logging-observability
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.
Why use this skill?
Master observability with structured logging, distributed tracing, and metrics. Learn to implement OTel, Prometheus, and alerts.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/wpank/logging-observabilityWhat This Skill Does
The logging-observability skill acts as a comprehensive framework for instrumenting, monitoring, and debugging distributed software systems. It enforces the implementation of the "Three Pillars of Observability": structured logs, high-cardinality metrics, and distributed tracing. By providing standardized patterns for log schemas, OpenTelemetry integration, and performance monitoring strategies (such as the RED and USE methods), this skill enables developers to build self-diagnosing, resilient architectures. It goes beyond simple log collection to ensure that data is actionable, PII-scrubbed, and cross-referenced with unique trace identifiers for full request lifecycle visibility.
Installation
To integrate this skill into your environment, run the following command in your terminal:
clawhub install openclaw/skills/skills/wpank/logging-observability
Ensure your project has the necessary instrumentation libraries compatible with your stack (e.g., Pino for Node.js, structlog for Python, or zap for Go) before invoking the agent to configure your logging pipelines.
Use Cases
This skill is ideal for teams transitioning from fragmented, free-text logging to structured observability. Use it to:
- Define and enforce JSON log schemas across microservices.
- Configure OpenTelemetry collectors to aggregate distributed traces across service boundaries.
- Design Prometheus/Grafana dashboards using the RED (Rate, Errors, Duration) method for API health.
- Perform security audits on logs to ensure PII and sensitive credentials are properly masked/scrubbed.
- Optimize production debugging by correlating logs with span and trace IDs.
Example Prompts
- "Analyze my current Node.js logging setup and help me migrate from text logs to structured Pino logs with automatic trace ID correlation."
- "Draft a Prometheus alerting strategy for our microservices based on the RED method, specifically alerting on latency spikes over 500ms."
- "Help me create a middleware pattern in Go that scrubs PII from user request payloads before they are recorded in our log stream."
Tips & Limitations
- Always prioritize structured data: Avoid free-text strings as they cannot be efficiently indexed in aggregators like ELK or Datadog.
- Correlation is key: The utility of logs drops significantly without trace IDs. Ensure your context propagation headers are correctly passed between services.
- PII Scrubbing: The skill includes patterns for scrubbing, but you must ensure your specific data keys are added to the exclusion list.
- Performance: While structured logging is efficient, avoid logging high-volume request bodies at the INFO level in production to prevent disk I/O bottlenecks.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-wpank-logging-observability": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
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