How does this skill handle sensitive information?

It includes built-in patterns for automatic redaction of sensitive fields like passwords, tokens, and API keys in both logs and trace attributes.

What libraries are required for this skill?

The skill is designed to work with Pino for structured logging and autotel for OpenTelemetry integration.

Does adding observability clutter my business logic?

No, it follows the Observer pattern using wrappers, ensuring that your core business logic remains 'blind' to the telemetry implementation.

Which monitoring backends are supported?

Since it uses OpenTelemetry standards, it is compatible with Jaeger, Honeycomb, Grafana Tempo, and any other OTel-compliant backend.

What are canonical log lines?

They are 'wide events' that emit a single log per request containing all accumulated context, making it significantly easier to query system behavior.

Observability & Tracing

Name: Observability & Tracing
Author: jagreehal

byjagreehal

Analytics & Monitoring

Integrates structured logging, OpenTelemetry tracing, and semantic conventions into functions for deep system visibility.

About

This skill empowers Claude to implement production-grade observability patterns using the Observer pattern, keeping telemetry separate from business logic. It leverages Pino for high-performance structured logging and autotel for OpenTelemetry-compliant tracing. By enforcing semantic conventions, correlation between logs and traces, and automated redaction of sensitive data, it ensures your services are debuggable, SOC2-compliant, and optimized for modern monitoring backends like Honeycomb and Jaeger.

Key Features

Enforcement of OTel semantic conventions for standardized telemetry
High-performance structured logging using Pino JSON formats
Implementation of canonical log lines (wide events) for easier querying
0 GitHub stars
Automatic OpenTelemetry span creation via the trace() wrapper
SOC2-compliant sensitive data redaction for logs and attributes

Use Cases

Debugging complex request flows by correlating logs with specific trace IDs
Instrumenting backend microservices for distributed tracing and performance monitoring
Standardizing telemetry data across a codebase to support automated dashboards

About

Key Features

Enforcement of OTel semantic conventions for standardized telemetry
High-performance structured logging using Pino JSON formats
Implementation of canonical log lines (wide events) for easier querying
0 GitHub stars
Automatic OpenTelemetry span creation via the trace() wrapper
SOC2-compliant sensitive data redaction for logs and attributes

Use Cases

Debugging complex request flows by correlating logs with specific trace IDs
Instrumenting backend microservices for distributed tracing and performance monitoring
Standardizing telemetry data across a codebase to support automated dashboards