How does it calculate the observability score?

It uses a penalty-based algorithm where high-severity issues, like missing health checks, carry higher penalties than low-severity issues like missing debug logs.

Can this skill be integrated into a CI/CD pipeline?

Yes, because it outputs findings in a structured JSON format, it is ideal for use within automated quality gates and delivery workflows.

Which logging libraries does it recognize?

The auditor identifies common structured loggers such as Winston, Pino, Logrus, and Zap, while flagging unstructured console.log calls.

What observability patterns does this skill check?

It checks for structured logging, health check endpoints, metrics collection (Prometheus/StatsD), request tracing, and appropriate log level usage.

Observability Auditor

Name: Observability Auditor
Author: levnikolaevich

bylevnikolaevich

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分析与监控

Audits codebases for production-readiness by evaluating logging, health checks, metrics collection, and request tracing patterns.

关于

The Observability Auditor is a specialized L3 worker skill designed to ensure your application is fully prepared for production monitoring and troubleshooting. It scans your codebase for critical observability patterns, including structured logging, health check endpoints (/health, /ready), metrics instrumentation for tools like Prometheus, and distributed tracing via correlation IDs. By analyzing your tech stack and implementation, it generates a comprehensive JSON report featuring a compliance score, severity-ranked findings, and actionable recommendations with estimated effort, making it an essential tool for SREs and DevOps-focused developers.

主要功能

Automated detection of structured logging patterns and improper console.log usage
Quantified observability scoring with severity-based findings and effort estimates
Verification of metrics collection instrumentation (Prometheus, StatsD, CloudWatch)
Validation of health, readiness, and liveness endpoints for container orchestration
38 GitHub stars
Auditing of distributed tracing and request correlation ID propagation

使用场景

Automating code reviews to enforce standardized logging and instrumentation practices
Assessing technical debt in legacy projects to identify monitoring blind spots
Performing pre-production readiness checks to ensure services are observable before deployment

关于

主要功能

Automated detection of structured logging patterns and improper console.log usage
Quantified observability scoring with severity-based findings and effort estimates
Verification of metrics collection instrumentation (Prometheus, StatsD, CloudWatch)
Validation of health, readiness, and liveness endpoints for container orchestration
38 GitHub stars
Auditing of distributed tracing and request correlation ID propagation

使用场景

Automating code reviews to enforce standardized logging and instrumentation practices
Assessing technical debt in legacy projects to identify monitoring blind spots
Performing pre-production readiness checks to ensure services are observable before deployment