A comprehensive technical framework for implementing structured logging, distributed tracing, and metrics collection to ensure system reliability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install logging-observability
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install logging-observability using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Logging and Observability Skill?
The logging and observability skill provides a robust blueprint for building observable systems based on the three pillars: logs, metrics, and traces. It moves beyond simple text logs into structured JSON logging and vendor-neutral telemetry collection. By integrating this skill into your workflow, you can ensure that every request is traceable across microservices and every failure is diagnostic through high-context metadata.
Using Openclaw Skills for observability allows developers to implement standardized patterns like the RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) methods. This approach ensures that performance bottlenecks are identified quickly and that monitoring stacks like Prometheus, Grafana, and Jaeger work seamlessly together to provide a unified view of system health.
Logging and Observability Skill Use Cases
- Implementing structured JSON logging to replace fragile string-based logs.
- Setting up distributed tracing with OpenTelemetry to visualize request flows across microservices.
- Designing service-level dashboards using RED metrics for consistent performance monitoring.
- Creating actionable alerting strategies that reduce on-call fatigue while maintaining high availability.
- Establishing PII and secret scrubbing protocols to prevent sensitive data leaks in telemetry.
How Logging and Observability Skill Works
- Initialize structured logging using high-performance libraries like Pino or Zap to output JSON by default.
- Configure OpenTelemetry SDKs to instrument application code and capture distributed traces.
- Embed trace_id and span_id into every log line to enable correlation between logs and traces.
- Define and expose RED metrics for every external API endpoint and USE metrics for underlying infrastructure.
- Set up context propagation across HTTP headers and message queues to maintain trace continuity.
- Deploy a monitoring stack—such as Prometheus, Grafana, and Loki—to aggregate and visualize the collected data.
Logging and Observability Skill Setup
To begin using this skill for Openclaw Skills, install the core OpenTelemetry and logging dependencies:
npm install @opentelemetry/sdk-node @opentelemetry/auto-instrumentations-node pino
Initialize the NodeSDK to start collecting traces:
import { NodeSDK } from '@opentelemetry/sdk-node';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
const sdk = new NodeSDK({
serviceName: 'my-service',
instrumentations: [getNodeAutoInstrumentations()],
});
sdk.start();
Logging and Observability Skill Data Schema & Taxonomy
The skill enforces a strict data schema for logs to ensure they are machine-readable and searchable within the Openclaw Skills ecosystem:
| Field | Type | Description |
|---|---|---|
timestamp |
String | ISO-8601 format with millisecond precision |
level |
String | Severity level (INFO, WARN, ERROR, etc.) |
service |
String | Name of the originating service |
trace_id |
String | 32-character hex string for trace correlation |
message |
String | Human-readable log description |
context |
Object | Key-value pairs of request-specific metadata |
Logging and Observability Skill Advanced Features
- Middleware-level context enrichment for automatic metadata inheritance in downstream logs.
- Tail-based sampling strategies to ensure 100% of error traces are captured even in high-traffic environments.
- Multi-burn-rate alert design to detect both rapid service failures and slow performance regressions.
- Automated runbook linking for every alert severity to ensure actionable incident response.
- Runtime log-level configuration to debug production issues without requiring service redeployment.
SKILL.md
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