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Found 49 Skills
Observability patterns for metrics, logging, distributed tracing, and error tracking. Trigger: When setting up monitoring, when implementing logging, when adding error tracking, when configuring distributed tracing, when building health checks, when creating dashboards.
Pino high-performance JSON logger for Node.js with worker thread transports, child loggers, redaction, and framework integrations. Use when setting up structured logging, configuring log transports, adding request correlation IDs, redacting sensitive data, or integrating with Fastify, Hono, or Express. Use for pino, logging, structured-logs, request-id, correlation, redaction, transports, pino-http, pino-pretty.
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.
Analyze application and system logs to identify errors, patterns, and root causes. Use log aggregation tools and structured logging for effective debugging.
You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging,
Observability guidelines for distributed systems using OpenTelemetry, tracing, metrics, and structured logging
High-performance structured JSON logging for Node.js. Use when building production APIs that need fast, structured logs for observability platforms (Datadog, ELK, CloudWatch). Provides request logging middleware, child loggers for context, and sensitive data redaction. Choose Pino over console.log for any production TypeScript backend.
Python logging with loguru and platformdirs. TRIGGERS - loguru, structured logging, JSONL logs, log rotation, XDG directories.
Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging
xUnit 測試輸出與記錄完整指南。當需要在 xUnit 測試中實作測試輸出、診斷記錄或 ILogger 替代品時使用。涵蓋 ITestOutputHelper 注入、AbstractLogger 模式、結構化輸出設計。包含 XUnitLogger、CompositeLogger、效能測試診斷工具實作。 Keywords: ITestOutputHelper, ILogger testing, test output xunit, 測試輸出, 測試記錄, AbstractLogger, XUnitLogger, CompositeLogger, testOutputHelper.WriteLine, 測試診斷, logger mock, 測試日誌, 結構化輸出, Received().Log
Apply when making VTEX IO services easier to observe, troubleshoot, and operate in production. Covers metrics, structured logging, failure visibility, rate-limit awareness, and production readiness checks for backend apps. Use for integration monitoring, error diagnosis, or improving the operational quality of VTEX IO services before or after release.
Expert guide for analyzing application logs including log searching, pattern detection, error tracking, and debugging. Use when investigating issues, tracking errors, or understanding application behavior.