synthesis-matrix

Organize extracted study data into structured evidence matrices for systematic reviews.

Updated Aug 27, 2026

Search Tags:#evidence #synthesis #matrix #systematic-review #risk-of-bias #effect-size #study-characteristics

Ready to run

npx skills add https://github.com/astoreyai/ai_scientist --skill synthesis-matrix

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: synthesis-matrix
Source: https://github.com/astoreyai/ai_scientist/tree/main/skills/synthesis-matrix
Command: npx skills add https://github.com/astoreyai/ai_scientist --skill synthesis-matrix

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers organize and synthesize evidence across multiple studies using structured matrices, saving time in preparing systematic reviews and manuscripts.

Core Features & Use Cases

Matrix construction: Create a data matrix capturing study characteristics (author, year, design, population), outcomes, effect sizes, and quality.
Cross-study comparisons: Identify patterns and contrasts across studies to support narrative synthesis.
Manuscript preparation: Export structured matrices for inclusion in manuscripts or reports.

Quick Start

Load your extracted study data (e.g., studies_export.csv) and run the synthesis-matrix skill to generate a structured evidence matrix.

Frequently Asked Questions about synthesis-matrix

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

FAQPage Schema

How do I organize study data for a systematic review?▼

Systematic reviews require organizing extracted study data into structured matrices capturing study characteristics, populations, interventions, outcomes, and effect sizes. This skill automates that organization, letting you load study exports and generate evidence matrices ready for synthesis and manuscript preparation.

What data should I extract for an evidence synthesis matrix?▼

Evidence synthesis matrices typically include study identifiers, population details, study design, intervention descriptions, measured outcomes, effect sizes, and quality assessments. This skill standardizes extraction across studies, enabling consistent comparison and cross-study pattern identification for narrative synthesis.

Can I compare study characteristics across multiple papers?▼

Yes. Once your studies are organized in a structured matrix, you can identify patterns and contrasts across study characteristics, designs, and outcomes. This cross-study comparison supports evidence synthesis and helps highlight methodological differences relevant to your review conclusions.

How do I prepare evidence data for manuscript publication?▼

Evidence matrices structured with study characteristics, outcomes, and quality metrics can be exported and included directly in manuscripts or supplementary materials. This skill formats your synthesis data into publication-ready matrices, reducing manual table construction.

What file formats work with evidence matrix synthesis?▼

Extracted study data in CSV format loads directly into evidence matrix workflows. The skill accepts structured exports from data extraction tools and standardizes them into matrix schema with fields for study, population, design, intervention, outcome, effect size, and quality.

Does this work with risk-of-bias assessments?▼

Yes. Quality assessment scores and risk-of-bias ratings integrate into the evidence matrix as part of study quality fields. This allows you to track methodological limitations across studies and weight evidence synthesis by study quality.