Results for “error-logging”

Production Fastify (TypeScript) patterns: schema validation, plugins, typed routes, error handling, security hardening, logging, testing with inject, and graceful shutdown

Managing dependencies, CI/CD optimization, Docker configuration, and operational observability (logging/alerting/health checks). Use for build errors or dev environment issues.

Audit a codebase for silent failures, swallowed exceptions, and observability gaps across Sentry and Langfuse, then produce a phased fix plan. Use when the user says "errors aren't showing in Sentry", "things fail silently", "empty catch blocks", "add error handling", "check my Langfuse tracing", or is hardening.

Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.

debug-logging

Provides patterns for robust error handling across TypeScript, Python, and Go, including typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.

跨 TypeScript、Python、Go 的健壮错误处理模式——涵盖类型化错误、错误边界、重试策略及优雅降级

Investigate a problem, log it to the debug changelog, cross-check past issues, and suggest a non-duplicate fix.

Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services.

Python logging with the standard library logging module and structlog. Covers log levels, handlers, formatters, structured logging, and production best practices for FastAPI/Django applications. USE WHEN: user mentions "python logging", "fastapi logging", "django logging", asks about "how to log in python", "python logging module", "logging configuration python" DO NOT USE FOR: Node.js logging - use `nodejs-logging` instead, Java logging - use `slf4j` or `logback`, structlog-specific - use `structlog` skill for deep dive

Use when investigating a bug, runtime error, unexpected behavior, test failure, or crash. For systematic root cause analysis, error message interpretation, stack trace reading, reproduction steps, and targeted fixes. Activate when the user says "it's broken", "this doesn't work", "I'm getting an error", or "help me debug".

Reads and analyzes application logs for debugging and monitoring.

Provides patterns for robust error handling across TypeScript, Python, and Go, covering typed errors, error boundaries, retries, circuit breakers, and user-facing messages.

Systematic debugging workflow for errors and bugs. Use when debugging errors, investigating bugs, troubleshooting issues, or when something isn't working as expected. Integrates Sentry MCP for production error context, Firecrawl for researching fix patterns, and Sequential Thinking for complex multi-step diagnosis.

Makes n8n workflow failures loud, structured, and recoverable by wiring per-node error outputs, retry logic, error-trigger workflows, and proper HTTP response shapes.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Audit a repo (or diff) for weak error handling — swallowed exceptions, bare catches, unhandled promise rejections, missing network timeouts/retries, and errors logged but not surfaced. Pure Claude Code, no MCP, no signup.

Provides a structured triage process for debugging test failures, build errors, runtime bugs, and production incidents, including steps to reproduce, localize, fix, and guard against recurrence.

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

**Server Logs:**

Provides a structured debugging workflow for diagnosing errors, test failures, and unexpected behavior, including root cause analysis and verification steps.

Use when the team needs a strategy for validation errors, API failures, job retries, user-facing error UX, observability, or recovery behavior. Trigger on requests to design error handling across frontend, backend, and integrations.

Logs the outcome of a skill execution to observations/<skill-name>/runs.md. Trigger on: "log this run", "skill worked", "skill failed", "this didn't work", "log the outcome", "record this", "note that", or after any skill completes with a clear success, partial, or failure outcome. Creates the observations file if it does not exist, then appends an entry with date, task description, skill used, outcome, what worked, what failed, and any error messages observed.

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Routes runtime-log requests into an evidence packet to isolate the first actionable blocker, repeated signature, blast radius, or safest next read-only check.

Runbook skill for failures: cron error, PA failure, cascade, gateway disconnect, semantic DB stale. Walks: detect → classify → diagnose → notify → log. Replaces ad-hoc failure handling. Triggers: "cron failed", "X is broken", "cascade", "incident", "gateway down", "PA failure".

Diagnose the current OMC session or repo state using logs, traces, state, and focused reproduction

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.