> browse/registry

> incident-response-smart-fix

[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res

> datadog-cli

Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.

> mlops-engineer

Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

> ml-engineer

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

> amazon-connect-custom-ccp

Design, build, and refine a custom Contact Control Panel (CCP) and full Amazon Connect contact‑center configuration. This skill helps create tailored web‑based CCP UIs, integrate them with Amazon Connect Streams, and model security profiles, routing profiles, queues, campaigns, contact flows, IVR/bots, monitoring (barge‑in/whisper), and metrics so the overall experience matches business needs.

> setting-up-synthetic-monitoring

This skill automates the setup of synthetic monitoring for applications. It allows Claude to proactively track performance and availability by configuring uptime, transaction, and API monitoring. Use this skill when the user requests to "set up synthetic monitoring", "configure uptime monitoring", "track application performance", or needs help with "proactive performance tracking". The skill helps to identify critical endpoints and user journeys, design monitoring scenarios, and configure alerts

> golang-logging

Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services. (triggers: go.mod, pkg/logger/**, logging, slog, structured logging, zap)

> risk-metrics-calculation

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

> setting-up-log-aggregation

This skill sets up log aggregation solutions using ELK (Elasticsearch, Logstash, Kibana), Loki, or Splunk. It generates production-ready configurations and setup code based on specific requirements and infrastructure. Use this skill when the user requests to set up logging infrastructure, configure log aggregation, deploy ELK stack, deploy Loki, deploy Splunk, or needs help with observability. It is triggered by terms like "log aggregation," "ELK setup," "Loki configuration," "Splunk deployment,

> azure-managed-grafana

Expert knowledge for Azure Managed Grafana development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Managed Grafana applications. Not for Azure Monitor (use azure-monitor).

> error-diagnostics-error-trace

You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging,

> sentry

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.