Kaggle — Unified Skill

Complete Kaggle integration for any LLM or agentic coding system (Claude Code, gemini-cli, Cursor, etc.): account setup, competition reports, dataset/model downloads, notebook execution, competition submissions, badge collection, and general Kaggle questions. Four integrated modules working together.

Overlap guard: For hackathon grading evaluation and alignment analysis, use the kaggle-hackathon-grading skill instead.

Network requirements: outbound HTTPS to

api.kaggle.com

www.kaggle.com

, and

storage.googleapis.com

Modules

Module	Purpose
registration	Account creation, API key generation, credential storage
comp-report	Competition landscape reports with Playwright scraping
kllm	Core Kaggle interaction (kagglehub, CLI, MCP, UI)
badge-collector	Systematic badge earning across 5 phases

Credential Setup

Always run the credential checker first:

bash

python3 skills/kaggle/shared/check_all_credentials.py

Three credential types are needed for full compatibility:

Variable	Format	Purpose
`KAGGLE_USERNAME`	Kaggle handle	Identity for all tools
`KAGGLE_KEY`	32-char hex	Legacy key (CLI, kagglehub, most MCP)
`KAGGLE_API_TOKEN`	`KGAT_` -prefixed	Scoped token (some MCP endpoints)

If any are missing, follow the registration walkthrough:

Read modules/registration/README.md

for the full step-by-step guide.

Security: Never echo, log, or commit actual credential values.

Module: Registration

Walks users through creating a Kaggle account and generating all three API credentials. Saves to

.env

and

~/.kaggle/kaggle.json

Key commands:

bash

python3 skills/kaggle/modules/registration/scripts/check_registration.py
bash skills/kaggle/modules/registration/scripts/setup_env.sh

Read modules/registration/README.md

for the complete walkthrough.

Module: Competition Reports

Generates comprehensive landscape reports of recent Kaggle competition activity. Uses Python API for metadata + Playwright MCP tools for SPA content.

6-step workflow:

Verify credentials
Gather competition list across all categories
Get structured details per competition (files, leaderboard, kernels)
Scrape problem statements, evaluation metrics, writeups via Playwright
Compose markdown report with Methods & Insights analysis
Present inline

bash

python3 skills/kaggle/modules/comp-report/scripts/list_competitions.py --lookback-days 30 --output json
python3 skills/kaggle/modules/comp-report/scripts/competition_details.py --slug SLUG

Read modules/comp-report/README.md

for full details including hackathon handling.

Module: Kaggle Interaction (kllm)

Four methods to interact with kaggle.com:

Method	Best For
kagglehub	Quick dataset/model download in Python
kaggle-cli	Full workflow scripting
MCP Server	AI agent integration
Kaggle UI	Account setup, verification

Capability matrix:

Task	kagglehub	kaggle-cli	MCP	UI
Download dataset	`dataset_download()`	`datasets download`	Yes	Yes
Download model	`model_download()`	`models instances versions download`	Yes	Yes
Execute notebook	—	`kernels push/status/output`	Yes	Yes
Submit to competition	—	`competitions submit`	Yes	Yes
Publish dataset	`dataset_upload()`	`datasets create`	Yes	Yes
Publish model	`model_upload()`	`models create`	Yes	Yes

Known issues:

dataset_load()

broken in kagglehub v0.4.3 — use

dataset_download()

pd.read_csv()

```
competitions download
```
has no
```
--unzip
```
in CLI >= 1.8
Competition-linked datasets return 403 — use standalone copies

Read modules/kllm/README.md

for full details and all task workflows.

Module: Badge Collector

Systematically earns ~38 automatable Kaggle badges across 5 phases:

Phase	Name	Badges	Time
1	Instant API	~16	5-10 min
2	Competition	~7	10-15 min
3	Pipeline	~3	15-30 min
4	Browser	~8	5-10 min
5	Streaks	~4	Setup only

bash

python3 skills/kaggle/modules/badge-collector/scripts/orchestrator.py --dry-run
python3 skills/kaggle/modules/badge-collector/scripts/orchestrator.py --phase 1
python3 skills/kaggle/modules/badge-collector/scripts/orchestrator.py --status

Read modules/badge-collector/README.md

for full details.

Orchestration Workflow

This skill is primarily a reference — use the modules and scripts as needed based on the user's request. When explicitly asked to run the full Kaggle workflow, follow these steps:

Step 1: Check Credentials

bash

python3 skills/kaggle/shared/check_all_credentials.py

If any credentials are missing, walk through the registration module. Never echo or log actual credential values.

Step 2: Generate Competition Landscape Report

Run the comp-report workflow: list competitions, get details, scrape with Playwright, compose report. Output inline.

Step 3: Summarize Kaggle Interaction Methods

Present a concise summary of the four ways to interact with Kaggle (kagglehub, kaggle-cli, MCP Server, UI) with the capability matrix from the kllm module.

