location.

Method-level:

File: /app/product_service.py
Class: CacheKeyNormalizer  (omit if no class)
Method: def normalize_for_lookup(self, product_id)
Capture arguments: ["product_id"]

Line-level (target line + 2 lines context):

File: /app/product_service.py
   40|     key = product_id
   41|     if settings["strip_whitespace"]:
>> 42|         key = key.strip()
   43|     if settings["lowercase"]:
   44|         key = key.lower()
Capture locals: ["key"]

Argument Names

The create operation requires explicit capture_arguments — argument names are not inferred, and it rejects both ["*"] and an empty list. (Line-level breakpoints use capture_locals the same way, and a line-level create requires capture_locals.)

Python: read the source file directly, match the function/method signature, and list the parameter names explicitly in capture_arguments.

Java — create with the REAL names; snapshots may rename them positionally. These are two separate phases and the names differ between them. Do not confuse them:

  1. At create time: pass the real parameter names from the source signature in capture_arguments (e.g. ["amount", "orderId"]) — exactly as for Python. Read the source and use those exact names. Never pass arg0/arg1 to create — positional placeholders are not valid breakpoint inputs and will not match the method's parameters.

  2. When reading the resulting snapshot: Java bytecode does not always preserve parameter names, so the captured values may come back under positional keys (arg0, arg1, ...) no matter which real names you created with. Map those positional keys back to the signature by order:

# What you pass at CREATE (real source names):
#   capture_arguments = ["productId", "quantity", "couponCode", "state"]
#
# Method signature:
#   calculateTotal(String productId, int quantity, String couponCode, String state)
#
# How the captured values may appear when READING the snapshot (positional):
#   arg0 = productId
#   arg1 = quantity
#   arg2 = couponCode
#   arg3 = state

When building snapshot search filters (reading phase), use the positional names that actually appear in the captured data:

@message like /"arg0"/ and @message like /"laptop"/     # filter by productId
@message like /"arg1"/ and @message like /"10"/          # filter by quantity

(Filters match what is in the snapshot — arg0/arg1 — not the real names you created with.)

max_hits and DISABLED

Breakpoints stop capturing after max_hits is reached, and their status transitions to DISABLED. Use max_hits=100 as the default. If a breakpoint is DISABLED due to max_hits exhaustion and you need more snapshots, delete it and recreate it with the same parameters (or a higher max_hits). When doing multi-phase debugging, check whether earlier breakpoints are still ACTIVE before relying on them for new data. To recover ACTIVE timestamps from a disabled breakpoint, run di_instrumentation.py get-status with an earlier time range, then use those timestamps to fetch snapshots.

Void / None-Return Mutated Fields

HARD RULE: If the target method returns void (Java) or None (Python), you MUST place a line-level breakpoint on the line immediately after the assignment. Do NOT use a method-level breakpoint to observe a mutated field.

Do not rely on capture_return for void methods. This is a common false assumption: "Java passes objects by reference, so capture_return=true will show the mutated field at method exit." This is wrong. For void/None methods the SDK omits the return key from the snapshot entirely — there is no body.captures.return. The capture_arguments snapshot reflects entry state only, so a field assigned inside the method still shows its pre-call value (0, null, or default). Setting capture_return=true on a void method does not re-capture argument fields at exit.

What a method-level breakpoint on a void method actually gives you:

  • No body.captures.return key at all

  • The mutated field stuck at its pre-call value in body.captures.entry.arguments

The ONLY way to observe the post-mutation value is a line-level breakpoint on the line immediately after the assignment, capturing the mutated object as a local:

// Java example
void applyCouponDiscount(PricingContext ctx) {
    ctx.couponSavings = round(ctx.subtotal * couponRate);  // line 57
    ctx.orderAmount = ctx.orderAmount - ctx.couponSavings; // line 58  ← breakpoint here
}
// At line 58, ctx.couponSavings is already set — it appears in body.captures.lines.58.locals.ctx

Proof (real snapshot from a method-level breakpoint on a void Java method with capture_return=true). Note: there is NO return key, and couponSavings is 0.0 even though the method sets it — because the snapshot is entry-state only:

{
  "body": {
    "captures": {
      "entry": {
        "arguments": {
          "ctx": {
            "type": "com.amazon.sampleapp.PricingService$PricingContext",
            "fields": {
              "subtotal": { "type": "java.lang.Double", "value": "299.99" },
              "orderAmount": { "type": "java.lang.Double", "value": "299.99" },
              "couponSavings": { "type": "java.lang.Double", "value": "0.0" }
            }
          }
        }
      }
    }
  }
}

There is no body.captures.return. capture_return=true was set and still produced nothing at exit. This is why you must use a line-level breakpoint.

When to apply this pattern:

  • Method signature is void / returns None (this alone is enough — apply the rule)

  • The value you need is assigned inside the method, not passed in as an argument

  • Method-level breakpoint snapshot shows the field as 0, null, or its default value, and has no body.captures.return key

Troubleshooting Playbooks

Breakpoint in ERROR state

Check the ErrorCause field and act on it:

  • FILE_NOT_FOUND — the file path may not match the running application.

  • METHOD_NOT_FOUND — the function name may be incorrect or not loaded.

  • LINE_NOT_EXECUTABLE — the line may be a comment, blank, or declaration.

  • OVERLOADED_METHODS — ambiguous Java method; disambiguate by signature.

  • LANGUAGE_MISMATCH — the wrong language was specified.

  • RUNTIME_ERROR — other runtime failure.

Record the error and notify the user with the specific cause.

Breakpoint stays in READY (no traffic)

The breakpoint installed but received no traffic. Tell the user, and ask whether this code path is actually being executed and whether to wait longer or try a different location. If traffic is known to hit the function but it stays READY, re-check Python direct-import aliasing (instrument the importing module — see "Direct import aliasing" above).

Breakpoint in DISABLED state

max_hits was exceeded. See "max_hits and DISABLED" above — recover ACTIVE timestamps via di_instrumentation.py get-status with an earlier time range, then delete and recreate with a higher max_hits if more data is needed.

No snapshot data found
  1. Check your timestamp — try the 2nd or 3rd most recent ACTIVE event, not just the latest (older events have had more time to ingest).

  2. CloudWatch Logs has ingestion delay (typically 1–3 minutes); wait and retry.

  3. If still no data after waiting, notify the user.

Parallel Breakpoints

Usually a single, well-chosen breakpoint is enough. Set multiple breakpoints at once only when you genuinely don't know which of several functions is implicated — e.g. a latency chain with several branches (compare durations), or an intermittent value/cache bug where you need data from the same request across functions before the next problematic request arrives. If you already have a strong hypothesis about one function, start there and expand only if needed.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs a command-line tool, Needs Python, Needs Node.js

Security

What our scanner noticed· Low risk

  • Phrasing that asks for actions without confirmation
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

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AWS
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AWS (Official)
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  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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