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code-implement

Reproduce external research code results with hermetic environments and reproduction logs.

Updated Aug 27, 2026
Search Tags:#Docker#configuration#environment setup#reproduction#artifact evaluation#result verification#research code
One-click install
npx skills add https://github.com/UnaryLab/ai-for-research --skill code-implement-unarylab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-implement
Source: https://github.com/UnaryLab/ai-for-research/tree/main/skills/code-implement
Command: npx skills add https://github.com/UnaryLab/ai-for-research --skill code-implement-unarylab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of getting external research code working end to end so you can reproduce a specific paper result reliably, rather than getting stuck on setup issues or undocumented commands.

Core Features & Use Cases

  • Hermetic environment setup: installs the code in a container or isolated environment pinned to the correct commit/tag and compatible dependency stack.
  • Deterministic, result-first execution: maps each claimed figure/table/number to an exact command, config, and output location.
  • Honest reproduction with a log: generates a repro.md reproduction log and tracks minimal, recorded edits needed to run (without logic tampering).

Quick Start

Ask an agent to reproduce the paper’s Figure 3 by installing the provided GitHub repo or paper artifact in an isolated environment, running the smallest smoke test, executing the exact result command(s), and writing a repro.md with reproduced-versus-claimed numbers and any minimal diffs.

Frequently Asked Questions about code-implement

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

FAQPage Schema
How do I reproduce paper results from a GitHub repository without getting stuck on environment setup?▼

Reproduce paper results by installing the research code in a hermetic, containerized environment pinned to the correct commit and dependency stack, ensuring the external code runs end to end without setup failures.

What is the best way to map a claimed figure in a research paper to the exact command needed to generate it?▼

Deterministic, result-first execution maps each claimed figure, table, or headline number to an exact command, configuration, and output location, transforming undocumented research code into a reliable pipeline.

How do I troubleshoot research code reproduction when the reported numbers do not match the paper?▼

Run a smoke test at a small scale before full execution, then generate a reproduction log tracking minimal recorded edits needed to run the code without logic tampering to verify result discrepancies.

Can I use Docker for artifact evaluation and environment pinning of AI/ML research code?▼

Docker is used for hermetic environment setup during artifact evaluation, isolating the AI/ML or scientific computing code in a container pinned to a compatible dependency stack for reliable result verification.

Does reproducing research code require modifying the original logic to get it running?▼

Reproducing research code requires only minimal, recorded edits to fix environment or setup issues, explicitly avoiding any logic tampering to ensure honest artifact evaluation and result verification.

What is a reproduction log and why do I need one for verifying paper artifacts?▼

A reproduction log, generated as a repro.md file, records reproduced-versus-claimed numbers and any minimal diffs applied, providing an honest account of the environment setup and execution process.

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