microservices-architect
Design and architect microservices following Chris Richardson's Microservices Patterns. Use when designing services, APIs, data flows, or evaluating architecture decisions. Guides full workflow from feature intake to production-ready design with observability, async patterns, security, testing, and deployment strategies.
optimize-costs
Analyze Cloudflare architecture and predict monthly costs with optimization recommendations. Use this skill when the user asks about costs, billing, pricing, or wants to understand their Cloudflare spend. Works with wrangler configs, observability data, and AI Gateway logs.
backend-dev-guidelines
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
holmesgpt-skill
Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).
pyroscope
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observability
Telemetry, metrics, tracing, and observability for Elixir/BEAM applications
detecting-anomalies
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier detection (Z-score, IQR), threshold-based alerts, rate-of-change detection with window functions, and moving average baselines. Choose pattern based on data distribution and anomaly type.
logging-observability
Guidelines for structured logging, distributed tracing, and debugging patterns across languages. Covers logging best practices, observability, security considerations, and performance analysis.
aws-cloudwatch
Implement monitoring, alerting, and observability with CloudWatch
cloud-infrastructure
Cloud platforms (AWS, Cloudflare, GCP, Azure), containerization (Docker), Kubernetes, Infrastructure as Code (Terraform), CI/CD, and observability.
monitoring-observability
Prometheus, Grafana, logging, alerting, and data pipeline observability
postgresql-monitoring
PostgreSQL monitoring - metrics, alerting, observability
microservices-design
Production-grade microservices design skill for service decomposition, service mesh, resilience patterns, and observability
ml-monitoring
Production-grade ML model monitoring, drift detection, and observability
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.
functional-testing
Write functional tests that test public interfaces only (HTTP APIs via requests, libraries via exported functions, CLIs via command execution). Never test internal functions directly. Expose observability APIs for internal behavior testing.
datadog-entity-generator
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langfuse-observability
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