kaggle-learner

name	kaggle-learner
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.
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	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/

File structure: one markdown file per competition, organized by domain directory.

Examples:

• 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. Front-runner Detailed Technical Analysis is automatically included

To browse existing knowledge:

• Browse the relevant domain directory: references/knowledge/[domain]/
• Each competition has one file containing:
  • Competition Brief
  • Front-runner Detailed Technical Analysis ⭐
  • Code Templates
  • Best Practices

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.

Knowledge Extraction Standard

Every time knowledge is extracted from a Kaggle competition, must include the following standard sections:

Required Content Checklist

Section	Description	Required
Competition Brief	Background, task description, data scale, evaluation metric	✅ Required
Original Summaries	Brief overview of top solutions	✅ Required
Front-runner Detailed Technical Analysis	Core techniques and implementation details for top 20 solutions	✅ Required ⭐
Code Templates	Reusable code templates	✅ Required
Best Practices	Best practices and common pitfalls	✅ Required
Metadata	Data source tags and date	✅ Required

Front-runner Detailed Technical Analysis Format

Each front-runner solution should include:

• Rank and team/author
• Core techniques list (3-6 key technical points)
• Implementation details (specific parameters, configs, data)

Example format:

**Rank Place - Core Technique Name (Author)**

Core techniques:
- **Technique 1**: brief description
- **Technique 2**: brief description

Implementation details:
- Specific parameters, models, configs
- Data and experimental results

Recommended to cover Top 20 solutions to capture more innovative techniques from top competitors

Additional Resources

Knowledge Directories

• references/knowledge/nlp/ - NLP competition techniques
• references/knowledge/cv/ - Computer vision techniques
• references/knowledge/time-series/ - Time series methods
• references/knowledge/tabular/ - Tabular data approaches
• references/knowledge/multimodal/ - Multimodal solutions

Competition Examples

• BirdCLEF+ 2025 (time-series/birdclef-plus-2025.md) - Complete Top 14 front-runner detailed technical analysis
• BirdCLEF 2024 (time-series/birdclef-2024.md) - Top 3 solutions detailed technical analysis
• AIMO-2 (nlp/aimo-2-2025.md) - Top 12+ front-runner technical summary