Nvidia Kaggle Skill logo

Nvidia Kaggle Skill

Organization
NVIDIA
nvidia-kaggle-skill

Use for Kaggle competition overview fetches, writeups, discussion/kernel research, submissions, and dataset uploads. Not for unrelated ML code.

Overview

PublisherNVIDIA
Repositorynvidia-kaggle
Skill namenvidia-kaggle-skill
Stars
341
Forks
38
Bundled files
42
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 42 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA on GitHub. Read the source before you install it.

Installation

Install the Nvidia Kaggle Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVIDIA/nvidia-kaggle/tree/main/skills/nvidia-kaggle-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA/nvidia-kaggle.git /tmp/nvidia-kaggle
mkdir -p .claude/skills
cp -r /tmp/nvidia-kaggle/skills/nvidia-kaggle-skill .claude/skills/nvidia-kaggle-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nvidia Kaggle Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nvidia Kaggle Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nvidia Kaggle Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

NVIDIA Kaggle Skill

Purpose

Use this skill for Kaggle competition work: context gathering, writeups, discussions, kernels, local reproduction, submission, and dataset upload.

Do not use it for unrelated ML training, generic notebook editing, general data analysis, or non-Kaggle dataset management unless the user explicitly ties the task to Kaggle.

Inputs

InputRequiredDescription
Kaggle slug, URL, writeup URL, kernel ref, or local folderDepends on taskPrimary target for the requested Kaggle action.
KAGGLE_API_TOKENRequired for API/CLI-backed workflowsKGAT token string for Kaggle API, CLI, and SDK calls.
Disk spaceRequired for kernel setupMust fit input datasets, competition data, models, and extracted archives.

Prerequisites

  • Run commands from this skill directory unless a referenced workflow says otherwise.
  • Install only the runtime packages needed for the requested workflow.
  • Set KAGGLE_API_TOKEN before API, CLI, kernel, discussion, dataset, or submission workflows.
  • Confirm local disk space before downloading competition data, kernel inputs, or extracted archives.
  • Require explicit user confirmation before sensitive, externally visible actions: competition submissions (each can consume a daily slot), dataset uploads, and creating a public dataset. Treat KAGGLE_API_TOKEN as a secret — never print, log, or echo it.

Runtime Dependencies

Install only the packages needed for the requested task into the current environment, then run scripts with python.

Kaggle API, CLI, kernels, discussions, datasets, competition pages, and writeups:

bash
if command -v uv >/dev/null 2>&1; then
  uv pip install httpx kaggle kagglesdk nbformat pydantic python-dotenv rich
else
  python -m pip install httpx kaggle kagglesdk nbformat pydantic python-dotenv rich
fi

For API/CLI tasks, verify credentials before calling Kaggle:

bash
: "${KAGGLE_API_TOKEN:?ERROR: KAGGLE_API_TOKEN environment variable is not set}"

Workflows

Use this workflow catalog to choose the right path. Run the direct script commands for quick tasks. For workflows that point to another markdown file, read that file only when the request needs that workflow.

Prefer the runtime's run_script helper when it exists, for example run_script("scripts/fetch_competition_info.py", args=["titanic"]). Otherwise run the equivalent python ./scripts/<script>.py ... command from this skill directory.

Competition Details

Use this when the user asks to retrieve or summarize a Kaggle competition overview, rules, evaluation, timeline, or dataset description.

Fetch overview:

bash
python ./scripts/fetch_competition_info.py <competition-slug-or-url>

Fetch dataset description:

bash
python ./scripts/fetch_dataset_info.py <competition-slug-or-url>

The scripts accept a bare competition slug or https://www.kaggle.com/competitions/<slug> URL and extract the slug automatically. Convert output to markdown when the user asks for saved documentation, using {slug}_competition_overview.md and {slug}_dataset_description.md in the current working directory.

Research Brief

Use this when the user asks you to research a competition and write a strategy brief in natural terms (e.g. "research this competition and brief me, with links and a few charts"). You chain the skill's individual research workflows yourself, write your own analysis/plotting code, and produce the brief. Read ./research-brief.md for the principles that keep the brief accurate, informative, and useful to a reader — how to cite real sources as links, and how to make plots honest and legible (every plotted number traces to what you gathered). These principles live in the skill so the user does not have to spell them out.

Writeups

Use this when the user asks to fetch one writeup, fetch top-k writeups, discover leaderboard writeup links, or summarize solution posts. Read ./writeups.md.

