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Found 776 Skills
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
Configure Sentry for error tracking, performance monitoring, and log aggregation. Integrates with Pino to forward logs to Sentry automatically.
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
AWS RDS (Relational Database Service) management using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.
Setup Sentry in React Native using the wizard CLI. Use when asked to add Sentry to React Native, install @sentry/react-native, or configure error monitoring for React Native or Expo apps.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Migration monitoring, CDC, and observability infrastructure
This skill should be used when the user asks to "fetch Sentry issues", "check Sentry errors", "triage Sentry", "categorize Sentry issues", "resolve Sentry issue", "mute Sentry issue", "unresolve Sentry issue", "sentry-cli", or mentions Sentry API, Sentry project issues, error monitoring, issue triage, Sentry stack traces, or browser extension errors in Sentry.
Git-centric implementation workflow. Enforces clean checkout, creates a properly named branch, tracks progress in a WIP markdown file, and commits/pushes continuously so remote git logs serve as the primary monitoring channel. Use when starting any plan-based implementation task.
Implement request logging, tracing, and observability. Use for debugging, monitoring, and production observability.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.