Learn from top Kaggle solutions across text, images, time series, tabular data, and multimodal tasks that combine data types. Explore technical analyses, model settings, reusable code templates, and practical lessons to inform machine learning projects.
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.
Review the instructions the agent will receive before you install.
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.
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.
Use this skill when:
| 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.mdnlp/aimo-2-2025.mdTo learn from a competition:
To browse existing knowledge:
references/knowledge/[domain]/This skill automatically updates its knowledge base when the kaggle-miner agent processes new competitions. The more you use it, the smarter it becomes.
每次从 Kaggle 竞赛提取知识时，必须包含以下标准部分：
| 部分 | 说明 | 必需性 |
|---|---|---|
| Competition Brief | 竞赛背景、任务描述、数据规模、评估指标 | ✅ 必需 |
| Original Summaries | 前排方案的简要概述 | ✅ 必需 |
| 前排方案详细技术分析 | Top 20 方案的核心技巧和实现细节 | ✅ 必需 ⭐ |
| Code Templates | 可复用的代码模板 | ✅ 必需 |
| Best Practices | 最佳实践和常见陷阱 | ✅ 必需 |
| Metadata | 数据源标签和日期 | ✅ 必需 |
每个前排方案应包含：
示例格式：
**排名 Place - 核心技术名称 (作者)**
核心技巧：
- **技巧1**: 简短说明
- **技巧2**: 简短说明
实现细节：
- 具体参数、模型、配置
- 数据和实验结果
建议覆盖 Top 20 方案，获取更多前排选手的创新技巧
references/knowledge/nlp/ - NLP competition techniquesreferences/knowledge/cv/ - Computer vision techniquesreferences/knowledge/time-series/ - Time series methodsreferences/knowledge/tabular/ - Tabular data approachesreferences/knowledge/multimodal/ - Multimodal solutionstime-series/birdclef-plus-2025.md) - 包含完整的 Top 14 前排方案详细技术分析time-series/birdclef-2024.md) - 包含 Top 3 方案详细技术分析nlp/aimo-2-2025.md) - 包含 Top 12+ 前排方案技术总结No reviews yet. Tell others what you thought.
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