research-antagonist

Review draft research articles for statistical, citation, and logical issues.

1|Updated Oct 22, 2025

Search Tags:#quality-control #statistical-review #writing-quality #citation-check #research-review

Ready to run

npx skills add https://github.com/Tristan578/research-team-tutorial --skill research-antagonist

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-antagonist
Source: https://github.com/Tristan578/research-team-tutorial/tree/main/.claude/skills/antagonist
Command: npx skills add https://github.com/Tristan578/research-team-tutorial --skill research-antagonist

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill acts as a quality control inspector that reviews the draft article for statistical validity, citation adequacy, logical consistency, and writing quality before finalization.

Core Features & Use Cases

Checks that correlations fall within -1 to 1, p-values are within 0-1, and sample sizes are stated.
Flags any causal language or overstatements not supported by correlational evidence.
Verifies that all factual claims are supported by citations and that all papers analyzed are cited.

Quick Start

Run the antagonist on results/draft_article.md to generate results/review_feedback.json.

Frequently Asked Questions about research-antagonist

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

FAQPage Schema

How do I check a research manuscript for statistical validity before submission?▼

Statistical validity checks verify that correlations fall within -1 to 1, p-values are within 0-1, and sample sizes are stated. This Skill reviews your draft manuscript against these criteria and flags violations with severity levels and correction guidance.

Can I automatically verify that all claims in my article are properly cited?▼

Yes. This Skill scans your draft for unsupported factual claims and confirms all analyzed papers are cited, flagging citation gaps and unsourced statements so you can add missing references before finalization.

How do I catch causal language errors in correlational research?▼

Causal overstatement is a common error in research writing. This Skill flags language implying causation when only correlational evidence exists, helping you revise claims to match your statistical findings.

What format does the research review output use?▼

The Skill reads a markdown draft article and outputs a structured JSON review file containing flagged issues, severity classifications, and specific recommendations for each section—ready for systematic revision.

Does this quality check work for logical consistency across sections?▼

Yes. Beyond statistics and citations, this Skill evaluates logical consistency across all sections of your manuscript, identifying contradictions and gaps that undermine coherence.