Which programming languages can it generate prototypes in?

It intelligently selects the optimal language based on the content domain, with primary support for Python, JavaScript/TypeScript, Rust, and Go.

Can it handle complex mathematical equations in papers?

Yes, it uses specialized extraction techniques to identify LaTeX and mathematical notations, translating them into executable algorithmic logic.

What input formats does the Article-to-Prototype skill support?

The skill can process a wide range of formats including academic PDFs, web-based blog posts, Jupyter Notebooks (.ipynb), and standard Markdown files.

Does it create a complete project or just code snippets?

It generates a full project structure, including environment configuration (like requirements.txt or package.json), test suites, and comprehensive documentation.

Article-to-Prototype

Name: Article-to-Prototype
Author: FrancyJGLisboa

byFrancyJGLisboa

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151

Learning & Documentation

Extracts technical implementation details from articles, papers, and documentation to generate functional, production-ready code prototypes.

About

The Article-to-Prototype skill bridges the gap between theoretical documentation and executable software by autonomously parsing technical content from diverse sources. Whether dealing with complex academic papers, technical blog posts, or Jupyter notebooks, this skill uses advanced semantic analysis to identify algorithms and architectural patterns. It intelligently selects the most appropriate programming language—such as Python for ML or Rust for systems—and generates a complete, documented repository including dependencies and unit tests, enabling developers to validate new concepts in minutes rather than days.

Key Features

Semantic algorithm detection that translates pseudocode and math into logic
AgentDB integration for context-aware learning and traceability
Multi-format extraction supporting PDFs, HTML, Jupyter Notebooks, and Markdown
Full project generation with dependency management, tests, and READMEs
Intelligent language selection based on domain-specific best practices
151 GitHub stars

Use Cases

Converting technical tutorial blog posts into functional proof-of-concept repositories
Rapidly prototyping new system architectures described in technical documentation
Implementing complex algorithms directly from academic research papers on arXiv

About

Key Features

Semantic algorithm detection that translates pseudocode and math into logic
AgentDB integration for context-aware learning and traceability
Multi-format extraction supporting PDFs, HTML, Jupyter Notebooks, and Markdown
Full project generation with dependency management, tests, and READMEs
Intelligent language selection based on domain-specific best practices
151 GitHub stars

Use Cases

Converting technical tutorial blog posts into functional proof-of-concept repositories
Rapidly prototyping new system architectures described in technical documentation
Implementing complex algorithms directly from academic research papers on arXiv