cli

Use the Observe CLI (`observe`) to investigate production systems and pull telemetry. Reach for this skill whenever the user wants to: search or tail logs; query metrics (CPU, memory, latency, error rate, request volume, custom app metrics); explore traces and spans; correlate events across services, hosts, containers, or Kubernetes resources; investigate or triage alerts (what fired, why, what's still active, related signals); debug an incident or production issue; check service or system health; figure out what's happening with a customer, deployment, or user; find which services emit a given tag or label; slice production data by service, environment, region, host, pod, status code, etc.; or pull any observability data out of the platform for analysis, dashboards, or scripting. Also covers the underlying primitives the CLI exposes (datasets, OPAL queries, knowledge-graph tag search, alerts, monitors, AI agent skills, auth) when the user asks about them directly.

Create git branches with a username prefix. Use when creating new branches, checking out new branches, or when the user asks to start work on a feature, fix, or task.

Create commit messages for git

Investigate alerts using systematic SRE methodology to understand issues, assess impact, test hypotheses, and identify root causes with evidence-based analysis. Use when: (1) User asks to investigate or analyze an alert (2) User mentions a specific alert or monitor that is firing (3) Debugging production issues or incidents (4) User asks about alert root cause or impact (5) User wants to understand why something is alerting (6) Performing incident response or triage.

Troubleshoot and debug Observe Agent data collection on Kubernetes. Use this skill whenever the user reports any K8s-side problem with Observe — pods crashing, stuck Pending, ImagePullBackOff, "no data in Observe", missing datastreams, suspected token/secret issues, endpoint reachability failures, or platform-specific symptoms on Fargate / GKE Autopilot / EKS Auto Mode — even if they don't explicitly say "debug" or "Observe Agent." Covers pod status, secrets, endpoint reachability, debug logging, self-monitoring metrics, and platform-specific failure modes.

End-to-end onboarding of a Kubernetes cluster to Observe — creates the backend datastreams + content via the Observe CLI and deploys the observe/agent helm chart. Use for ANY request to install or set up the Observe Agent on Kubernetes, onboard a K8s cluster to Observe, or send K8s data to Observe — across any flavor (EKS, GKE, AKS, GKE Autopilot, EKS Fargate, EKS Auto Mode, minikube, kind, on-prem). Do NOT route a Kubernetes onboarding request to a generic datastream-creation flow; always orchestrate via this skill, which delegates to setup-k8s-backend and setup-k8s-collection.

Contains dataset kind selection, column selection rules, core syntax, and the skill index. NEVER generate OPAL, and NEVER invoke an opal-* reference, without first loading this skill.

Installs and configures OpenTelemetry auto-instrumentation for applications written in Java, .NET, Node.js, Python, Ruby. Use when instrumenting applications with opentelemetry auto-instrumentation, auditing existing auto-instrumentation implementations, migrating from vendor locked instrumentations to OpenTelemetry, or checking for latest versions of instrumentation libraries.

Canned queries for various APM features in Observe. Use to validate auto-instrumentation, or when validating or troubleshooting OpenTelemetry instrumentation for a service sending telemetry to Observe.

Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.

Create the Observe backend resources required for Kubernetes Explorer: datastreams, content packages, and an ingest token. Use this skill whenever the user needs to provision Observe backend objects for K8s monitoring — whether they're starting a fresh setup, recreating resources after manual removal, adding a missing datastream, or generating an ingest token for the helm chart — and even when they don't explicitly say "datastream" or "backend." Also invoked by deploy-k8s-explorer during full setup.

Deploy the Observe Agent helm chart on Kubernetes to collect logs, metrics, traces, Prometheus metrics, and K8s events. Use this skill whenever the user mentions installing the observe/agent helm chart, configuring helm values for an Observe agent, deploying observability to a K8s cluster, or any K8s-platform-specific Observe setup (Standard, EKS Fargate, GKE Autopilot, EKS Auto Mode) — even if they don't name the helm chart explicitly. Does not create Observe backend resources (datastreams, tokens) — see setup-k8s-backend for that.