methodology-extractor

What This Skill Does

The methodology-extractor skill is a specialized agentic tool designed for the systematic batch extraction, comparison, and synthesis of experimental protocols found within scientific literature. By utilizing a structured execution path centered on the scripts/main.py entry point, this skill allows researchers and analysts to parse multiple technical papers simultaneously to identify key experimental steps, reagent concentrations, equipment configurations, and environmental conditions. It is engineered to provide a reproducible, audit-ready output format, ensuring that evidence is captured with clear assumptions and bounded scopes rather than vague summaries. The skill leverages local reference materials to guide its extraction logic, making it ideal for standardizing unstructured textual data into actionable protocol documentation.

Installation

To integrate this skill into your OpenClaw environment, ensure your system meets the Python 3.10+ requirement. Execute the following command in your terminal:

clawhub install openclaw/skills/skills/aipoch-ai/methodology-extractor

Once installed, navigate to the skill directory (typically 20260318/scientific-skills/Evidence Insight/methodology-extractor) and verify the installation by running python -m py_compile scripts/main.py. If the compilation succeeds, the tool is ready for execution.

Use Cases

• Protocol Synthesis: Aggregating methods from 5+ papers on CRISPR gene editing to create a master protocol document.
• Literature Review Assistance: Rapidly identifying specific centrifuge speeds or incubation temperatures across a library of PDF papers to save hours of manual reading.
• Reproducibility Audits: Checking whether a collection of published studies provides sufficient experimental details to meet reproducibility standards.
• Cross-Study Comparison: Comparing variations in buffer compositions across different research labs within a specific domain.

Example Prompts

1. "Extract the experimental protocol for PCR amplification from the papers in the references/genomics folder and output them as a comparison table."
2. "Review the provided research papers and summarize the cell culture conditions, explicitly noting any assumptions made where data is missing."
3. "Using the methodology-extractor, find all instances of reagent concentrations for the protein purification process described in the latest batch of uploaded articles."

Tips & Limitations

• Input Quality: Ensure your input files are clean, text-extractable documents; scanned images without OCR will not be parsed effectively.
• Bounded Scope: Always define your extraction scope clearly to prevent the script from hallucinating details in sections it was not instructed to analyze.
• Verification: While the skill is designed for consistency, always review the generated output for 'Assumptions' sections. These sections are explicitly added to highlight where the agent had to infer data, which is critical for scientific accuracy.
• Configuration: If the default script parameters do not align with your specific paper formatting, edit the CONFIG block within scripts/main.py to match your domain-specific needs.