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Skills directory/meleantonio/awesome-econ-ai-stuff/lit-review-assistant

Economics Literature Review Skill for ChatGPT: Compare Methods and Evidence

Define a search scope and inclusion rules, then summarize papers by question, data, methods, findings, and limitations. Use the evidence table to distinguish agreement, differences, and gaps. Begin with supplied abstracts or bibliographic notes.

中文

Install in ChatGPT

Use an eligible ChatGPT workspace with permission to create skills and, for uploads, upload permission. The installation path for this text-only adaptation was checked against OpenAI documentation. Workspace runtime verification is pending.

  1. Download _skills/literature/lit-review-assistant/SKILL.md from the pinned GitHub source. Keep the name, description, and instructions for lit-review-assistant together as a standalone text skill.
  2. In a ChatGPT workspace with skills and uploads enabled, open Plugins, Skills, Create, and Upload from your computer. Choose a skill package format accepted by the file picker. You can also choose Create with editor and enter the original name, description, and instructions.
  3. After the scan completes, check that lit-review-assistant appears in Installed. Type @ in a new chat, select the skill, and use the fictional example on this page to check the output.

Example task

Give it three fictional paper cards. A randomly assigns commuter subsidies and finds bus use rises in three months. B observes the same city for a year and notes low take-up. C studies bike parking. Ask for a review of commuter subsidies and bus use, with citation placeholders and a methods comparison.

What to check

Include A and B and distinguish their designs and observation windows. Exclude C by topic. Keep citations as placeholders and flag the lack of long-term causal evidence as a follow-up question.

Original SKILL.md on GitHub · Updated 2026-11-13

Frequently asked question

Can I compare effect sizes using abstracts alone?

Record direction and magnitude only where the abstract supplies them. Mark data, modeling, and robustness details for full-text review before comparing estimates on a common basis.

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