What input file does the Synthesis Writer require?

The skill requires a 'papers/extraction_table.csv' file containing the extracted data from your research papers.

What is the primary output format?

The skill produces an 'output/SYNTHESIS.md' file including research questions, methods summary, thematic findings, and bounded conclusions.

Can I use this if my extraction table is incomplete?

It is recommended to complete the extraction and bias assessment phases first, as the skill relies on the table as its primary evidence base to avoid 'hollow' writing.

How does this skill handle risk-of-bias?

It acts as a 'Bias Reporter' that summarizes patterns in your data and explicitly bounds conclusions based on the observed risk-of-bias and missing data.

Evidence Synthesis Writer

Name: Evidence Synthesis Writer
Author: WILLOSCAR

byWILLOSCAR

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Data Science & ML

Synthesizes structured research data into comprehensive, evidence-backed narratives for systematic reviews.

About

The Synthesis Writer skill bridges the gap between raw research data and polished narrative summaries. By utilizing an evidence-first methodology, it transforms data from an extraction table into a structured SYNTHESIS.md file that remains strictly traceable to its sources. This skill is specifically designed for the writing phase of systematic reviews, ensuring that claims are grounded in data while explicitly addressing heterogeneity, risk-of-bias patterns, and evidence limitations to prevent the generation of unsupported conclusions.

Key Features

Evidence Traceability: Ensures all claims are grounded in specific data points to prevent 'hollow' writing.
Thematic Grouping: Organizes findings by intervention, outcome, or theme based on extraction fields.
89 GitHub stars
Table-to-Narrative Transformation: Converts CSV extraction data into structured Markdown narratives.
Bias Reporting: Automatically incorporates risk-of-bias patterns and limitations into the interpretation.
Strict Guardrails: Prevents the AI from inventing facts or making overconfident claims unsupported by data.

Use Cases

Generating comparative summaries of technical research papers for R&D teams.
Writing the synthesis and results section of a systematic literature review.
Automating evidence-based reporting for internal audits or protocol evaluations.

About

Key Features

Evidence Traceability: Ensures all claims are grounded in specific data points to prevent 'hollow' writing.
Thematic Grouping: Organizes findings by intervention, outcome, or theme based on extraction fields.
89 GitHub stars
Table-to-Narrative Transformation: Converts CSV extraction data into structured Markdown narratives.
Bias Reporting: Automatically incorporates risk-of-bias patterns and limitations into the interpretation.
Strict Guardrails: Prevents the AI from inventing facts or making overconfident claims unsupported by data.

Use Cases

Generating comparative summaries of technical research papers for R&D teams.
Writing the synthesis and results section of a systematic literature review.
Automating evidence-based reporting for internal audits or protocol evaluations.