A specialized tool for refactoring raw research scripts into professional, reproducible computational workflows ready for scientific publication.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install code-refactor-for-reproducibility
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install code-refactor-for-reproducibility using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Research Code Reproducibility Refactoring Tool?
The Research Code Reproducibility Refactoring Tool is a comprehensive framework designed to bridge the gap between experimental code and publication-standard software. By leveraging Openclaw Skills, this tool systematically identifies technical debt in research scripts, such as hardcoded paths and missing seeds, and implements industry best practices. It ensures that scientific results are verifiable and easily shared across different computational environments.
Beyond simple cleanup, this skill emphasizes the creation of robust environment specifications and validation layers. It helps researchers move from 'it works on my machine' to a standardized pipeline that includes defensive programming, comprehensive logging, and automated dependency management, making the transition to open science seamless and reliable.
Research Code Reproducibility Refactoring Tool Use Cases
- Preparing academic research code for peer-review and public repository sharing.
- Converting legacy analysis scripts into parameterized, reusable command-line tools.
- Standardizing computational environments across a research team using Conda or Docker.
- Debugging non-deterministic results by implementing rigorous random seed management.
- Automating the generation of high-quality README documentation for complex data pipelines.
How Research Code Reproducibility Refactoring Tool Works
- The tool performs a static analysis of source files to detect reproducibility anti-patterns like absolute paths or unpinned imports.
- It refactors the codebase by adding Type-hinted docstrings, replacing hardcoded variables with argument parsers, and injecting global random seeds.
- Defensive coding patterns are implemented, including structured logging and explicit error handling for data I/O operations.
- It generates environment-specific files (requirements.txt or environment.yml) by scanning actual project dependencies.
- A comprehensive README and validation suite are created to verify that the refactored output matches the original scientific baseline.
Research Code Reproducibility Refactoring Tool Setup
To integrate this capability into your workflow, ensure you have the Openclaw Skills environment configured. You can use the following commands to initialize the refactoring dependencies:
# Install dependency analysis tools
pip install pipreqs pytest
# Clone the research code repository
git clone <your-research-repo>
cd <your-research-repo>
# Initialize the reproducibility workflow
# The agent will use the Read, Write, and Bash tools to begin Step 1
Research Code Reproducibility Refactoring Tool Data Schema & Taxonomy
The skill organizes research projects into a standardized taxonomy to ensure clarity for external collaborators:
| Component | Description | Format |
|---|---|---|
| Source Code | Refactored scripts with relative paths and docstrings | .py |
| Environments | Dependency manifests for reproduction | environment.yml / requirements.txt |
| Documentation | Installation and execution instructions | README.md |
| Validation | Checksums and test results for verification | .md5 / pytest reports |
| Metadata | Versioning and author attribution | SKILL.md frontmatter |
Research Code Reproducibility Refactoring Tool Advanced Features
- Automated detection of hardcoded absolute paths using Abstract Syntax Tree (AST) parsing.
- Cross-platform determinism validation through MD5 checksum comparison of output files.
- Multi-framework seeding support covering Python random, NumPy, PyTorch, and Scikit-Learn.
- Automated environment resolution testing via temporary virtual environment instantiation.
- Template-based generation of publication-ready README files including 'Expected Outputs' sections.
SKILL.md
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