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Found 768 Skills
Configures and manages Depot CI, a drop-in replacement for GitHub Actions that runs workflows entirely within Depot. Use when migrating GitHub Actions workflows to Depot CI, running `depot ci migrate`, managing Depot CI secrets and variables, running workflows with `depot ci run`, debugging Depot CI runs, checking workflow compatibility, or understanding Depot CI's current beta limitations. Also use when the user mentions .depot/ directory, depot ci commands, or asks about running GitHub Actions workflows on Depot's infrastructure without GitHub-hosted runners. NOTE: Depot CI is currently in beta with limited availability.
Cloud infrastructure design and deployment patterns for AWS, Azure, and GCP. Use when designing cloud architectures, implementing IaC with Terraform, optimizing costs, or setting up multi-region deployments.
Use when creating professional architecture diagrams, cloud infrastructure visuals, network topologies, Kubernetes cluster diagrams, or microservices architecture diagrams as PNG/SVG images using Python Diagrams library with real provider icons (AWS, Azure, GCP, K8s, OnPrem, Generic)
Scaffolds an xUnit integration test project for validating Oracle-to-PostgreSQL database migration behavior in .NET solutions. Creates the test project, transaction-rollback base class, and seed data manager. Use when setting up test infrastructure before writing migration integration tests, or when a test project is needed for Oracle-to-PostgreSQL validation.
Write and run Rust tests using cargo test with unit tests, integration tests, doc tests, and property-based testing. Use when writing Rust tests or setting up test infrastructure.
Operate InstaVM infrastructure: run ephemeral sessions, create or manage VMs, host or deploy apps, take snapshots, clone machines, register SSH keys, expose shares, set egress, mount volumes, and use platform APIs. Use this whenever the user mentions InstaVM, instavm.io, the `instavm` Python SDK, `ssh instavm.dev`, app hosting, or VM lifecycle work, even if they do not explicitly say "InstaVM".
Autonomous Frontend Code Generation Agent specialized in project-aware API integration. Use when user provides backend API specs needing frontend request code, mock data to convert to request types and handlers, API endpoints to add with types mocks and tests, or new API integration following existing project conventions. Automatically detects TypeScript, request patterns, mock infrastructure, and test frameworks to generate artifact-gated code.
Develop e-commerce strategy for Southeast Asian markets including platform selection, payment infrastructure, logistics challenges, and localization requirements. Use this skill when the user is expanding e-commerce to SEA, evaluating Shopee vs Lazada vs Tokopedia, or needs to understand SEA market differences — even if they say 'sell to Southeast Asia', 'which platform in Vietnam', 'SEA payment methods', or 'cross-border e-commerce in ASEAN'.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Audit the Claude Code ecosystem — skill health and staleness, project activity pulse, CLAUDE.md instruction drift, Mac Mini service status. Use this skill whenever the user asks about ecosystem health, stale skills, abandoned projects, system status, infrastructure check, "what's broken", "what's stale", "how's my setup", or any request to review the state of their Claude Code environment. Also triggers on "/ecosystem", "audit my ecosystem", "ecosystem health", "ecosystem audit".
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Generates architecture diagrams from code, infrastructure, or descriptions. Use when user asks to visualize, diagram, or document system architecture.