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Found 14 Skills
Start here for any Runpod task — running GPU/CPU pods, deploying serverless endpoints, templates, network volumes, building images, or understanding how Runpod works. Routes the request to the right Runpod skill (runpod-mcp, runpodctl, flash, companion-clis, or runpod-usage). Use when it is unclear which Runpod skill applies.
How Runpod works and how to work it — pods vs serverless, GPU/VRAM selection, storage, building a container, networking, plus the agentic pod development loop (provision → ssh-exec → set up → poll readiness) and on-pod install hygiene (uv/apt). Use to answer "how does X work", "which GPU", "how do I build a container", or "how do I stand up a workload on a pod". Guidance, not a tool — execute with runpodctl, runpod-mcp, or flash.
Early-access skill for Mimiry's softlaunch GPU compute platform. Use this skill whenever the user wants to run a GPU job, start a compute session, train a model, launch a container on a GPU, check their balance, manage running sessions, or build a compute job script on the Mimiry softlaunch environment. Also triggers when the user mentions Mimiry softlaunch, Mimiry compute, GPU sessions, SSH-ing into a session, or asks to "run this on a GPU" or "launch a training job". Covers both quick one-liners and interactive job-building workflows. This is the softlaunch (early beta) version — the API and features may change.
Runpod CLI to manage your GPU workloads.
Vast.ai CLI to manage GPU instances, volumes, serverless endpoints, and billing.
Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).
Runpod's official prebuilt pod templates (ComfyUI, PyTorch, and others): what each image ships, which ports and paths it uses, how to pick one, how to pin a version, and what is missing on first boot. Use when a task says "run <X> on a pod" and an official template already covers it, instead of building an image — and when a user needs help with or wants to fix something about a Runpod template they are already running (won't boot, can't reach the UI, missing models, wrong CUDA line): start here and route onward. Deploy with runpodctl or runpod-mcp; end-to-end walkthroughs live in the runpod router's golden paths.
Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).
Create a minimal working Vast.ai example. Use when starting a new Vast.ai integration, testing your setup, or learning basic Vast.ai API patterns. Trigger with phrases like "vastai hello world", "vastai example", "vastai quick start", "simple vastai code".
Manage Lightning AI Studios (cloud dev machines with CPUs/GPUs) - create, start, stop, delete studios, switch machine types, run commands in them, upload/download files, and SSH in. Use when the user wants to work with lightning.ai Studios, needs a cloud GPU dev box, or asks to run something "on a studio".
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.