observability-telemetry

Enforce structured JSON logging, trace_id propagation, and metrics for backend services.

Updated Nov 26, 2025

Search Tags:#logging #metrics #observability #tracing #distributed-systems #error-handling

Ready to run

npx skills add https://github.com/guiledo/dotfiles --skill observability-telemetry-guiledo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: observability-telemetry
Source: https://github.com/guiledo/dotfiles/tree/main/opencode/.config/opencode/skills/observability-telemetry
Command: npx skills add https://github.com/guiledo/dotfiles --skill observability-telemetry-guiledo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Production-grade observability is often missing, leading to opaque failures and slow debugging. This skill standardizes logging, tracing, and metrics to transform incident response and performance tuning.

Core Features & Use Cases

Structured logging in JSON with mandatory fields (timestamp, level, context).
Context propagation via request_id/trace_id across services.
Actionable error reporting with operation, stack trace, and masked PII.
Clear separation between logs and metrics, with appropriate use of counters and histograms.
Guidance for setup workflows, middleware, and library choices.

Quick Start

Integrate structured JSON logging, propagate a request_id through your services, and emit metrics alongside logs to enable production observability.

Frequently Asked Questions about observability-telemetry

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement structured JSON logging with trace_id propagation in microservices?▼

Implement structured JSON logging with trace_id propagation by applying middleware that enforces mandatory fields like timestamp, level, and context. This ensures traceability and consistent error detail across distributed backend services.

What's the best way to separate logging and metrics for distributed systems observability?▼

The best way to separate logging and metrics is to maintain a dedicated metrics layer using counters and histograms alongside structured logs. This prevents metric data from bloating logs and ensures measurable performance tracking.

How does context propagation work for tracing requests across backend services?▼

Context propagation works by passing a consistent request_id or trace_id through service boundaries. This links distributed operations together, enabling end-to-end traceability for incident response and debugging.

Can I use this observability approach for error handling and masking PII in distributed systems?▼

Yes, you can use this approach for error handling in distributed systems. It enforces actionable error reporting that includes the operation name and stack trace while sanitizing details to mask PII.

Why do I need a logging library and middleware for production-grade observability?▼

You need a logging library and middleware for production-grade observability because they standardize the emission of structured logs and metrics. This transforms opaque failures into traceable, actionable incidents.