Receber por email
← Voltar para o catálogo

kaggle

Unified Kaggle skill. Use when the user mentions kaggle, kaggle.com, Kaggle competitions, datasets, models, notebooks, GPUs, TPUs, hackathons, writeups, badges, or anything Kaggle-related. Handles account setup, competition reports, dataset/model downloads, notebook execution, competition submissions, hackathon writeup retrieval, badge collection, and general Kaggle questions.

66estrelas
Atualizado há 5 meses

Ver no GitHub ↗ Licença: MIT

Como adicionar

/plugin marketplace add shepsci/kaggle-skill

O comando exato pode variar conforme o repositório. Confira o README no GitHub.

Para o autor da skill

Cole no README do seu repo

Mostra que sua skill está catalogada na Skillteca, gera backlink e tráfego rastreável.

[![Skillteca](https://www.skillteca.com.br/api/badge/kaggle/svg)](https://www.skillteca.com.br/skills/kaggle)
#ai

Skills relacionadas

Ver todas de Dados e Análise →

Alerta por categoria

Receba novas skills de Dados e Análise toda segunda

1 email curto, só com as skills novas de Dados e Análise. 4 minutos de leitura, sem spam, cancela com 1 clique.

Você confirma o email no primeiro envio. Sem spam. Cancela com 1 clique.

Ler descrição completa

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, hackathon writeup retrieval, badge collection, and general Kaggle questions. Five integrated modules working together.

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 (Python API + optional Playwright via host agent) | | kllm | Core Kaggle interaction (kagglehub, CLI, MCP) — includes the hackathon/ submodule for writeup retrieval and overview/rubric extraction | | badge-collector | Systematic badge earning across 5 phases |

Credential Setup

Always run the credential checker first:

python3 shared/check_all_credentials.py

Primary credential (recommended):

| Variable | How to Get | Purpose | |----------|------------|---------| | KAGGLE_API_TOKEN | "Generate New Token" at kaggle.com/settings | Works with CLI (>= 1.8.0), kagglehub (>= 0.4.1), MCP |

Legacy credentials (optional, for older tools):

| Variable | How to Get | Purpose | |----------|------------|---------| | KAGGLE_USERNAME | Account creation | Identity (auto-detected from token) | | KAGGLE_KEY | "Create Legacy API Key" at kaggle.com/settings | Legacy key for older CLI/kagglehub versions |

Store your API token in ~/.kaggle/access_token (recommended) or as an env var. 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 API credentials (API token as primary, legacy key as optional). Saves to ~/.kaggle/access_token and optionally .env and ~/.kaggle/kaggle.json.

Key commands:

python3 modules/registration/scripts/check_registration.py
bash 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; SPA-only content (problem statement, rendered evaluation details, winner writeup links) requires the host agent to provide Playwright MCP tools — the skill itself does not bundle them. For most overview content, prefer list_competition_pages in the kllm module (no Playwright required).

6-step workflow:

  1. Verify credentials
  2. Gather competition list across all categories
  3. Get structured details per competition (files, leaderboard, kernels)
  4. Scrape problem statements, evaluation metrics, writeups via Playwright
  5. Compose markdown report with Methods & Insights analysis
  6. Present inline
python3 modules/comp-report/scripts/list_competitions.py --lookback-days 30 --output json
python3 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.

Sub-module: kllm/hackathon

Retrieves hackathon writeups, rules, and judging rubrics from Kaggle's MCP hackathon endpoints. Lives under kllm because it's a focused MCP-workflow surface like the rest of kllm. Built around the endpoint order from the 2026-04-22 audit (retested 2026-05-04):

  1. get_hackathon_overview — rules, eligibility, rubric, prizes
  2. list_hackathon_write_ups — submission roster (paginated, with track ids)
  3. list_hackathon_tracks — resolve numeric track ids to titles
  4. get_writeup — preferred full-body fetch (simpler arg shape than

get_hackathon_write_up)

  1. get_writeup_by_topic / get_writeup_by_slug — fallbacks when id missing
  2. get_resolved_writeup_links — host/judge-gated link enrichment
python3 modules/kllm/hackathon/scripts/hackathon_overview.py --competition kaggle-measuring-agi
python3 modules/kllm/hackathon/scripts/list_writeups.py --competition kaggle-measuring-agi
python3 modules/kllm/hackathon/scripts/fetch_writeup.py --writeup-id 123456

Live-server status (verified 2026-05-04):

  • get_hackathon_write_up — was broken in the 2026-04-22 audit, now works.
  • get_benchmark_leaderboard — was permission-blocked in 2026-04-22, now PASS for ordinary KGAT tokens.
  • get_competition for classic competitions — now PASS (recovered upstream).
  • download_hackathon_write_ups may return CSV header only in some host contexts.
  • get_resolved_writeup_links is role-gated; participants get an explicit denial.

Read modules/kllm/hackathon/README.md for the full retrieval workflow, role-specific guidance (host/judge vs. participant), and the bundle shape returned to the agent.

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 |

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

python3 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
Compartilhar X LinkedIn