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.
Start a fresh conversation. Replace the highlighted placeholders.
Use kaggle-learner to extract applicable winning-solution techniques and code patterns for [Kaggle competition or URL] and [modeling problem].
Required inputs
[Kaggle competition or URL]
[modeling problem]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: kaggle-learnerdescription: 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.version: 0.1.0
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
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:
Provide the Kaggle competition URL
The kaggle-miner agent will extract the winning solution
Knowledge is automatically added to the relevant category
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
This skill automatically updates its knowledge base when the kaggle-miner agent processes new competitions. The more you use it, the smarter it becomes.