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Found 62 Skills
Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
Logging best practices for applications and services including structured logging, log levels, and log management strategies
Implements error handling patterns, structured logging, retry strategies, circuit breakers, and graceful degradation. Use when designing error handling, setting up logging, implementing retries, adding error tracking, or when asked about error boundaries, log aggregation, alerting, or resilience patterns.
Expert logging guidance based on Boris Tane's loggingsucks.com philosophy. Use when implementing logging, adding observability, debugging production issues, or reviewing code that includes log statements. Covers wide events architecture, structured logging, smart sampling, and high-cardinality field design.
Complete observability stack with structured logging, error tracking, and web analytics.
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.
Analyze application and system logs to identify errors, patterns, and root causes. Use log aggregation tools and structured logging for effective debugging.
Capture exceptions, add context, create performance spans, and use structured logging with Sentry.
Use when choosing a logging approach, configuring slog, writing structured log statements, or deciding log levels in Go. Also use when setting up production logging, adding request-scoped context to logs, or migrating from log to slog, even if the user doesn't explicitly mention logging. Does not cover error handling strategy (see go-error-handling).
Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.
Structured logging strategy including log levels, correlation IDs, context propagation, and PII avoidance. Use when designing logging, reviewing log statements, or setting up log aggregation.