agentic-kaggle-skill
agent
Uyari
Health Uyari
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 6 GitHub stars
Code Uyari
- Code scan incomplete — No supported source files were scanned during light audit
Permissions Gecti
- Permissions — No dangerous permissions requested
Purpose
This tool provides an AI agent-driven workflow for participating in Kaggle data science competitions. It automates notebook analysis, submission troubleshooting, and score monitoring by acting as an autonomous collaborator for the user.
Security Assessment
The installation process relies on piping a remote curl script directly into the file system, which is a common but inherently risky practice since it executes commands without prior manual review. As an autonomous agent framework, it is explicitly designed to execute shell commands, manage local files, and interact with the Kaggle API via network requests. The automated scan noted no hardcoded secrets or explicitly dangerous permissions in its manifest. However, because the automated code scanner could not parse the underlying source files, a manual review of the exact script logic is impossible through this automated audit. Overall risk is rated as Medium due to the extensive system access required to function.
Quality Assessment
The project is very new and currently has low visibility with only 6 GitHub stars. It uses the permissive MIT license and shows active development, with its most recent push occurring today. Because of the low community adoption, the tool has not been widely vetted by a large audience, meaning unknown bugs or unstable behaviors might be present.
Verdict
Use with caution: the utility shows active maintenance and legitimate goals, but low community trust and a lack of visible source code scanning mean you should manually inspect the downloaded files before letting the agent execute automated tasks on your system.
This tool provides an AI agent-driven workflow for participating in Kaggle data science competitions. It automates notebook analysis, submission troubleshooting, and score monitoring by acting as an autonomous collaborator for the user.
Security Assessment
The installation process relies on piping a remote curl script directly into the file system, which is a common but inherently risky practice since it executes commands without prior manual review. As an autonomous agent framework, it is explicitly designed to execute shell commands, manage local files, and interact with the Kaggle API via network requests. The automated scan noted no hardcoded secrets or explicitly dangerous permissions in its manifest. However, because the automated code scanner could not parse the underlying source files, a manual review of the exact script logic is impossible through this automated audit. Overall risk is rated as Medium due to the extensive system access required to function.
Quality Assessment
The project is very new and currently has low visibility with only 6 GitHub stars. It uses the permissive MIT license and shows active development, with its most recent push occurring today. Because of the low community adoption, the tool has not been widely vetted by a large audience, meaning unknown bugs or unstable behaviors might be present.
Verdict
Use with caution: the utility shows active maintenance and legitimate goals, but low community trust and a lack of visible source code scanning mean you should manually inspect the downloaded files before letting the agent execute automated tasks on your system.
🤖 AI Agent-driven Kaggle competition workflow. Battle-tested patterns for score stabilization, submission troubleshooting, kernel workflows, and spec-driven development.
README.md
🤖 Agentic Data Science Competition
🤖 智能体驱动的数据科学竞赛
Turn AI Agents into Kaggle Teammates
让 AI 智能体成为你的 Kaggle 队友
Not just tools — autonomous collaborators that research, debug, iterate, and win.
不仅仅是工具 —— 能够自主研究、调试、迭代并获胜的合作者。
Quick Start • Key Insights • Case Studies • Install
Distilled from real Kaggle competition experience
提炼自 真实 Kaggle 竞赛实战经验
Including: RL Game AI • Audio Classification • LLM Reasoning • Multiple debugging journeys
包括：强化学习游戏 AI • 音频分类 • LLM 推理 • 多次调试实践
🎯 What This Skill Does / 这个 Skill 能做什么
Transform your Kaggle workflow from manual iteration to autonomous agent-driven competition:
将你的 Kaggle 工作流从手动迭代转变为智能体驱动竞赛：
| Before 之前 | After 之后 |
|---|---|
| Manual notebook analysis 手动分析 notebook | Agent pulls top solutions with dependencies 智能体拉取顶级方案及依赖 |
| Guess why submission failed 猜测提交失败原因 | Agent diagnoses 400 errors, zip format issues 智能体诊断错误 |
| Wait and refresh for scores 刷新等待分数 | Cronjob monitors kernel, auto-submits Cronjob 监控，自动提交 |
| Try random improvements 随机尝试改进 | Spec-driven development → delegate → verify 规范驱动开发 |
⚡ Quick Start / 快速开始
# Install the skill / 安装 skill
mkdir -p ~/.hermes/skills/data-science/agentic-kaggle/
curl -sL https://raw.githubusercontent.com/FrankS-IntelLab/agentic-kaggle-skill/main/SKILL.md \
-o ~/.hermes/skills/data-science/agentic-kaggle/SKILL.md
Then in your AI agent / 然后在你的 AI 智能体中：
> Use the agentic kaggle skill to help me replicate this top notebook
> Why is my submission returning 400 error?
