Use this agent to validate ML/DL datasets, check format compatibility, generate conversion scripts, and create visualizations. Trigger proactively when the /reuse command enters Phase 4 (dataset preparation), or when a user asks to "检查数据集", "数据可视化", "转换数据格式", "验证标注", mentions dataset validation, or has data format mismatch issues. <example> Context: User needs to verify their dataset before training. user: "帮我检查一下数据集格式是否正确" assistant: "Launching data-inspector to validate your dataset..." <commentary>User asks for dataset validation, trigger data-inspector agent.</commentary> </example> <example> Context: Data format mismatch between user's data and project requirements. user: "我的数据是YOLO格式,但项目需要COCO格式" assistant: "Launching data-inspector to generate a conversion script..." <commentary>Format conversion need triggers data-inspector agent.</commentary> </example>
Use this agent to check the local machine's environment compatibility with an ML/DL project's requirements. Trigger proactively when the /reuse command enters Phase 3 (environment setup), or when a user asks to "检查环境", "搭建环境", "安装依赖", or mentions GPU/CUDA/PyTorch compatibility issues. <example> Context: User needs to set up environment for a PyTorch project. user: "帮我检查环境是否兼容这个项目" assistant: "Launching env-checker to analyze your system compatibility..." <commentary>User asks for environment check, trigger env-checker agent.</commentary> </example> <example> Context: Phase 3 of the /reuse workflow. user: "环境准备阶段" assistant: "Running environment compatibility check..." <commentary>Reuse workflow Phase 3 triggers env-checker automatically.</commentary> </example>
Use this agent to deeply research an ML/DL GitHub repository. Trigger proactively when a user provides a GitHub link for project reuse, or when the /reuse command enters Phase 2 (deep research). This agent reads DeepWiki, finds and summarizes the associated paper, analyzes code structure, scans Issues, and outputs a structured PROJECT_NOTES.md file to the project root directory. <example> Context: User provides a GitHub link for an ML project they want to reuse. user: "/reuse https://github.com/open-mmlab/mmdetection" assistant: "Starting deep research on mmdetection repository..." <commentary>Phase 2 of the reuse workflow triggers repo-researcher to analyze the project.</commentary> </example> <example> Context: User wants to understand a deep learning project before using it. user: "帮我调研一下这个项目 https://github.com/ultralytics/yolov5" assistant: "Launching repo-researcher to analyze yolov5..." <commentary>User explicitly wants project research, trigger repo-researcher agent.</commentary> </example>
Requires secrets
Needs API keys or credentials to function
Uses power tools
Uses Bash, Write, or Edit tools
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
ML/DL GitHub 项目复用助手 - 从 clone 到训练的全流程引导插件。
提供结构化的 6 阶段工作流,帮助快速复用任意 ML/DL GitHub 项目:
PROJECT_NOTES.md| 组件 | 类型 | 功能 |
|---|---|---|
ml-project-reuse | Skill | 6 阶段方法论、调研笔记规范、错误诊断表 |
/reuse | Command | 用户入口,接收 GitHub 链接,启动全流程 |
repo-researcher | Agent | 深度调研:DeepWiki + 论文 + Issues + 代码结构 → PROJECT_NOTES.md |
env-checker | Agent | 环境检测 + 兼容性分析 + OOM 预估 + 安装命令生成 |
data-inspector | Agent | 数据格式检查 + 转换脚本生成 + 可视化输出 |
/reuse https://github.com/open-mmlab/mmdetection
插件会自动引导你完成 6 个阶段,每阶段给出可直接执行的命令。
当你在对话中提到以下关键词时,相关 Skill 会自动加载:
claude --plugin-dir /path/to/github-reuse-assistant
将插件目录复制到项目的 .claude-plugin/ 下。
插件在工作过程中会生成以下文件:
| 文件 | 位置 | 用途 |
|---|---|---|
PROJECT_NOTES.md | 项目根目录 | 调研笔记,贯穿全流程的参考文档 |
data_check/ | 项目根目录 | 数据验证结果目录 |
data_check/visualize/ | 数据检查目录 | 标注可视化图像 |
data_check/class_distribution.png | 数据检查目录 | 类别分布统计图 |
data_check/anomaly_report.md | 数据检查目录 | 数据异常报告 |
data_check/convert_dataset.py | 数据检查目录 | 格式转换脚本(如需要) |
npx claudepluginhub lidapengpeng/github-reuse-assistantAutomated academic paper review system with venue-adaptive review, competitive paper analysis, rebuttal assistance, PDF parsing, reference verification, SOTA comparison, and 8-dimension scoring powered by 10 top-conference reviewer methodologies
ML/perf investigation skills: topic, plan, judge, run, sweep
Skills migrated to ProjectMnemosyne for cross-project reuse
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Set up ML experiment tracking
TensorFlow machine learning and deep learning framework skills.
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.