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Found 228 Skills
Debug Django web applications with systematic diagnostic approaches. This skill covers troubleshooting Django-specific errors including TemplateDoesNotExist, ImproperlyConfigured, IntegrityError, migration conflicts, CSRF failures, N+1 query problems, and circular imports. Includes Django Debug Toolbar setup, ORM query logging, pdb/ipdb usage, shell_plus debugging, and comprehensive logging configuration. Provides four-phase methodology for root cause analysis and regression prevention.
Debug FastAPI applications systematically with this comprehensive troubleshooting skill. Covers async/await issues, Pydantic validation errors (422 responses), dependency injection failures, CORS configuration problems, database session management, and circular import resolution. Provides structured four-phase debugging methodology with FastAPI-specific tools including uvicorn logging, OpenAPI docs, and middleware debugging patterns.
Debug Express.js and Node.js applications with systematic diagnostic techniques. This skill provides comprehensive guidance for troubleshooting middleware execution issues, routing problems, CORS errors, async error handling, memory leaks, and unhandled promise rejections. Covers DEBUG environment variable usage, Node Inspector with Chrome DevTools, VS Code debugging, Morgan request logging, and diagnostic middleware patterns. Includes four-phase debugging methodology and common error message reference.
Build complete, production-ready Arduino projects (environmental monitors, robot controllers, IoT devices, automation systems). Assembles multi-component systems combining sensors, actuators, communication protocols, state machines, data logging, and power management. Supports Arduino UNO, ESP32, and Raspberry Pi Pico with board-specific optimizations. Use this skill when users request complete Arduino applications, not just code snippets.
Use when working on the backend API (packages/api). Covers Elysia routes, Drizzle ORM, TypeBox schemas, JWT authentication, S3 uploads, Google Sheets logging, and the Next.js hybrid setup.
Application monitoring and observability setup for Python/React projects. Use when configuring logging, metrics collection, health checks, alerting rules, or dashboard creation. Covers structured logging with structlog, Prometheus metrics for FastAPI, health check endpoints, alert threshold design, Grafana dashboard patterns, error tracking with Sentry, and uptime monitoring. Does NOT cover incident response procedures (use incident-response) or deployment (use deployment-pipeline).
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Checklists and anti-patterns for reviewing Go code. Covers API design, error handling, concurrency, interfaces, safety, performance, naming, testing, functional options, logging, and deterministic simulation testing.
Track which optimization experiment was best. Use when you've run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to production.
Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Covers DSPy evaluation for ongoing monitoring, prediction logging, drift detection, and alerting.
Idiomatic Go HTTP middleware patterns with context propagation, structured logging via slog, centralized error handling, and panic recovery. Use when writing middleware, adding request tracing, or implementing cross-cutting concerns.
This skill should be used when the user asks to "scan for PHI", "detect PII", "HIPAA compliance check", "audit for protected health information", "find sensitive healthcare data", "generate HIPAA audit report", "check code for PHI leakage", "scan logs for PHI", "check authentication on PHI endpoints", "scan FHIR resources", "check HL7 messages", or mentions PHI detection, HIPAA compliance, healthcare data privacy, medical record security, logging PHI violations, authentication checks for health data, or healthcare data formats (FHIR, HL7, CDA).