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Found 350 Skills
Expert in error handling patterns, exception management, error responses, logging, and error recovery strategies for React, Next.js, and NestJS applications
Set up monitoring, logging, and alerting for infrastructure and applications. Use when implementing observability, creating dashboards, or configuring alerts.
Python logging with loguru and platformdirs. TRIGGERS - loguru, structured logging, JSONL logs, log rotation, XDG directories.
Error handling patterns for ERPNext Document Controllers. Use when implementing try/except, validation errors, permission errors, and transaction management. Covers rollback patterns, error logging, and user feedback. V14/V15/V16 compatible. Triggers: controller error, try except catch, ValidationError, PermissionError, rollback, error handling.
System architecture guidance for Python/React full-stack projects. Use during the design phase when making architectural decisions — component boundaries, service layer design, data flow patterns, database schema planning, and technology trade-off analysis. Covers FastAPI layer architecture (Routes/Services/Repositories/Models), React component hierarchy, state management, and cross-cutting concerns (auth, errors, logging). Produces architecture documents and ADRs. Does NOT cover implementation (use python-backend-expert or react-frontend-expert) or API contract design (use api-design-patterns).
Tools for reading and analyzing Arduino serial monitor output for enhanced debugging. Provides real-time monitoring, data logging, filtering, and pattern matching to help troubleshoot Arduino sketches using arduino-cli or Arduino IDE.
Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging
Use the @contextvm/sdk TypeScript SDK effectively. Reference for core interfaces, signers, relay handlers, transports, encryption, logging, and SDK patterns. Use when implementing SDK components, extending interfaces, configuring transports, or debugging SDK usage.
Explain how to do logging
Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.
Use when adding logging to services, setting up monitoring, creating alerts, debugging production issues, designing SLIs/SLOs, or implementing structured logging (Pino, Winston), metrics (Prometheus, DataDog, CloudWatch), or distributed tracing (OpenTelemetry).
Designs and outputs n8n workflow JSON with robust triggers, idempotency, error handling, logging, retries, and human-in-the-loop review queues. Use when you need an auditable automation that won’t silently fail.