Loading...
Loading...
Found 1,265 Skills
Expert in integrating third-party APIs with proper authentication, error handling, rate limiting, and retry logic. Specializes in Auth.js v5, GPT-5 model orchestration, Stripe SDK v13+, and architectural context packing for large codebases. Optimized for 2026 standards with Edge-first performance and autonomous agent integration.
Provides production-ready Kubernetes manifest guidance including resource management, security, high availability, and configuration best practices. This skill should be used when working with Kubernetes YAML files, deployments, pods, services, or when users mention k8s, container orchestration, or cloud-native applications.
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.
CI/CD automation and workflow orchestration using GitHub Actions for builds, tests, deployments, and repository automation
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.
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.
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
Goal-based workflow orchestration - routes tasks to specialist agents based on user goals
Manage Alibaba Cloud Intelligent Cloud Editing (ICE) media workflows via OpenAPI/SDK. Use for media processing jobs, template/workflow orchestration, editing and production pipelines, and job status troubleshooting.
Design multi-container applications with Docker Compose. Covers service orchestration, networking, volumes, environment configuration, health checks, and production deployment patterns. Use when defining local development stacks or deploying multi-service applications.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Creates and orchestrates multi-agent pipelines on the iii engine. Use when building AI agent collaboration, agent orchestration, research/review/synthesis chains, or any system where specialized agents hand off work through queues and shared state.