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Found 384 Skills
Comprehensive infrastructure engineering covering DevOps, cloud platforms, FinOps, and DevSecOps. Platforms: AWS (EC2, Lambda, S3, ECS, EKS, RDS, CloudFormation), Azure basics, Cloudflare (Workers, R2, D1, Pages), GCP (GKE, Cloud Run, Cloud Storage), Docker, Kubernetes. Capabilities: CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins), GitOps, infrastructure as code (Terraform, CloudFormation), container orchestration, cost optimization, security scanning, vulnerability management, secrets management, compliance (SOC2, HIPAA). Actions: deploy, configure, manage, scale, monitor, secure, optimize cloud infrastructure. Keywords: AWS, EC2, Lambda, S3, ECS, EKS, RDS, CloudFormation, Azure, Kubernetes, k8s, Docker, Terraform, CI/CD, GitHub Actions, GitLab CI, Jenkins, ArgoCD, Flux, cost optimization, FinOps, reserved instances, spot instances, security scanning, SAST, DAST, vulnerability management, secrets management, Vault, compliance, monitoring, observability. Use when: deploying to AWS/Azure/GCP/Cloudflare, setting up CI/CD pipelines, implementing GitOps workflows, managing Kubernetes clusters, optimizing cloud costs, implementing security best practices, managing infrastructure as code, container orchestration, compliance requirements, cost analysis and optimization.
Manage Istio service mesh for traffic management, security, and observability. Use for traffic shifting, canary releases, mTLS, and service mesh troubleshooting.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
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.
Set up comprehensive observability for Groq integrations with metrics, traces, and alerts. Use when implementing monitoring for Groq operations, setting up dashboards, or configuring alerting for Groq integration health. Trigger with phrases like "groq monitoring", "groq metrics", "groq observability", "monitor groq", "groq alerts", "groq tracing".
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
Elasticsearch and Elastic APM integration with Serilog structured logging for .NET applications. Use when: (1) Implementing or configuring Serilog with Elasticsearch sink, (2) Setting up Elastic APM with data streams and authentication, (3) Creating logging extension methods in Infrastructure layer, (4) Enriching logs with app-name and app-type properties, (5) Configuring log levels and environment-specific logging, (6) Questions about logging security (PII, credentials), or (7) Troubleshooting observability and monitoring setup.
Set up comprehensive observability for Mistral AI integrations with metrics, traces, and alerts. Use when implementing monitoring for Mistral AI operations, setting up dashboards, or configuring alerting for Mistral AI integration health. Trigger with phrases like "mistral monitoring", "mistral metrics", "mistral observability", "monitor mistral", "mistral alerts", "mistral tracing".
Production observability with structured logging, metrics collection, distributed tracing, and alerting
Expert-level monitoring and observability with Prometheus, Grafana, logging, and alerting
Complete reference for the Galileo AI platform TypeScript/JS SDK for evaluating, observing, and protecting GenAI applications. Use when building Node.js or TypeScript applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.