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Found 1,182 Skills
Deploy Django on Google App Engine Standard with Cloud SQL PostgreSQL. Covers Unix socket connections, Cloud SQL Auth Proxy for local dev, Gunicorn configuration, and production-ready settings. Use when: deploying Django to App Engine, configuring Cloud SQL PostgreSQL, setting up Unix socket connections, or troubleshooting "No such file or directory", "connection refused", or "FATAL: password authentication failed".
Deploy to Cloudflare (Workers, R2, D1), Docker, GCP (Cloud Run, GKE), Kubernetes (kubectl, Helm). Use for serverless, containers, CI/CD, GitOps, security audit.
Deploy Python applications to Google App Engine Standard/Flexible. Covers app.yaml configuration, Cloud SQL socket connections, Cloud Storage for static files, scaling settings, and environment variables. Use when: deploying to App Engine, configuring app.yaml, connecting Cloud SQL, setting up static file serving, or troubleshooting 502 errors, cold starts, or memory limits.
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Create Railway projects, services, and databases with proper configuration. Use when user says "setup", "deploy to railway", "initialize", "create project", "create service", or wants to deploy from GitHub. Handles initial setup AND adding services to existing projects. For databases, use railway-railway-database skill instead.
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
Deploy code to Railway using "railway up". Use when user wants to push code, says "railway up", "deploy", "ship", or "push". For initial setup or creating services, use railway-new skill. For Docker images, use railway-environment skill.
Create and deploy serverless functions using AWS Lambda with event sources, permissions, layers, and environment configuration. Use for event-driven computing without managing servers.