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Found 1,984 Skills
Modern Python API development with FastAPI covering async patterns, Pydantic validation, dependency injection, and production deployment
Use when architecting OCI solutions, migrating from AWS/Azure, designing multi-AD deployments, or avoiding common OCI anti-patterns. Covers VCN sizing mistakes, Cloud Guard gotchas, free tier specifics, OCI terminology confusion, and multi-AD patterns.
Build full-stack web applications powered by Google Gemini's Nano Banana & Nano Banana Pro image generation APIs. Use when creating Next.js image generators, editors, galleries, or any web app that integrates gemini-2.5-flash-image or gemini-3-pro-image-preview models. Covers React components, server actions, API routes, storage, rate limiting, and production deployment patterns.
Use Robonet's MCP server to build, backtest, optimize, and deploy trading strategies. Provides 24 specialized tools for crypto and prediction market trading: (1) Data tools for browsing strategies, symbols, indicators, Allora topics, and backtest results, (2) AI tools for generating strategy ideas and code, optimizing parameters, and enhancing with ML predictions, (3) Backtesting tools for testing strategy performance on historical data, (4) Prediction market tools for Polymarket trading strategies, (5) Deployment tools for live trading on Hyperliquid, (6) Account tools for credit management. Use when: building trading strategies, backtesting strategies, deploying trading bots, working with Hyperliquid or Polymarket, or enhancing strategies with Allora Network ML predictions.
Sagemaker Endpoint Deployer - Auto-activating skill for ML Deployment. Triggers on: sagemaker endpoint deployer, sagemaker endpoint deployer Part of the ML Deployment skill category.
Set up GitHub Actions workflows for CI/CD with automated testing, linting, and deployment for Python/UV projects. Use when creating CI pipelines, automating tests, or setting up deployment workflows.
Master Kubernetes with pods, deployments, services, ingress, ConfigMaps, secrets, and production cluster management.
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.
Scaffolds a production-ready Next.js turborepo with TypeScript, Tailwind CSS, shadcn CLI, Blode UI components from ui.blode.co, blode-icons-react, Biome, Ultracite, and Vercel deployment. Use when creating a new Next.js app, bootstrapping a turborepo, scaffolding a web project, starting a new website, or asking "create a Next.js project."
Strategic AI thinking frameworks and mental models from Satya Nadella's perspective on platform shifts, AI deployment, and building successful AI products. Use when evaluating AI strategy decisions, assessing platform opportunities, thinking through AI product positioning, considering enterprise AI deployment challenges, evaluating talent and team capabilities, or needing frameworks for justifying AI investments in terms of economic surplus. Triggers on questions about AI platform strategy, change management for AI adoption, building AI scaffolding layers, evaluating AI opportunities, or thinking through AI's societal implications.
Full interactive onboarding for remobi — the mobile terminal overlay for tmux. Checks prerequisites, inspects tmux config, interviews the user about their workflow, generates a validated remobi.config.ts, suggests tmux mobile optimisations, and walks through deployment. Use this skill whenever someone asks to set up remobi, configure remobi, onboard with remobi, generate a remobi config, make tmux mobile-friendly, or deploy remobi with Tailscale. Also use when the user says "onboard me" or "set up my phone terminal".
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure ML pipelines, AutoML, managed online/batch endpoints, prompt flow, or MLflow deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm).