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

0•

Analíticas y Monitorización

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

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.

Características Principales

01Enforcement of OTel semantic conventions for standardized telemetry

02High-performance structured logging using Pino JSON formats

03Implementation of canonical log lines (wide events) for easier querying

040 GitHub stars

05Automatic OpenTelemetry span creation via the trace() wrapper

06SOC2-compliant sensitive data redaction for logs and attributes

Casos de Uso

01Debugging complex request flows by correlating logs with specific trace IDs

02Instrumenting backend microservices for distributed tracing and performance monitoring

03Standardizing telemetry data across a codebase to support automated dashboards

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jagreehal/jagreehal-claude-skills observability

For use in Claude.ai and ChatGPT

Características Principales

01Enforcement of OTel semantic conventions for standardized telemetry

02High-performance structured logging using Pino JSON formats

03Implementation of canonical log lines (wide events) for easier querying

040 GitHub stars

05Automatic OpenTelemetry span creation via the trace() wrapper

06SOC2-compliant sensitive data redaction for logs and attributes

Casos de Uso

01Debugging complex request flows by correlating logs with specific trace IDs

02Instrumenting backend microservices for distributed tracing and performance monitoring

03Standardizing telemetry data across a codebase to support automated dashboards

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jagreehal/jagreehal-claude-skills observability

For use in Claude.ai and ChatGPT