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Found 150 Skills
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
Centrifugo real-time messaging server expert for WebSocket PUB/SUB, channel management, JWT authentication, event proxying, and horizontal scaling with Redis/NATS. Use when: centrifugo, centrifugal, real-time messaging, websocket pubsub, channel subscriptions, real-time notifications, live updates, presence, history recovery, server-sent events integration, real-time transport layer. Do not use for: general WebSocket programming without Centrifugo, Socket.IO, Pusher SDK, or other real-time frameworks.
Unauthorized access playbook for common exposed services. Use when Redis, Rsync, PHP-FPM, AJP/Ghostcat, Hadoop YARN, H2 Console, or similar management interfaces are exposed without authentication.
Deploy and operate Infisical self-hosted instances with Docker, Docker Compose, and Kubernetes. Covers architecture, environment variables, ENCRYPTION_KEY management, database setup, Redis configuration, production hardening, FIPS compliance, scaling, and high availability patterns.
Deploy containerized applications (especially Rails) to VPS using Kamal 2. Covers deploy.yml configuration, accessories (PostgreSQL, Redis, Sidekiq), SSL/TLS, secrets management, CI/CD with GitHub Actions, database backups, server hardening, debugging, and scaling. Use when setting up Kamal, configuring deployments, troubleshooting deploy issues, or managing production infrastructure with Kamal.
The definitive skill for building and deploying high-performance, distributed systems using Cloud Native standards (Dapr, Redis, Microservices). Use when a project requires professional-grade architecture, cross-service communication, elastic scaling, and sub-second agentic latency. Mandatory for flawless deployments on Kubernetes (Local or Cloud).
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
Deploy containerized web applications to any Linux server using Kamal. Use when users need to deploy, configure, debug, or manage Kamal deployments including initial setup, configuration of deploy.yml, deployment workflows, rollbacks, managing accessories (databases, Redis), troubleshooting deployment issues, or understanding Kamal commands and best practices.
Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.
Deploys infrastructure components via Helm charts on TrueFoundry. Supports any public or private OCI Helm chart including databases (Postgres, MongoDB, Redis), message brokers (Kafka, RabbitMQ), and vector databases (Qdrant, Milvus). Uses YAML manifests with `tfy apply`. Use when installing Helm charts or deploying infrastructure on TrueFoundry.
Use when running commands inside a Zeabur service container. Use for one-off database operations like queries, data cleanup, or migrations (e.g. mongosh, psql, mysql, redis-cli). Use when user says "exec into container", "run command in service", "query database", "delete from database", "run mongo command", "run SQL", "check files in container", "debug inside service", or "shell into service". Use for container-level debugging like checking env vars, files, processes, or connectivity. NOT for deploying databases (use zeabur-template-deploy instead).
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.