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Found 795 Skills
Comprehensive guide for production-ready Python backend development and software architecture at scale. Use when designing APIs, building backend services, creating microservices, structuring Python projects, implementing database patterns, writing async code, or any Python backend/server-side development task. Covers Clean Architecture, Domain-Driven Design, Event-Driven Architecture, FastAPI/Django patterns, database design, caching strategies, observability, security, testing strategies, and deployment patterns for high-scale production systems.
Check and stream Convex deployment logs from the CLI. Use when debugging Convex actions, 401/500 errors, failed queries or mutations, or when you need to see what functions ran and their output.
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.
Kubernetes operations including deployment, management, troubleshooting, kubectl mastery, and cluster stability. Covers K8s workloads, networking, storage, and debugging pods. Use when user mentions Kubernetes, K8s, kubectl, pods, deployments, services, ingress, ConfigMaps, Secrets, or cluster operations.
Java plugin development for Hytale servers. Covers Java 25 setup, Gradle builds, Antigravity/IDE configuration, plugin lifecycle (setup/start/shutdown), command registration, event handling, ECS architecture, and deployment. Use when creating server plugins, extending game functionality, or implementing custom game mechanics.
Provides comprehensive Google Cloud Platform (GCP) guidance including Compute Engine, Cloud Storage, Cloud SQL, BigQuery, GKE (Google Kubernetes Engine), Cloud Functions, Cloud Run, VPC networking, load balancing, IAM, Cloud Build, infrastructure as code (Terraform, Deployment Manager), security configuration, cost optimization, and multi-region deployment. Produces infrastructure code, deployment scripts, configuration guides, and architecture designs. Use when deploying to Google Cloud, designing GCP infrastructure, migrating to GCP, configuring GCE instances, setting up Cloud Storage, managing Cloud SQL databases, working with BigQuery, deploying to GKE, or when users mention "Google Cloud", "GCP", "Compute Engine", "Cloud Storage", "BigQuery", "GKE", "Cloud Run", "Cloud Functions", "VPC", "Cloud SQL", or "Google Cloud Platform".
Google Cloud Platform services including GKE, Cloud Run, Cloud Storage, BigQuery, and Pub/Sub. Activate for GCP infrastructure, Google Cloud deployment, and GCP integration.
Use when preparing a Bknd application for production deployment. Covers security hardening, environment configuration, isProduction flag, JWT settings, Guard enablement, CORS, media storage, and production checklist.
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.
When the user wants to deploy AI sales development reps, automate sales qualification, build signal-to-action routing, or design AI agent architecture for sales. Also use when the user mentions 'AI SDR,' 'AI sales agent,' 'automated qualification,' 'signal routing,' 'sales automation,' '11x,' 'Artisan,' 'AiSDR,' 'AI BDR,' or 'autonomous sales.' This skill covers AI SDR deployment, qualification automation, and agent architecture for sales development.
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"