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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
paidf-anomalygen
Full PAIDF AnomalyGen pipeline — fine-tune on a new anomaly dataset, generate synthetic anomaly images (SDG), evaluate quality (nn_score), and search per-sample (guidance, crop_ratio) parameters. Thre
paidf-curation-and-retrieval
Use when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining nearest-neighbor matching on Curat
tao-launch-workflow
Shared launch intake for any TAO workflow or action. Use when the user wants to run TAO AutoML, train, evaluate, infer, export, generate TensorRT engines, or launch DEFT/workflow jobs on an execution
tao-list-capabilities
Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. Use when the user asks "what can TAO Skill Bank do", "li
tao-port-huggingface-model
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a Hugging
tao-run-automl-deft-pipeline
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmente
tao-run-platform
TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Brev, SLURM, local Docker, Kubernetes). Use when the user wants to run TAO jobs through the SDK, get job track
tao-run-platform
TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Brev, SLURM, local Docker, Kubernetes). Use when the user wants to run TAO jobs through the SDK, get job track
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.