devils-advocate

Generate five to seven skeptical questions with threat, response, vulnerability, and action fields for economics manuscripts.

Updated Feb 26, 2026
One-click install
npx skills add https://github.com/naj2r/claude-econ-paper-template --skill devils-advocate-naj2r
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: devils-advocate
Source: https://github.com/naj2r/claude-econ-paper-template/tree/main/.claude/skills/devils-advocate
Command: npx skills add https://github.com/naj2r/claude-econ-paper-template --skill devils-advocate-naj2r

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured, referee-style critique by generating 5-7 tough, skeptical questions to challenge a manuscript's argument before submission, helping authors identify and address weaknesses early.

Core Features & Use Cases

  • Structured critique: Produces 5-7 challenging questions spanning identified risk areas (Identification & Endogeneity, Data & Measurement, Specification & Robustness, Interpretation, Literature & Contribution).
  • Fully articulated outputs: Each question is accompanied by a threat, a best available response, remaining vulnerability, and a recommended action.
  • Customizable prompts: Accepts a specific section, finding, or specification to challenge via the ARGUMENTS input for targeted reviews.

Quick Start

Provide the ARGUMENTS string to be challenged and receive 5-7 questions in the required format, ready for review.

Frequently Asked Questions about devils-advocate

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

FAQPage Schema
How do I generate a peer-review critique for an economics paper before submission?

To generate a peer-review critique for an economics paper before submission, provide your manuscript's specific section or finding as an ARGUMENTS input to receive 5-7 structured challenging questions. Each question includes an explicit threat, response, vulnerability, and recommended action for targeted review.

What is a devil's advocate critique and how does it identify identification strategy weaknesses?

A devil's advocate critique is a structured referee-style review that identifies identification strategy weaknesses by generating 5-7 skeptical questions challenging a manuscript's argument. It spans risk areas like endogeneity, data measurement, specification robustness, and theoretical framing to expose vulnerabilities before publication.

Can I target the critique to a specific specification or data interpretation section?

Yes, you can target the critique to a specific specification or data interpretation section by supplying that text through the ARGUMENTS input. The skill processes the provided excerpt and generates customized challenging questions focused on the robustness and interpretation of that exact section.

What does the structured output format for manuscript questioning look like?

The structured output format for manuscript questioning consists of 5-7 items, each containing four explicit fields: threat, best available response, remaining vulnerability, and recommended action. This repeatable format ensures authors receive actionable feedback to address identification and specification weaknesses.

Does this peer-review critique work for theoretical framing or just empirical robustness checks?

This peer-review critique works for both theoretical framing and empirical robustness checks across economics papers. The generated questions span five risk areas including Identification, Data, Specification, Interpretation, and Literature Contribution, ensuring comprehensive coverage of both empirical and theoretical arguments.

When should I not use an automated referee critique for research quality?

You should not use an automated referee critique when you lack a specific argument, finding, or specification to challenge, as the skill requires an ARGUMENTS input to function. It is designed for targeted pre-submission robustness checks of explicit text rather than general manuscript brainstorming.