Agent skill

nemotron-retrieval-recipes

Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexApache-2.0
Install
npx skills add NVIDIA/skills --skill nemotron-retrieval-recipes --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 10 KB
Bundled scripts: none
Version: 0.2.0
Declared author: NVIDIA Nemotron Team <noreply@nvidia.com>
Path: skills/nemotron-retrieval-recipes/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Nemotron Retrieval Recipes Invocation: `$nemotron-retrieval-recipes`. ## Purpose Use this skill to work with public Nemotron embedding and reranking retrieval recipes in a source checkout or installed package. Prefer the current checkout over memory, because the recipe CLI, configs, containers, and output paths are actively changing. Treat each recipe family as available only after its recipe di

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About this skill
What does the nemotron-retrieval-recipes skill do?

Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.

How do I install it?

Run `npx skills add NVIDIA/skills --skill nemotron-retrieval-recipes --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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