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Found 491 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.
Salesforce Data Cloud Harmonize phase. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use sf-datacloud-prepare), segments/insights (use sf-datacloud-segment), retrieval/search (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Salesforce Data Cloud Segment phase. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use sf-datacloud-harmonize), activation work (use sf-datacloud-act), query/search-index work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Investigates a root cause and files a minimal fix PR for a reported bug or observability finding.
Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery.
Lead complex software implementation, architecture decisions, and reliable delivery across any modern technology stack. Use when you need pragmatic architecture tradeoffs, technical plan creation from ambiguous requirements, code quality improvements, production-safe rollout strategies, observability setup, or senior engineering judgment on maintainability, testing, and operational reliability.
Build and operate modern Node.js applications with strong architecture, dependency hygiene, performance, resilience, observability, and security controls. Use when designing project layout, runtime/module strategy, testing and CI, release workflows, and production operations.
This skill should be used when the user asks to "review code", "review my changes", "check effect patterns", "run effect review", "effect review", "review for effect best practices", or wants a comprehensive code review against Effect-TS conventions, branded types, observability, error handling, test coverage, and UI quality.
Go implementation guide for PMA-managed service and CLI projects. Covers project layout (cmd/internal), strict linting with golangci-lint v2, database access (sqlc + pgx or GORM), HTTP patterns (stdlib + Chi or Gin), layered config with koanf, structured logging with slog, OpenTelemetry observability, and CI quality gates.