Why does this skill emphasize bounded cardinality?

Using high-cardinality labels like User IDs in metrics can crash monitoring systems; this skill teaches you to use bounded labels or alternative strategies to keep monitoring costs low.

What are the Four Golden Signals tracked by this skill?

The skill helps you monitor Latency (time to service a request), Traffic (demand on the system), Errors (rate of failed requests), and Saturation (how 'full' your service is).

Does this work with popular Python frameworks like FastAPI?

Yes, the skill includes patterns for middleware implementation that can be used with FastAPI, Flask, and other ASGI/WSGI frameworks for automatic request tracking.

How does this skill improve production debugging?

It implements structured logging and correlation IDs, allowing developers to filter logs by specific requests across multiple services to find the root cause of issues without adding new code.

Python Observability Patterns

Name: Python Observability Patterns
Author: Microck

byMicrock

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Analytics & Monitoring

Instruments Python applications with structured logging, Prometheus metrics, and distributed tracing to enhance production reliability and system transparency.

This skill provides a standardized framework for implementing production-grade observability in Python applications. It guides developers through transitioning from basic logs to machine-readable JSON logging using structlog, tracking the 'Four Golden Signals' via Prometheus, and establishing end-to-end distributed tracing with OpenTelemetry. By focusing on correlation ID propagation and bounded metric cardinality, this skill ensures that complex distributed systems are easy to debug, monitor, and scale without incurring excessive telemetry costs.

Key Features

01Structured JSON logging configuration using structlog for machine-readable telemetry

022 GitHub stars

03Metric cardinality management to prevent storage cost explosions in Prometheus

04Implementation patterns for the Four Golden Signals: Latency, Traffic, Errors, and Saturation

05Distributed tracing setup with OpenTelemetry and OTLP integration

06Correlation ID propagation across service boundaries and asynchronous request chains

Use Cases

01Standardizing logging across microservices to enable powerful centralized log querying

02Tracing complex request lifecycles across distributed systems to identify bottlenecks and failures

03Implementing real-time performance monitoring and alerting for FastAPI or Flask backends

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/opendots-microck python-observability

For use in Claude.ai and ChatGPT

Download Skill