CLI guide for interacting with the Kaggle platform.

Install

mkdir -p .claude/skills/kaggle-cli && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18027" && unzip -o skill.zip -d .claude/skills/kaggle-cli && rm skill.zip

Installs to .claude/skills/kaggle-cli

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use the local Kaggle CLI skill for command guidance, workflows, and troubleshooting across competitions, datasets, kernels/notebooks, models, model variations and versions, inbox file uploads, forums/discussions, benchmarks, configuration, OAuth/API-token authentication, and accelerator quota. Activate this skill when the user asks about kaggle CLI commands, examples, flags, metadata files, download/upload flows, submissions, benchmark tasks, or Kaggle CLI behavior.
470 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Beginner

Key capabilities

  • List Kaggle competitions, datasets, kernels, models, and forums
  • Download files for competitions and datasets
  • Push updates to Kaggle kernels/notebooks
  • Create and manage model variations and versions
  • Upload files to Kaggle inbox
  • Authenticate with Kaggle using API tokens or OAuth

How it works

The Kaggle CLI provides a command-line interface to interact with various Kaggle services, including competitions, datasets, kernels, and models. It uses authentication methods like API tokens or OAuth to secure access.

Inputs & outputs

You give it
kaggle datasets download -d 'user/dataset-name'
You get back
Dataset files downloaded to the local machine

When to use kaggle-cli

  • Download kaggle datasets
  • Push notebook updates
  • Submit to competition

About this skill

Kaggle CLI

Use this skill to answer or operate on the kaggle command-line tool. Treat this skill and its references as the available command guide.

Quick Start

pip install kaggle
kaggle --help

Authentication options:

kaggle auth login
# or set KAGGLE_API_TOKEN
# or place an access token in ~/.kaggle/access_token
# or use legacy ~/.kaggle/kaggle.json credentials

Command Tree

kaggle
├── competitions | c
│   ├── list, files, download, submit, submissions, leaderboard
│   ├── team-submissions, episodes, replay, logs, pages
│   └── topics {list, show}, topic-messages
├── datasets | d
│   ├── list, files, download, init, create, version
│   ├── metadata, status, delete
│   └── topics {list, show}
├── kernels | k
│   └── list, files, init, push|update, pull|get, output, status, logs, delete
├── models | m
│   ├── list, init, create, get, update, delete
│   ├── topics {list, show}
│   └── variations | instances | v | i
│       ├── get, init, create, files, list, update, delete
│       └── versions | v {list, create, download, files, delete}
├── files {upload}
├── forums | f {list, topics {list, show}}
├── benchmarks | b
│   ├── auth, init
│   ├── tasks | t {push, run, list, status, download, log|logs, models, delete, publish}
│   └── topics {list, show}
├── config {view, set, unset}
├── auth {login, print-access-token, revoke}
├── quota
└── search

Note: the CLI accepts aliases such as kernels get for kernels pull and kernels update for kernels push. Do not recommend models variations versions init; use models variations init for variation metadata instead.

Reference Map

Read only the reference needed for the user's task:

  • Competitions - competition discovery, files, downloads, submissions, leaderboards, simulations, pages, topics.
  • Datasets - dataset search, files, downloads, metadata, create/version/status/delete, topics.
  • Kernels - notebook/script discovery, metadata, push/pull, outputs, status, logs, delete.
  • Models - model records, metadata, create/get/update/delete, model topics.
  • Model Variations - create and manage framework-specific model variations.
  • Model Variation Versions - create, list, download, inspect, and delete variation versions.
  • Files - inbox uploads, resumable uploads, directory compression behavior.
  • Forums - global discussion forums, topics, and comments.
  • Benchmarks - benchmark auth/init, task push/run/status/download/log/model flows, benchmark topics.
  • Configuration - config file, default path, proxy, default competition.
  • Authentication - OAuth login, access token printing, revocation, token/key sources.
  • Quota - weekly GPU/TPU accelerator quota.
  • Search - unified cross-content search over competitions, datasets, notebooks, models, users, and discussions.

Operating Guidance

  • Read only the reference needed for the user's task.
  • Prefer kaggle <group> --help or kaggle <group> <command> --help when a flag is uncertain in the installed CLI.
  • For metadata files, prefer the relevant init command to generate a starter file before editing it.
  • Do not invent commands that are not listed in this skill. If live --help output differs, report it as a version-specific difference.

When not to use it

  • When inventing commands not listed in the skill
  • When using `models variations versions init` instead of `models variations init`
  • When the user asks about a command that is not part of the `kaggle` command-line tool

Prerequisites

pip install kaggle

Limitations

  • The CLI accepts aliases such as `kernels get` for `kernels pull`
  • Do not recommend `models variations versions init`
  • Do not invent commands that are not listed in this skill

How it compares

The Kaggle CLI offers a programmatic way to manage Kaggle resources, automating tasks that would otherwise require manual interaction through the Kaggle website.

Compared to similar skills

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kaggle-cli (this skill)018dReviewBeginner
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umap-learn61moReviewIntermediate
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