Name: nvidia-kaggle
Author: nvidia

NVIDIA Kaggle Plugin

NVIDIA Kaggle Plugin gives agents end-to-end Kaggle competition workflows through a single skill, nvidia-kaggle-skill. It can gather competition context, study public writeups and notebooks, reproduce kernels locally, submit to competitions, and manage Kaggle datasets.

The skill accepts whatever's natural — a competition slug, a Kaggle URL, a kernel ref, a writeup URL, or a local path — and picks the right script to run based on the request.

Demo: asking Claude Code to summarize the top AIMO Progress Prize 2 solutions

Examples of supported workflows include:

Workflow	What it does
Competition context	Fetches the overview, rules, evaluation, timeline, and dataset description.
Solution writeups	Finds leaderboard writeups and summarizes the strongest approaches.
Discussions	Indexes Kaggle discussions, searches them, and reads selected threads.
Kernels	Indexes, queries, reads, and researches public Kaggle kernels.
Kernel reproduction	Downloads a Kaggle notebook and its inputs into a local workspace.
Submission	Pushes a Kaggle kernel submission and polls for the final result.
Dataset upload	Creates or updates a Kaggle dataset from a local folder.

Requirements

Python 3.10 or newer.
An agent runtime with plugin or Agent Skills support, such as Codex, Claude Code, or another compatible harness.
A Kaggle account and KAGGLE_API_TOKEN for API-backed workflows, including dataset access, kernels, submissions, and dataset uploads.

Examples

Top Solution Writeups

What this does: Scrapes the competition leaderboard, opens the top-N solution discussions, and summarizes the techniques each top team used. Useful when starting a new competition and you want to know what has historically won.

/nvidia-kaggle:nvidia-kaggle-skill Get the top 3 solution writeups from the AI Mathematical Olympiad - Progress Prize 2 competition and summarize the key strategies.

Show answer

AIMO Progress Prize 2 - Top 3 Writeup Summary

Rank  Team                  Approach
1st   NemoSkills (NVIDIA)   Large-scale SFT + TIR fine-tune + model merge + TensorRT-LLM/FP8/ReDrafter
2nd   imagination-research  SFT + DPO on DeepSeek-R1-14B + lmdeploy W4KV8 + dual CoT/Code prompting
3rd   Aliev                 Off-the-shelf DeepSeek-R1-14B AWQ + branching, duplication, and early-stop inference

Key strategies:
1. Tool-integrated reasoning improved the winning team's majority-vote accuracy.
2. DPO shortened outputs, enabling more samples within the time budget.
3. Quantization, fast inference engines, and speculative decoding were critical.
4. Multi-level early stopping reduced wasted generation.
5. Dynamic time budgeting helped allocate samples across questions.
6. Strong inference design was enough for 3rd place without fine-tuning.

Files written:
- 1st_place_writeup.md
- 2nd_place_writeup.md
- 3rd_place_writeup.md
- summary.md

Competition Overview And Dataset

What this does: Fetches the competition's overview, rules, evaluation metric, timeline, and dataset description in one pass. This is usually the first thing to run for a new competition — it gives the agent the framing it needs before writing any code.

/nvidia-kaggle:nvidia-kaggle-skill Fetch the competition overview and dataset description for the ARC Prize 2025 competition.

Show answer

ARC Prize 2025 - Competition Overview

Type: Research competition - $1,000,000 total prize pool
Goal: Build AI systems that efficiently learn new skills and solve novel abstract reasoning tasks.
Metric: Percent correct predictions, with exact grid match required and 2 attempts per test output.
Submission: JSON predictions for 240 unseen test tasks. Notebooks only, no internet, up to 12h runtime.

Dataset files:
- arc-agi_training_challenges.json
- arc-agi_training_solutions.json
- arc-agi_evaluation_challenges.json
- arc-agi_evaluation_solutions.json
- arc-agi_test_challenges.json
- sample_submission.json

Total size: 6.91 MB
License: Apache 2.0
Grid format: rectangular matrices of integers 0-9, up to 30x30.

nvidia-kaggle

Popularity

What's Inside

README

NVIDIA Kaggle Plugin

Requirements

Examples

Top Solution Writeups

Competition Overview And Dataset

Confidence

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