synthetic-data
Skill138 starsChanged 14 days agoMITView on GitHub
Generate synthetic training data using NVIDIA Cosmos world foundation models for SDG pipelines
3 other projects use a version of this.
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- thistleknot/skillstheir own version, first seen Nov 8, 2026
- flonat/flonat-researchtheir own version, first seen Nov 3, 2026
- longtermrisk/claude-skillstheir own version, first seen Oct 13, 2026
Other versions3 projects use a version of this.
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Wrote their own version · 3
- flonat/flonat-research★ 150same name
- longtermrisk/claude-skills★ 1same name
- thistleknot/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository10 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- azureml-k3s-compute-target-setupSkill
- dataviewerSkill
- e2e-infrastructureSkill
- e2e-testsSkill
- environment-deploymentSkill
- fleet-deploymentSkill
- fleet-intelligenceSkill
- infrastructureSkill
- osmo-lerobot-trainingSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 41
All NVIDIA Cosmos containers require:
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Synthetic Data Skill
Generate photorealistic training data using NVIDIA Cosmos world foundation models — Cosmos Transfer, Cosmos Predict, and Cosmos Reason.
Overview
The Synthetic Data domain provides SDG pipelines that transform simulation-rendered frames into photorealistic training data, predict future environment states, and curate output for training quality.
Pipeline Stages
| Stage | Model | Purpose |
| Transfer | Cosmos Transfer 2.5 | Convert Isaac Sim renders to photorealistic images |
| Predict | Cosmos Predict 2.5 | Generate future frame sequences from observations |
| Reason | Cosmos Reason 2 | Assess data quality and filter training samples |
Workflow Submission
SDG workflows can be submitted via OSMO or AzureML:
- OSMO workflows:
synthetic-data/workflows/osmo/ - AzureML jobs:
synthetic-data/workflows/azureml/
Key Files
| File | Purpose |
synthetic-data/README.md | Domain overview and directory structure |
synthetic-data/workflows/osmo/sdg-pipeline.yaml | End-to-end OSMO SDG pipeline |
synthetic-data/cosmos/configs/README.md | Model configuration reference |
synthetic-data/specifications/synthetic-data.specification.md | SDG pipeline specification |
synthetic-data/specifications/cosmos-integration.specification.md | Cosmos model integration specification |
Environment Requirements
All NVIDIA Cosmos containers require:
| Variable | Value |
ACCEPT_EULA | Y |
PRIVACY_CONSENT | Y |
NVIDIA_DRIVER_CAPABILITIES | all |
GPU Requirements
Each Cosmos model stage requires a minimum of 1x A100 (40 GB) GPU. H100 (80 GB) recommended for production workloads.
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