> Set up auto-monitoring for my kernel
💡 Key Insights / 核心洞察
2️⃣ Competition Types Matter / 竞赛类型很重要
| Type 类型 | Submit 提交内容 | Detection 检测方法 |
|---|---|---|
| Answer 答案 | CSV predictions CSV 预测 | Most competitions 大多数竞赛 |
| Model 模型 | LoRA/checkpoints LoRA/检查点 | Top notebooks train models 顶级 notebook 训练模型 |
3️⃣ Kernel Mode Trap / Kernel 模式陷阱
| Mode 模式 | Test Set 测试集 | Result 结果 |
|---|---|---|
kaggle kernels push |
❌ Hidden 隐藏 | Invalid submission 无效提交 |
| "Save & Run All" | ✅ Mounted 挂载 | Valid submission 有效提交 |
4️⃣ Top Replication Workflow / 顶级方案复制流程
# Pull WITH metadata (-m is critical!) / 带 metadata 拉取（-m 很关键！）
kaggle kernels pull user/top-notebook -p ./solution/ -m
# Edit only id/title, KEEP all dependencies / 只修改 id/title，保留所有依赖
kaggle kernels push -p ./solution/
📊 Case Studies / 案例研究
RL Strategy Game Competition / RL 策略游戏竞赛
| Lesson 教训 | Details 详情 |
|---|---|
| Feature completeness 功能完整性 | Top agents: 3,000+ lines → LB 1200+ 顶级智能体：3000+ 行 |
| Simplified agents 简化版 | ~120 lines, 4/12 features → LB 500-600 简化版：~120 行 |
| Time budget 时间预算 | Strict turn limits — profile after each change 严格回合限制 |
Audio Classification Competition / 音频分类竞赛
| Lesson 教训 | Details 详情 |
|---|---|
| Hybrid ensemble 混合集成 | Temporal model + SED ensemble = Top scores 时序模型 + SED 集成 |
| Prior limitations 先验局限 | Location priors don't help when all samples are similar 样本相似时先验无效 |
| Silent failures 静默失败 | Log exceptions during feature extraction 记录异常 |
🛠️ Troubleshooting Cheat Sheet / 故障排除速查表
| Problem 问题 | Solution 解决方案 |
|---|---|
400 Bad Request |
Try .zip format (only zip the CSV!) 尝试 .zip 格式 |
FileNotFoundError |
Check /kaggle/input/competitions/<name>/ 检查路径 |
| Training IDs in submission 提交包含训练 ID | Use sample_submission.csv for fallback 使用 sample_submission.csv |
| Score dropped 分数下降 | Wait 4h for stabilization 等待 4 小时稳定 |
| GPU OOM | Use 4-bit quantization 使用 4-bit 量化 |
CUDA error |
FP16 → load_in_4bit=True |
📦 Installation / 安装
Option 1: Direct Copy / 方式 1：直接复制
mkdir -p ~/.hermes/skills/data-science/agentic-kaggle/
cp SKILL.md ~/.hermes/skills/data-science/agentic-kaggle/
Option 2: Claude Code CLI / 方式 2：Claude Code CLI
npx skills add FrankS-IntelLab/agentic-kaggle-skill
Option 3: Manual Download / 方式 3：手动下载
Download SKILL.md and place in your skills directory.
下载 SKILL.md 并放入你的 skills 目录。
📁 Repository Structure / 仓库结构
agentic-kaggle-skill/
├── SKILL.md # Core skill definition / 核心 skill 定义
├── README.md # This file / 本文件
├── LICENSE # MIT
├── references/research/
│ ├── 01-competition-patterns.md # Score stabilization, types
│ ├── 02-troubleshooting-guide.md # Error diagnosis
│ └── 03-automation-patterns.md # Cronjobs, delegation
└── examples/
├── rl-game-case-study.md # RL game competition
└── audio-classification-case-study.md # Audio classification
🧠 Mental Models / 心智模型
| Model 模型 | Description 描述 |
|---|---|
| Score Stabilization 分数稳定 | Early scores lie — wait 4h for truth 早期分数会骗人 |
| Spec-Driven Development 规范驱动开发 | Document before coding, delegate with clarity 先文档后编码 |
| Fail Fast, Learn Faster 快速失败，快速学习 | Systematic debugging beats random iteration 系统化调试 |
| Agent as Teammate 智能体即队友 | Not just a tool — an autonomous collaborator 不仅是工具 |
🔗 Related Skills / 相关 Skills
| Skill | Purpose 用途 |
|---|---|
agentic-competition-workflow |
Git-first project management, validation pipelines Git 项目管理，验证流程 |
kaggle-auto-submit |
End-to-end automation with cronjob 端到端自动化 |
autonomous-iteration |
ANALYSIS → BUILD → EXPERIMENT → REVIEW 分析→构建→实验→审查 |
opencode |
Delegate coding to OpenCode CLI 委托编码 |
claude-code |
Delegate coding to Claude Code CLI 委托编码 |
⭐ Why Star This Repo? / 为什么 Star？
- ✅ Battle-tested patterns from real competitions 实战验证的模式
- ✅ Bilingual documentation (English + 中文) 双语文档
- ✅ Practical troubleshooting for common Kaggle issues 实用故障排除
- ✅ Spec-driven workflow templates included 规范驱动工作流模板
- ✅ Case studies with actual LB scores 带实际 LB 分数的案例研究
🤝 Contributing / 贡献
Found a new pattern? Solved a tricky error? 发现了新模式？解决了棘手错误？
- Fork the repo / Fork 仓库
- Add your insight to
references/research/orexamples/添加你的洞察 - Submit a PR / 提交 PR
📄 License / 许可证
MIT — Use freely, modify freely, learn freely.
MIT —— 自由使用，自由修改，自由学习。
Made with 🤖 by Frank S (IntelLab)
If this skill helped you win a competition, ⭐ star the repo!
如果这个 skill 帮助你赢得了竞赛，请 ⭐ star 这个仓库！
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