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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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+ 前排方案技术总结まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
VS-Enhanced Research Question Refiner - Prevents Mode Collapse and derives differentiated research questions Enhanced VS 3-Phase process: Modal question avoidance, alternatives presentation, differentiated RQ recommendation Use when: refining research ideas, formulating research questions, clarifying scope Triggers: research question, 연구 질문, PICO, SPIDER, research idea
日本語の概要は準備中です。原文の説明を表示しています。
VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities Use when: building theoretical foundations, designing conceptual models, deriving hypotheses, critiquing frameworks, visualizing models Triggers: theoretical framework, 이론적 프레임워크, conceptual model, 개념적 모형, hypothesis derivation, critique, devil's advocate, 반론, visualization, diagram
日本語の概要は準備中です。原文の説明を表示しています。
Agent A5 - Paradigm & Worldview Advisor - Philosophical foundations for research design. Covers ontology, epistemology, axiology, and methodology alignment. Use when: establishing philosophical foundations, justifying methodological choices, writing positionality statements Triggers: paradigm, 패러다임, ontology, epistemology, worldview, 세계관, philosophical foundations, 철학적 기초
日本語の概要は準備中です。原文の説明を表示しています。
Academic paper writing skill with 12-agent pipeline. v2.5: Style Calibration (learn author's writing voice from past papers) + Writing Quality Check (writing quality checklist for natural prose). Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, and Socratic guided modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review.
日本語の概要は準備中です。原文の説明を表示しています。