paper-to-pipeline根据机器学习/深度学习论文的实验规划文档自动生成完整的 Python 实验 pipeline。支持数据预处理、模型构建、训练循环、评估指标、结果可视化。Use when user uploads an experiment plan document and wants to generate runnable...
Install via ClawdBot CLI:
clawdbot install lhbzx1984/paper-to-pipelineGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Potentially destructive shell commands in tool definitions
eval(Audited Apr 16, 2026 · audit v1.0
Generated Mar 21, 2026
Researchers can upload experimental plans from papers to quickly generate runnable code for validating methods, accelerating prototyping and reproducibility in fields like computer vision or NLP.
Data science teams in companies use this to convert documented experiment designs into production-ready pipelines, reducing manual coding time for tasks like predictive maintenance or customer segmentation.
Instructors provide experiment plans as assignments, and students generate code to learn ML workflows hands-on, covering basics like image classification or regression in courses.
Startups with limited engineering resources upload simple ML experiment plans to generate initial code for proof-of-concepts, such as sentiment analysis for product feedback or time-series forecasting.
Offer tiered monthly subscriptions for individuals, teams, or enterprises, with features like advanced templates, cloud integration, and priority support, generating recurring revenue.
Provide a free basic version for generating simple pipelines, then charge for premium features like custom templates, multi-framework support, and automated deployment tools.
Sell annual licenses to large organizations for internal use, including customization, security compliance, and dedicated training, targeting industries like finance or healthcare.
💬 Integration Tip
Integrate with cloud platforms like AWS or Google Cloud for seamless deployment, and add version control hooks to track code changes.
Scored Apr 19, 2026
Control desktop applications on Windows — launch, close, focus, resize, move windows, simulate keyboard/mouse input, manage processes, control VSCode, read clipboard, and capture screen info. Use when the user wants to interact with any running program, switch windows, type text, press shortcuts, open files in VSCode, manage running processes, or get system display information.
Conduct rigorous, adversarial code reviews with zero tolerance for mediocrity. Use when users ask to "critically review" my code or a PR, "critique my code", "find issues in my code", or "what's wrong with this code". Identifies security holes, lazy patterns, edge case failures, and bad practices across Python, R, JavaScript/TypeScript, SQL, and front-end code. Scrutinizes error handling, type safety, performance, accessibility, and code quality. Provides structured feedback with severity tiers (Blocking, Required, Suggestions) and specific, actionable recommendations.
Coding style memory that adapts to your preferences, conventions, and patterns for consistent coding.
Pragmatic coding standards for writing clean, maintainable code — naming, functions, structure, anti-patterns, and pre-edit safety checks. Use when writing new code, refactoring existing code, reviewing code quality, or establishing coding standards.
Claude Code integration for OpenClaw. This skill provides interfaces to: - Query Claude Code documentation from https://code.claude.com/docs - Manage subagents and coding tasks - Execute AI-assisted coding workflows - Access best practices and common workflows Use this skill when users want to: - Get help with coding tasks - Query Claude Code documentation - Manage AI-assisted development workflows - Execute complex programming tasks
Plan, draft, version, and refine written content with enforced versioning and quality audits.