> browse/registry

> sentry

Assists with monitoring application errors, performance, and user experience using Sentry. Use when integrating Sentry SDKs, configuring alerting, analyzing stack traces, uploading source maps, or tracking release health in production. Trigger words: sentry, error monitoring, error tracking, performance monitoring, source maps, session replay.

> braintrust

You are an expert in Braintrust, the evaluation and observability platform for AI applications. You help developers run systematic evaluations, compare model versions, track experiments, log production traces, and measure quality metrics — with a focus on making AI development as rigorous as traditional software testing.

> airflow

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Learn to write DAGs, use operators, set up connections, configure scheduling, and deploy with Docker Compose.

> envoy

You are an expert in Envoy, the high-performance C++ proxy designed for cloud-native applications. You help teams configure Envoy as an API gateway, service mesh sidecar, and load balancer using its L4/L7 routing, circuit breaking, rate limiting, TLS termination, gRPC support, and observability features — powering Istio, AWS App Mesh, and most modern service mesh implementations.

> grafana

Grafana is an open-source visualization and dashboarding platform that connects to dozens of data sources including Prometheus, PostgreSQL, ClickHouse, and Elasticsearch. It lets you build interactive dashboards with panels, set up alerting rules, and manage everything as code through JSON dashboard definitions and provisioning configuration.

> datadog

Configure and manage Datadog for infrastructure monitoring, application performance monitoring (APM), log management, and alerting. Use when a user needs to set up Datadog agents, create dashboards, configure monitors and alerts, integrate services, or query metrics and logs through Datadog's API.

> arize

You are an expert in Arize and its open-source Phoenix library for AI observability. You help developers monitor LLM applications with tracing, evaluation, embedding analysis, drift detection, and retrieval quality metrics — using Phoenix for local development (open-source, self-hosted) and Arize platform for production monitoring at scale.

> checkly

Expert guidance for Checkly, the synthetic monitoring platform that runs Playwright-based browser checks and API checks from locations worldwide. Helps developers implement monitoring-as-code (MaC) with the Checkly CLI, set up API and browser checks, configure alerting, and integrate monitoring into CI/CD pipelines.

> agent-sandbox

Run AI agent code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.

> helicone

LLM observability proxy that sits between your app and LLM providers. Logs every request, enables caching, rate limiting, and provides cost analytics. Works with OpenAI, Anthropic, and other providers with a one-line integration change.

> betterstack

Expert guidance for Better Stack (formerly Better Uptime + Logtail), the observability platform combining uptime monitoring, log management, incident response, and status pages. Helps developers set up comprehensive monitoring with alerting, on-call schedules, and public status pages.

> railway-deploy

Manage Railway deployments using the CLI. Use when a user asks to deploy to Railway, check deployment status, manage Railway services, set environment variables on Railway, view Railway logs, link a Railway project, add a database on Railway, scale Railway services, manage Railway environments, rollback a Railway deployment, or run commands with Railway env vars. Covers the full deploy lifecycle from project setup to production monitoring.

> spring-boot

Spring Boot is a Java framework that simplifies building production-ready applications. It provides auto-configuration, embedded servers, and opinionated defaults for REST APIs, data access with JPA, security, and monitoring via Actuator.

> azure-mgmt-applicationinsights-dotnet

Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management. Use for creating Application Insights components, web tests, workbooks, analytics items, and API keys. Triggers: "Application Insights", "ApplicationInsights", "App Insights", "APM", "application monitoring", "web tests", "availability tests", "workbooks".

> azure-monitor-opentelemetry-exporter-java

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Triggers: "AzureMonitorExporter java", "opentelemetry azure java", "application insights java otel", "azure monitor tracing java". Note: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.

> implementing-database-audit-logging

This skill helps implement database audit logging for tracking changes and ensuring compliance. It is triggered when the user requests to "implement database audit logging", "add audit trails", "track database changes", or mentions "audit_log" in relation to a database. The skill provides options for trigger-based auditing, application-level logging, Change Data Capture (CDC), and parsing database logs. It generates a basic audit table schema and guides the user through selecting the appropriate

> implementing-real-user-monitoring

This skill assists in implementing Real User Monitoring (RUM) to capture and analyze actual user performance data. It helps set up tracking for key metrics like Core Web Vitals, page load times, and custom performance events. Use this skill when the user asks to "setup RUM", "implement real user monitoring", "track user experience", or needs assistance with "performance monitoring". It guides the user through choosing a RUM platform, designing an instrumentation strategy, and implementing the ne

> nestjs-observability

Configure structured logging with Pino, Prometheus metrics, and health checks for NestJS services. Use when adding JSON logging, request tracing with correlation IDs, Prometheus metric endpoints, or liveness/readiness health checks. (triggers: main.ts, **/*.module.ts, nestjs-pino, Prometheus, Logger, reqId)

> common-observability

Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware. (triggers: **/*.service.ts, **/*.handler.ts, **/*.middleware.ts, **/*.interceptor.ts, **/*.go, **/*.java, **/*.kt, **/*.py, logging, tracing, metrics, opentelemetry, observability, slo)

> monitoring-database-transactions

This skill enables Claude to monitor database transactions for performance and lock issues using the database-transaction-monitor plugin. It is triggered when the user requests transaction monitoring, lock detection, or rollback rate analysis for databases. Use this skill when the user mentions "monitor database transactions," "detect long-running transactions," "identify lock contention," "track rollback rates," or asks about "transaction anomalies." The skill leverages the `/txn-monitor` comma