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Found 2,042 Skills
End-to-end Stellar development playbook. Covers Soroban smart contracts (Rust SDK), Stellar CLI, JavaScript/Python/Go SDKs for client apps, Stellar RPC (preferred) and Horizon API (legacy), Stellar Assets vs Soroban tokens (SAC bridge), wallet integration (Freighter, Stellar Wallets Kit), smart accounts with passkeys, status-sensitive zero-knowledge proof patterns, testing strategies, security patterns, and common pitfalls. Optimized for payments, asset tokenization, DeFi, privacy-aware applications, and financial applications. Use when building on Stellar, Soroban, or working with XLM, Stellar Assets, trustlines, anchors, SEPs, ZK proofs, or the Stellar RPC/Horizon APIs.
Integrate and embed OpenAI ChatKit UI into TypeScript/JavaScript frontends (Next.js, React, or vanilla) using either hosted workflows or a custom backend (e.g. Python with the Agents SDK). Use this Skill whenever the user wants to add a ChatKit chat UI to a website or app, configure api.url, auth, domain keys, uploadStrategy, or debug blank/buggy ChatKit widgets.
Use this skill when building MCP (Model Context Protocol) servers with FastMCP in Python. FastMCP is a framework for creating servers that expose tools, resources, and prompts to LLMs like Claude. The skill covers server creation, tool/resource definitions, storage backends (memory/disk/Redis/DynamoDB), server lifespans, middleware system (8 built-in types), server composition (import/mount), OAuth Proxy, authentication patterns, icons, OpenAPI integration, client configuration, cloud deployment (FastMCP Cloud), error handling, and production patterns. It prevents 25+ common errors including storage misconfiguration, lifespan issues, middleware order errors, circular imports, module-level server issues, async/await confusion, OAuth security vulnerabilities, and cloud deployment failures. Includes templates for basic servers, storage backends, middleware, server composition, OAuth proxy, API integrations, testing, and self-contained production architectures. Keywords: FastMCP, MCP server Python, Model Context Protocol Python, fastmcp framework, mcp tools, mcp resources, mcp prompts, fastmcp storage, fastmcp memory storage, fastmcp disk storage, fastmcp redis, fastmcp dynamodb, fastmcp lifespan, fastmcp middleware, fastmcp oauth proxy, server composition mcp, fastmcp import, fastmcp mount, fastmcp cloud, fastmcp deployment, mcp authentication, fastmcp icons, openapi mcp, claude mcp server, fastmcp testing, storage misconfiguration, lifespan issues, middleware order, circular imports, module-level server, async await mcp
Dual skill for deploying scientific models. FastAPI provides a high-performance, asynchronous web framework for building APIs with automatic documentation. Streamlit enables rapid creation of interactive data applications and dashboards directly from Python scripts. Load when working with web APIs, model serving, REST endpoints, interactive dashboards, data visualization UIs, scientific app deployment, async web frameworks, Pydantic validation, uvicorn, or building production-ready scientific tools.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
Work with Vercel Sandbox — ephemeral Linux microVMs for running untrusted code, AI agent output, and developer experimentation on Vercel. Use this skill when the user mentions "Vercel Sandbox", "@vercel/sandbox", sandbox microVMs, running code in isolated environments on Vercel, or wants to create/manage/snapshot sandboxes via the TypeScript/Python SDK or Vercel CLI. Also trigger when the user asks about sandbox pricing, resource limits, authentication (OIDC tokens, access tokens), system specifications, CLI commands (`vercel sandbox`), or wants to update the local documentation cache for this skill.
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
Implement payment integrations with SePay (Vietnamese payment gateway with VietQR, bank transfers, cards) and Polar (global SaaS monetization platform with subscriptions, usage-based billing, automated benefits). Use when integrating payment processing, implementing checkout flows, managing subscriptions, handling webhooks, processing bank transfers, generating QR codes, automating benefit delivery, or building billing systems. Supports authentication (API keys, OAuth2), product management, customer portals, tax compliance (Polar as MoR), and comprehensive SDK integrations (Node.js, PHP, Python, Go, Laravel, Next.js).
Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified. Originally from OpenAI's curated skills catalog.
Guidance for working with the Beltic KYA (Know Your Agent) ecosystem - a credential-based trust framework for AI agents. Use when: (1) Working in any Beltic repository (beltic-spec, beltic-cli, beltic-sdk, fact-python, kya-platform, wizard, nasa), (2) Implementing agent credential signing/verification, (3) Using @belticlabs/kya SDK or beltic-sdk Python, (4) Understanding agent safety certification, (5) Working with verifiable credentials for AI. Triggers on: Beltic CLI commands, agent credentials, HTTP message signatures (RFC 9421), safety scores, KYB tier verification, trust chain validation.