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Found 49 Skills
Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code.
Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See golang-benchmark and golang-performance skills).
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Review code for logging patterns and suggest evlog adoption. Detects console.log spam, unstructured errors, and missing context. Guides wide event design, structured error handling, request-scoped logging, and log draining with adapters (Axiom, OTLP).
Instrumenting Go applications with OpenTelemetry for distributed tracing, Prometheus for metrics, and structured logging with slog
Setup Sentry Logging in any project. Use when asked to add Sentry logs, enable structured logging, capture console logs, or integrate logging libraries (Pino, Winston, Loguru) with Sentry. Supports JavaScript, Python, and Ruby.
Configure structured logging with Pino. Outputs human-readable colorized logs in development and structured JSON in production for log aggregation services.
Logging best practices focused on wide events (canonical log lines) for powerful debugging and analytics
Use when building or reviewing service, job, or CLI runtime behavior in Python — designing startup validation, shutdown sequences, observability, and structured logging. Also use when startup crashes from late config, shutdown leaves orphaned processes, terminal states are implicit, or logs lack structure.
Set up Apollo.io monitoring and observability. Use when implementing logging, metrics, tracing, and alerting for Apollo integrations. Trigger with phrases like "apollo monitoring", "apollo metrics", "apollo observability", "apollo logging", "apollo alerts".
Complete observability stack with structured logging, error tracking, and web analytics.
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".