> browse/registry

> setting-up-distributed-tracing

This skill automates the setup of distributed tracing for microservices. It helps developers implement end-to-end request visibility by configuring context propagation, span creation, trace collection, and analysis. Use this skill when the user requests to set up distributed tracing, implement observability, or troubleshoot performance issues in a microservices architecture. The skill is triggered by phrases such as "setup tracing", "implement distributed tracing", "configure opentelemetry", or

> cloudwatch

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.

> database-optimizer

Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.

> pyvene-interventions

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

> azure-mgmt-weightsandbiases-dotnet

Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability. Triggers: "Weights and Biases", "W&B", "WeightsAndBiases", "ML experiment tracking", "model registry", "experiment management", "wandb".

> Function Call Tracing

Instrument C/C++ with -finstrument-functions for execution tracing and Perfetto visualization

> brand-guidelines

Write copy following Sentry brand guidelines. Use when writing UI text, error messages, empty states, onboarding flows, 404 pages, documentation, marketing copy, or any user-facing content. Covers both Plain Speech (default) and Sentry Voice tones.

> observability-engineer

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

> weights-and-biases

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

> langsmith-observability

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

> grafana-dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

> azure-compliance

Comprehensive Azure compliance and security auditing capabilities including best practices assessment, Key Vault expiration monitoring, and resource configuration validation. USE FOR: compliance scan, security audit, azqr, Azure best practices, Key Vault expiration check, compliance assessment, resource review, configuration validation, expired certificates, expiring secrets, orphaned resources, policy compliance, security posture evaluation. DO NOT USE FOR: deploying resources (use azure-deploy

> phoenix-observability

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

> monitoring-cpu-usage

This skill enables Claude to monitor and analyze CPU usage patterns within applications. It helps identify CPU hotspots, analyze algorithmic complexity, and detect blocking operations. Use this skill when the user asks to "monitor CPU usage", "optimize CPU performance", "analyze CPU load", or "find CPU bottlenecks". It assists in identifying inefficient loops, regex performance issues, and provides optimization recommendations. This skill is designed for improving application performance by addr

> x-tweet-fetcher

Fetch tweets, replies, and user timelines from X/Twitter without login or API keys. Also supports Chinese platforms (Weibo, Bilibili, CSDN, WeChat). Includes camofox_search() for zero-cost Google search without API keys. Basic tweet fetching: zero dependencies. Replies/timelines/search: requires Camofox. NEW: X-Tracker for tweet growth monitoring with burst detection.

> common-debugging

Systematic troubleshooting using the Scientific Method. Use when debugging crashes, tracing errors, diagnosing unexpected behavior, or investigating exceptions. (triggers: debug, fix bug, crash, error, exception, troubleshooting)

> deploying-monitoring-stacks

This skill deploys monitoring stacks, including Prometheus, Grafana, and Datadog. It is used when the user needs to set up or configure monitoring infrastructure for applications or systems. The skill generates production-ready configurations, implements best practices, and supports multi-platform deployments. Use this when the user explicitly requests to deploy a monitoring stack, or mentions Prometheus, Grafana, or Datadog in the context of infrastructure setup.

> collecting-infrastructure-metrics

This skill enables Claude to collect comprehensive infrastructure performance metrics across compute, storage, network, containers, load balancers, and databases. It is triggered when the user requests "collect infrastructure metrics", "monitor server performance", "set up performance dashboards", or needs to analyze system resource utilization. The skill configures metrics collection, sets up aggregation, and helps create infrastructure dashboards for health monitoring and capacity tracking. It

> devops-troubleshooter

Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system troubleshooting.

> distributed-tracing

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.