mcpbeat

Cosmos3 Inference

nvidia/cosmos-framework-cosmos3-inference

> Guide users through running Cosmos3 inference — offline batch generation, online serving with Ray and Gradio, parallelism options, input formats, sampling parameters, and prompt upsampling. Use when the user asks "how do I run inference", "how do I generate a video", "how do I serve the model", "what parameters should I use", or any question about running the model to produce outputs.

This is a copy. The original lives at nvidia/cosmos3-inference.

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Install

one command, takes just this skill from the repository 
npx skills add https://github.com/NVIDIA/cosmos-framework --skill cosmos3-inference

The instruction itself

6 sections, as written by the author

Cosmos3 Inference

When to use this skill

  • Use when a user wants to generate images or videos with Cosmos3
  • Use when a user asks about inference parameters, input formats, or parallelism
  • Use when a user wants to set up online serving (Ray Serve, Gradio)
  • Use when a user asks about prompt engineering or upsampling
  • For environment or import errors, hand off to cosmos3-env-troubleshoot

Path convention

All paths below are relative to the cosmos3 package root (../../../ from this skill file). All uv run / python commands should also be run from there.

Where to find answers

| User question | Go to |

| ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |

| How do I run inference? (single-GPU, multi-GPU) | README.md § Inference |

| Which model should I use? (Nano vs Super, memory, shift) | README.md § Models |

| Which modality? (t2i, t2v, i2v, examples) | README.md § Modalities |

| What parallelism preset? (latency vs throughput) | README.md § Inference |

| What input fields are available? (prompt, vision_path, num_frames, ...) | docs/inference.md § Sample Arguments |

| What are the default parameter values? | cosmos_framework/inference/defaults/<model_mode>/sample_args.json (per-modality JSON) |

| How do I use custom defaults? | docs/inference.md § Custom Defaults |

| How do I override a parameter? (precedence) | docs/faq.md § How do I override a default parameter? |

| What is the shift parameter? | docs/faq.md § What is the shift parameter? |

| How many frames can I generate? (resolution caps) | docs/faq.md § How many frames can I generate? |

| How do I start Ray Serve / Gradio / submit requests? | docs/faq.md § How do I run online inference with Ray? |

| How do I upsample short prompts? | docs/faq.md § Prompt upsampling |

| How do I use the low-level API? (examples/) | examples/inference.py (model API) / examples/inference_pipeline.py (pipeline API) |

| All CLI flags | uv run --all-extras --group=cu130 python -m cosmos_framework.scripts.inference --help |

Things not obvious from the docs

  • Path resolution: relative paths in input JSON files are resolved relative to the JSON file's directory, not the working directory.
  • Seed: always pass --seed for reproducible results. Without it, a random seed is used each time.
  • Resume: interrupted runs can be resumed by re-running the same command — existing outputs are skipped automatically.
  • --keep-going: continues processing remaining samples after a per-sample failure (e.g. guardrail rejection). Used in online serving by default.
  • Unique names: every sample in a run must have a unique name field, or the script will error.

Related skills

| Skill | When to use |

| -------------------------------------- | ---------------------------------------------- |

| ../cosmos3-setup/SKILL.md | Installation and environment setup |

| ../cosmos3-codebase-nav/SKILL.md | Finding files, parameters, and configs in code |

| ../cosmos3-env-troubleshoot/SKILL.md | Debugging environment and runtime errors |

How to use it

Copy the folder

Take nvidia/cosmos-framework-cosmos3-inference from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.