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Found 7 Skills
Monitoring guidelines for applications and infrastructure including metrics collection, alerting strategies, and SLO-based monitoring
Implement structured logging across applications with log aggregation and centralized analysis. Use when setting up application logging, implementing ELK stack, or analyzing application behavior.
Profile and optimize application memory usage. Identify memory leaks, reduce memory footprint, and improve efficiency for better performance and reliability.
Inspector integration. Manage data, records, and automate workflows. Use when the user wants to interact with Inspector data.
Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
Test application performance and load handling
Error tracking and monitoring integration. Sentry, Datadog RUM, Bugsnag. Source maps, breadcrumbs, release tracking, performance monitoring, and alerting configuration. USE WHEN: user mentions "Sentry", "error tracking", "Bugsnag", "Datadog RUM", "crash reporting", "source maps", "release tracking", "error monitoring" DO NOT USE FOR: application logging - use logging skills; APM/tracing - use `opentelemetry`; structured error responses - use `error-handling`