Structured logging, distributed tracing, and metrics collection patterns for building observable systems. Use when implementing logging infrastructure, setting up distributed tracing with OpenTelemetry, designing metrics collection (RED/USE methods), configuring alerting and dashboards, or reviewing observability practices. Covers structured JSON logging, context propagation, trace sampling, Prometheus/Grafana stack, alert design, and PII/secret scrubbing.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install logging-observabilityOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install logging-observabilityβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/logging-observability/π‘Extract and place the folder at the path above, then restart your agent.
Category
Developer & DevOpsWhat Logging Observability can do for your AI workflow
Structured logging, directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 1,699+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Logging Observability
Help me get started with Logging Observability
Explains what Logging Observability does, walks through the setup, and runs a quick demo based on your current project
Use Logging Observability to structured logging, distributed tracing, and metrics collection pat...
Invokes Logging Observability with the right parameters and returns the result directly in the conversation
What can I do with Logging Observability in my developer & devops workflow?
Lists the top use cases for Logging Observability, with example commands for each scenario
Guides & tutorials for AI skills
Logging Observability extends your AI assistant with the ability to structured logging, distributed tracing, and metrics collection patterns for building observable systems. Use when implementing logging infrastructure, setting up distributed tracing with OpenTelemetry, designing metrics collection (RED/USE methods), configuring alerting and dashboards, or reviewing observability practices. Covers structured JSON logging, context propagation, trace sampling, Prometheus/Grafana stack, alert design, and PII/secret scrubbing. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Logging Observability as its underlying capability.
Logging Observability works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Logging Observability once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
To install Logging Observability, copy the skill folder to `~/.claude/skills/logging-observability/` for use across all your projects, or `.claude/skills/logging-observability/` for a single project. Restart Claude and the skill is immediately active β invoke it with `/logging-observability` or just describe your goal and the AI will pick it up automatically.
Logging Observability has been installed 1,699 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/logging-observability/ for personal use (all projects), or .claude/skills/logging-observability/ for project-specific use. Restart your AI client, then invoke with /logging-observability or let the AI discover it automatically.
Logging Observability supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Logging Observability is free to install. Check the repository for licensing information.
Structured logging, distributed tracing, and metrics collection patterns for building observable systems. Use when implementing logging infrastructure, setting up distributed tracing with OpenTelemetry, designing metrics collection (RED/USE methods), configuring alerting and dashboards, or reviewing observability practices. Covers structured JSON logging, context propagation, trace sampling, Prometheus/Grafana stack, alert design, and PII/secret scrubbing.
Automate my developer & devops tasks using Logging Observability
Identifies repetitive steps in your workflow and sets up Logging Observability to handle them automatically
MCP vs traditional plugins: what's the difference?
Logging Observability is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.