# Kaggle Learner

Should be used when the user asks to "learn from Kaggle", "study Kaggle solutions"...

- Repository: https://github.com/Galaxy-Dawn/claude-scholar (5959 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Galaxy-Dawn/claude-scholar --skill kaggle-learner -g -a claude-code -y`
Codex: `npx skills add Galaxy-Dawn/claude-scholar --skill kaggle-learner -g -a codex -y`
Cursor: `npx skills add Galaxy-Dawn/claude-scholar --skill kaggle-learner -g -a cursor -y`
Gemini CLI: `npx skills add Galaxy-Dawn/claude-scholar --skill kaggle-learner -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Galaxy-Dawn/claude-scholar`

## Description

This skill should be used when the user asks to "learn from Kaggle", "study Kaggle solutions", "analyze Kaggle competitions", or mentions Kaggle competition URLs. Provides access to extracted knowledge from winning Kaggle solutions across NLP, CV, time series, tabular, and multimodal domains.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Kaggle Learner

Extract and apply knowledge from Kaggle competition winning solutions. This skill provides access to a continuously updated knowledge base of techniques, code patterns, and best practices from top Kaggle competitors.

## Overview

Kaggle competitions are at the forefront of practical machine learning. Winning solutions often innovate with novel techniques, clever feature engineering, and optimized pipelines. This skill captures that knowledge and makes it accessible for your projects.

## When to Use

Use this skill when:
- Studying for a Kaggle competition
- Looking for proven techniques in a specific domain (NLP, CV, etc.)
- Need code templates for common ML tasks
- Want to learn from competition winners

## Knowledge Categories

| Category | Focus | Directory |
|----------|-------|-----------|
| **NLP** | Text classification, NER, translation, LLM applications | `references/knowledge/nlp/` |
| **CV** | Image classification, detection, segmentation, generation | `references/knowledge/cv/` |
| **Time Series** | Forecasting, anomaly detection, sequence modeling | `references/knowledge/time-series/` |
| **Tabular** | Feature engineering, traditional ML, structured data | `references/knowledge/tabular/` |
| **Multimodal** | Cross-modal tasks, vision-language models | `references/knowledge/multimodal/` |

**文件组织结构**：每个竞赛一个独立的 markdown 文件，按 domain 分类到对应目录。

示例：
- `time-series/birdclef-plus-2025.md`
- `nlp/aimo-2-2025.md`

## Quick Reference

**To learn from a competition:**
1. Provide the Kaggle competition URL
2. The kaggle-miner agent will extract the winning solution
3. Knowledge is automatically added to the relevant category
4. **前排方案详细技术分析** (Front-runner Detailed Technical Analysis) is automatically included

**To browse existing knowledge:**
- 浏览相关 domain 目录：`references/knowledge/[domain]/`
- 每个竞赛一个独立文件，包含：
  - Competition Brief (竞赛简介)
  - **前排方案详细技术分析** (前排方案详细技术分析) ⭐
  - Code Templates (代码模板)
  - Best Practices (最佳实践)

## Self-Evolving

The rest: https://github.com/Galaxy-Dawn/claude-scholar/blob/HEAD/skills/kaggle-learner/SKILL.md