Step 4: Present Interactive Menu

Ask the user what they'd like to do next:

Earn Kaggle badges — Run the badge collector (5 phases, ~38 automatable badges)
Explore recent competitions — Dive deeper into specific competitions from the report
Enter a Kaggle competition — Register, download data, build a submission, submit
Download a Kaggle dataset — Search for and download any public dataset
Download a Kaggle model — Download pre-trained models (LLMs, CV, etc.)
Run a notebook on Kaggle — Push and execute a notebook on KKB with free GPU/TPU
Publish to Kaggle — Upload a dataset, model, or notebook
Learn about Kaggle progression — Tiers, medals, how to rank up
Something else — Free-form Kaggle help

Step 5: Execute and Continue

Handle the user's choice using the appropriate module, then loop back to offer more options.

Security

Never commit
```
.env
```
,
```
kaggle.json
```
, or any credential files
Never echo or log actual credential values in terminal output
The
```
.gitignore
```
excludes
```
.env
```
,
```
kaggle.json
```
, and related files
Set file permissions:
```
chmod 600 .env ~/.kaggle/kaggle.json
```
If credentials are accidentally exposed, rotate them immediately at https://www.kaggle.com/settings

Scope of Operations

This skill performs both read-only and write operations on kaggle.com.

Read-only operations (no account side-effects):

List/search competitions, datasets, models, notebooks
Download datasets, models, competition data
View leaderboards, competition details, badge progress
Generate competition landscape reports

Write operations (create or modify resources on your account):

Create/publish datasets, notebooks, models (always private by default)
Submit predictions to competitions
Push and execute notebooks on Kaggle Kernel Backend (KKB)
Earn badges through API activity (profile-visible)

Phase 5 (Streaks) generates a local shell script for daily execution but does not auto-install cron jobs or launchd plists. Users must manually configure scheduling if desired.

Scripts Index

Shared:

```
shared/check_all_credentials.py
```
— Unified credential checker (all 3 types)

Registration:

modules/registration/scripts/check_registration.py

— Check all 3 credentials

```
modules/registration/scripts/setup_env.sh
```
— Auto-configure credentials from env/dotenv

Competition Reports:

```
modules/comp-report/scripts/utils.py
```
— Credential check, API init, rate limiting

modules/comp-report/scripts/list_competitions.py

— Fetch competitions across categories

modules/comp-report/scripts/competition_details.py

— Files, leaderboard, kernels per competition

Kaggle Interaction (kllm):

```
modules/kllm/scripts/setup_env.sh
```
— Auto-configure credentials (with .env loading)

modules/kllm/scripts/check_credentials.py

— Verify and auto-map credentials

```
modules/kllm/scripts/network_check.sh
```
— Check Kaggle API reachability
```
modules/kllm/scripts/cli_download.sh
```
— Download datasets/models via CLI
```
modules/kllm/scripts/cli_execute.sh
```
— Execute notebook on KKB
```
modules/kllm/scripts/cli_competition.sh
```
— Competition workflow (list/download/submit)
```
modules/kllm/scripts/cli_publish.sh
```
— Publish datasets/notebooks/models
```
modules/kllm/scripts/poll_kernel.sh
```
— Poll kernel status and download output

modules/kllm/scripts/kagglehub_download.py

— Download via kagglehub

modules/kllm/scripts/kagglehub_publish.py

— Publish via kagglehub

Badge Collector:

modules/badge-collector/scripts/orchestrator.py

— Main entry point

modules/badge-collector/scripts/badge_registry.py

— 59 badge definitions

modules/badge-collector/scripts/badge_tracker.py

— Progress persistence

modules/badge-collector/scripts/utils.py

— Shared utilities

modules/badge-collector/scripts/phase_1_instant_api.py

— Instant API badges

modules/badge-collector/scripts/phase_2_competition.py

— Competition badges

modules/badge-collector/scripts/phase_3_pipeline.py

— Pipeline badges

modules/badge-collector/scripts/phase_4_browser.py

— Browser badges

modules/badge-collector/scripts/phase_5_streaks.py

— Streak automation

References Index

```
modules/registration/references/kaggle-setup.md
```
— Full credential setup guide with troubleshooting

modules/comp-report/references/competition-categories.md

— Competition types and API mapping

modules/kllm/references/kaggle-knowledge.md

— Comprehensive Kaggle platform knowledge

modules/kllm/references/kagglehub-reference.md

— Full kagglehub Python API reference

```
modules/kllm/references/cli-reference.md
```
— Complete kaggle-cli command reference

modules/kllm/references/mcp-reference.md

— Kaggle MCP server reference

modules/badge-collector/references/badge-catalog.md

— Complete 59-badge catalog

kaggle

NPX Install

Tags

SKILL.md Content

Kaggle — Unified Skill

Modules

Credential Setup

Module: Registration

Module: Competition Reports

Module: Kaggle Interaction (kllm)

Module: Badge Collector

Orchestration Workflow

Step 1: Check Credentials

Step 2: Generate Competition Landscape Report

Step 3: Summarize Kaggle Interaction Methods

Step 4: Present Interactive Menu

Step 5: Execute and Continue

Security

Scope of Operations

Scripts Index

References Index