log-designing

What problem does it solve?

This Skill helps teams and developers design logging that is both machine-readable and actionable, reducing time-to-diagnosis and enabling automated incident triage by prescribing format, content, and CLI conventions.

Core Features & Use Cases

• Seven design principles: structured-first JSON output, semantic error codes, complete reproduction data, correlation IDs, stdout/stderr separation, staged verbosity, and signal-to-noise optimization.
• CLI patterns & verbosity: clear rules for when to write to stdout vs stderr, standard -v/-vv/-vvv/-q mappings, and environment variable fallbacks like LOG_LEVEL or RUST_LOG.
• Error code design & required fields: category-based E4xx/E5xx/E6xx template and mandatory error log fields (error_code, message, input, expected/actual, stack_trace, context, correlation_id, suggestions).
• Libraries & rollout: recommended libraries per language (Python structlog, Node Pino, Go slog, Rust tracing) and a phased adoption flow from quick JSON adoption to OpenTelemetry integration.

Quick Start

Generate a concise logging design checklist that enforces JSON structured logs, semantic error codes with E4xx–E6xx categories, correlation IDs, stdout/stderr separation, and staged verbosity flags.