domain-build

Convert academic PDFs into validated, functional code repositories.

Updated May 24, 2026
One-click install
npx skills add https://github.com/angrysky56/hermes-ops --skill domain-build-angrysky56
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: domain-build
Source: https://github.com/angrysky56/hermes-ops/tree/main/skills/domain-build
Command: npx skills add https://github.com/angrysky56/hermes-ops --skill domain-build-angrysky56

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the tedious, error-prone manual work of converting academic research papers into functional, validated code, cutting down development time for paper implementations drastically.

Core Features & Use Cases

  • End-to-end Paper-to-Code Pipeline: Automatically converts PDF research papers into functional code repositories using Paper2Code-Enhanced, no manual coding required.
  • Automated Code Validation & Patching: Runs OrCAID commit0 to test, validate, and fix generated code for correctness, eliminating debugging overhead for paper implementations.
  • Meta-Harness Outcome Analysis: Reads validated code outcomes to compute frontier scores and propose Pack deltas for continuous improvement of the code generation pipeline.
  • Use Case: Research teams implementing cutting-edge ML papers, or developers building reproducible codebases from academic publications can use this Skill to go from paper PDF to production-ready, validated code in minutes.

Quick Start

Use the domain-build skill to convert the arXiv paper 'Tree of Thoughts' PDF into a validated, patched code repository and generate a meta-harness frontier score report for the implementation.

Frequently Asked Questions about domain-build

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert an academic research paper PDF into validated code automatically?

Automated paper-to-code conversion works by parsing PDF research papers to generate functional code repositories, then running validation and patching workflows to test and fix the generated code for correctness.

What is the best way to implement machine learning papers from PDF sources into a reproducible codebase?

Implementing ML papers from PDF sources is best handled by an automated pipeline that generates functional code repositories, validates correctness through automated patching, and analyzes outcomes to ensure reproducible codebases.

How does automated code validation and patching work for academic paper implementations?

Automated code validation and patching works by executing validation tests against generated code repositories, detecting correctness errors, and applying automated patches to fix implementation issues, eliminating manual debugging overhead for academic paper implementations.

Do I need to configure specific tool paths to automate paper-to-code generation and validation?

Automating paper-to-code generation and validation requires configuring specific paths for the code generation, validation, and outcome analysis tools, along with aligned orchestrator memory directories for all three integrated components.

Can I use this automated paper-to-code pipeline for building knowledge graphs from academic publications?

This automated paper-to-code pipeline supports knowledge graph builders by converting academic publications from PDF sources into functional, validated code repositories, covering the full code generation, validation, and pipeline improvement workflow.

What are the limitations of using automated paper-to-code generation for research implementation?

Limitations of automated paper-to-code generation include the strict requirement for properly configured paths across the three integrated tools and aligned orchestrator memory directories, preventing the pipeline from functioning without precise environment setup.