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splunk-observability-k8s-auto-instrumentation-setup

Render and manage Splunk/OpenTelemetry Kubernetes auto-instrumentation overlays for Java, Node.js, Python, .NET, Go, Apache, Nginx, and SDK workloads.

36|7|Updated Mar 17, 2026
Search Tags:#automation#kubernetes#instrumentation#gitops#opentelemetry#render#splunk
One-click install
npx skills add https://github.com/chambear2809/splunk-cisco-skills --skill splunk-observability-k8s-auto-instrumentation-setup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: splunk-observability-k8s-auto-instrumentation-setup
Source: https://github.com/chambear2809/splunk-cisco-skills/tree/main/skills/splunk-observability-k8s-auto-instrumentation-setup
Command: npx skills add https://github.com/chambear2809/splunk-cisco-skills --skill splunk-observability-k8s-auto-instrumentation-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Render, apply, verify, and uninstall Splunk/OpenTelemetry Kubernetes auto-instrumentation overlays for workloads after the base Splunk Observability Cloud collector/operator is installed.

Core Features & Use Cases

  • Generates Instrumentation CRs and workload/namespace annotations to enable zero-code instrumentation across Java, Node.js, Python, .NET, Go, Apache HTTPD, Nginx, and SDK workloads.
  • Creates backup ConfigMaps, OpenTelemetry Operator integration, and optional OpenTelemetry Blackbox Instrumentation (OBI) assets, plus preflight validation and GitOps-ready manifests.
  • Supports uninstall, patch rollback, and environment-specific customization for deployment scenarios, including GitOps workflows.

Quick Start

Render the overlay assets and review the generated artifacts, then run the rendered helper scripts to apply or validate in your cluster.

Frequently Asked Questions about splunk-observability-k8s-auto-instrumentation-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up Splunk OpenTelemetry auto-instrumentation for Kubernetes workloads?▼

Splunk OpenTelemetry auto-instrumentation for Kubernetes is set up by rendering and applying overlays that generate Instrumentation CRs and workload annotations, enabling zero-code instrumentation across Java, Python, Node.js, .NET, Go, Apache, and Nginx.

Can I manage Kubernetes instrumentation overlays through a GitOps workflow?▼

Yes, Kubernetes instrumentation overlays can be managed through a GitOps workflow because the rendering process produces GitOps-ready YAML manifests, allowing you to version and apply configurations declaratively across environments.

Do I need the Splunk Observability collector installed before applying auto-instrumentation?▼

Yes, the base Splunk Observability Cloud collector and operator must be installed first, as the auto-instrumentation overlays are applied afterward to inject and manage tracing across your existing workloads.

What is the best way to uninstall or roll back Kubernetes auto-instrumentation patches?▼

The best way to uninstall or roll back Kubernetes auto-instrumentation patches is by using the included uninstall scripts and backup ConfigMaps, which safely revert workload annotations and remove the applied Instrumentation CRs.

Does this Kubernetes instrumentation approach support OpenTelemetry Blackbox Instrumentation?▼

Yes, this approach supports OpenTelemetry Blackbox Instrumentation (OBI) natively, allowing you to optionally generate and apply OBI assets alongside the standard workload instrumentation Custom Resources.

How do preflight checks validate Kubernetes instrumentation before deployment?▼

Preflight checks validate Kubernetes instrumentation by verifying environment readiness and configuration safety before deployment, ensuring the base operator is present and preventing invalid overlay application.

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