Discussions

Use this when the user asks for Kaggle competition discussions, community insights, questions, tips, or a specific discussion thread.

bash
python ./scripts/discussion_ingest.py <competition_id> [--max-pages N] [--sort-by hotness|votes|comments|created|updated] [--page-size N] [--nofetch-comments]
python ./scripts/discussion_query.py <competition_id> [--search TERM] [--min-votes N] [--author NAME] [--limit N] [--as-json]
python ./scripts/discussion_read.py <discussion_id> [--competition-id ID]
python ./scripts/discussion_db_info.py [competition_id]

Storage:

PathContents
data/discussions.dbSQLite cache for discussions, comments, and competition metadata

Always run ingest before query/read if the database is empty. Keep retries bounded if Kaggle rate limits or API shapes change.

Kernels

Use this when the user asks to ingest, query, or read kernels; research top public kernels; fetch kernel scores; or analyze kernel lineage. Read ./kernels.md.

Kernel Setup

Use this when the user asks to download and reproduce a Kaggle notebook locally with its inputs. Read ./kernel-setup.md.

Submission

Use this when the user asks to push, poll, or submit a Kaggle kernel to a competition. Read ./submission.md.

Upload Dataset

Use this when the user wants to create or update a Kaggle dataset from local files.

bash
python ./scripts/upload_dataset.py <path-to-data-folder> [--title "My Dataset"] [--public] [--version-notes "notes"] [--dir-mode zip|tar|skip] [--collaborator user:reader]

Defaults:

  • Datasets are private unless the user explicitly asks for --public.
  • If --title is omitted, derive it from the folder name.
  • If an existing dataset-metadata.json has description, keywords, subtitle, or license fields, preserve them.
  • If the dataset already exists, do not overwrite silently; ask for or use --version-notes.

Outputs

  • Competition detail scripts print cleaned text that can be saved as markdown.
  • Discussion scripts write/read data/discussions.db and print tables, JSON, or rendered threads.
  • Dataset upload writes dataset-metadata.json in the data folder and prints the Kaggle dataset URL.
  • Referenced workflows may write markdown reports, notebook caches, local kernel workspaces, or submission logs as described in their markdown files.

Troubleshooting

Use this table for common failure modes across Kaggle workflows. Workflow files may add only narrow entries that are not covered here.

SymptomCauseAction
KAGGLE_API_TOKEN missing or invalidAPI/CLI-backed workflow started without valid Kaggle credentials.Stop before Kaggle API/CLI calls, set KAGGLE_API_TOKEN, and rerun the exact command.
Empty discussion or kernel query resultsThe local cache has not been populated for that competition.Run the matching ingest script first, then query again.
Private, restricted, or unavailable Kaggle contentThe active account lacks access, rules were not accepted, or the content was removed.Report the URL/ref and ask the user for access context before retrying.
Kaggle API, SDK, rate-limit, or page-structure failureKaggle returned partial data, changed an API/layout, or limited requests.Preserve the failing command and output, keep retries bounded, and label unavailable evidence.
Disk space or archive extraction failureCompetition data, kernel inputs, models, or extracted archives exceed local capacity or extraction failed.Stop, report the partial workspace state, and ask before deleting files or retrying.
Submission retry or uncertain submission statusA successful submit can spend a competition submission slot.Read existing logs and require explicit user intent before rerunning a submission workflow.

Runtime Compatibility

This skill works with any agent runtime that follows the Agent Skills convention. Codex uses the repository checkout or plugin installation, Claude Code uses marketplace plugin installation, and Claude Agent SDK can load the same project-scoped plugin settings. Scripts are self-contained under this skill's scripts/ directory.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nvidia Kaggle Skill AI skill do?

Use for Kaggle competition overview fetches, writeups, discussion/kernel research, submissions, and dataset uploads. Not for unrelated ML code.

Why use Nvidia Kaggle Skill on TypingMind?

Because you install it once and use it with any model. Nvidia Kaggle Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nvidia Kaggle Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA/nvidia-kaggle/tree/main/skills/nvidia-kaggle-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nvidia Kaggle Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nvidia Kaggle Skill?

As many as you like. As long as a model supports skills, you can use Nvidia Kaggle Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nvidia Kaggle Skill AI skill free?

Yes. It is published on GitHub by NVIDIA under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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