ai-econ-finance-research-handbook

name

ai-econ-finance-research-handbook

description

Use this when helping economics or finance scholars use AI for research workflows, including learning core concepts, choosing copy-ready skills, setting up Git/GitHub/agents, checking sources and datasets, drafting or auditing empirical methods, reviewing papers, building slides, teaching workshops, or finding safe data/resource paths. Route the user to the relevant section of the AI for Economics and Finance Research handbook, ask clarifying questions when inputs are missing, confirm structure before long outputs, and enforce verification, privacy, citation, and disclosure checks.

AI for Economics and Finance Research Handbook Router

Use this file as a compact router for the full GitHub-native handbook:

https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research

For a downloadable one-skill package with local reference files, use:

skill-pack/ai-econ-finance-research/SKILL.md

This skill does not replace the handbook. It tells an AI assistant how to use the handbook with a researcher who wants practical help.

How To Use This Skill

1. Identify the user's research task.
2. Route to the most relevant section below.
3. Ask only the missing questions needed to proceed.
4. If the task is long, file-producing, code-producing, slide-producing, or method-sensitive, first return Proposed structure and assumptions and wait for confirmation.
5. Use only verified facts, supplied sources, public documentation, or clearly stated assumptions.
6. End with:
   • What I produced
   • What I did not change
   • What you must verify
   • Questions for you

If the AI tool cannot open links, ask the user to paste the relevant README or skill block from the linked section.

Route By User Goal

User goal	Use this section	Direct link
Learn AI concepts from zero	01 handbook	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/01-Start-Here-to-Learn-AI-for-Econ-Finance-Research
Copy a ready-to-use skill or prompt	02 skill library	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/02-Copy-and-Use-AI-Research-Instructions-and-Templates
Set up Git, GitHub, agents, project instructions, or automation	03 setup workflows	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/03-Set-Up-Agents-and-Automated-Research-Workflows
See examples, diagrams, and failure cases	04 examples	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/04-See-Examples-Diagrams-and-Failure-Cases
Check sources, builders, official docs, datasets, or access rules	05 resources	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/05-Check-Builders-Official-Docs-and-Resources
Teach a workshop, prepare slides, or onboard students/RAs	06 teaching and slides	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/06-Teach-Workshops-Practice-Talks-and-Share-Slides
Read Chinese content	Chinese version	https://github.com/SuperJayLiu/AI-for-Economics-and-Finance-Research/tree/main/ZH-%E4%B8%AD%E6%96%87-AI%E7%BB%8F%E6%B5%8E%E9%87%91%E8%9E%8D%E7%A0%94%E7%A9%B6%E6%89%8B%E5%86%8C

Route By Research Task

Task	Start with
research idea, proposal, or question sharpening	02/skills/01-ideas-brainstorming-proposal-and-literature-skills.md and 02/skills/18-research-question-taste-and-positioning-skills.md
source-grounded literature review	02/skills/10-literature-review-and-source-synthesis-skills.md
writing or revising a paper	02/skills/02-paper-drafting-revision-and-citation-skills.md
economics empirical methods	02/skills/03-empirical-methods-skills-for-economics-research.md
finance empirical methods	02/skills/04-empirical-methods-skills-for-finance-research.md
causal inference, DiD, IV, RD, panel FE, time series	02/skills/11-causal-inference-econometrics-and-time-series-skills.md
Stata, R, Python, debugging, code review	02/skills/08-coding-data-analysis-and-debugging-skills.md
data cleaning, merging, WRDS, CRSP, Compustat, tables, figures	02/skills/14-data-cleaning-merging-analysis-and-output-skills.md
text-as-data or LLM-as-measurement	02/skills/15-text-as-data-and-llm-measurement-skills.md
theory, math, model, proof, equilibrium	02/skills/12-theory-model-and-math-skills.md
structural, quantitative, simulation, welfare	02/skills/16-structural-quantitative-and-welfare-skills.md
referee report, paper review, response to reviewers	02/skills/13-referee-reports-and-peer-review-skills.md
Git, data safety, AGENTS.md, AI-use log	02/skills/05-git-data-replication-and-research-safety-templates.md
ChatGPT/Claude/Codex project roles and agent instructions	02/skills/07-project-instructions-and-agent-role-templates.md
slides, HTML deck, Beamer deck, talk practice, academic website	02/skills/06-presentations-slides-websites-and-talk-practice-skills.md
verification, reproducibility, disclosure	02/skills/17-verification-reproducibility-and-disclosure-skills.md
tool selection, AI updates, finding resources	02/skills/09-tool-selection-updates-and-skill-improvement.md

Default Operating Rules

Always apply these rules unless the user explicitly gives a stricter rule.

Before producing the final answer, check whether required inputs, data rules, method details, tool permissions, institutional constraints, audience, or output format are missing.

Ask up to five clarifying questions if needed. If you can proceed with reasonable assumptions, state them clearly.

For long outputs, file-producing tasks, code, slides, empirical methods, literature reviews, referee responses, or agentic workflows, first return "Proposed structure and assumptions" and wait for confirmation.

Do not invent citations, papers, datasets, coefficients, robustness checks, institutional facts, mathematical derivations, or claims about the literature.

Follow university, employer, journal, conference, funder, data-provider, coauthor, and referee-report rules. If a local rule is stricter, follow the stricter rule.

Before finalizing, double-check for unsupported claims, invented citations or results, privacy risks, and mismatch with the requested format.

Tool Access Reality

Links in this file are useful in three ways:

1. If the AI tool has browsing or GitHub access, it can open the linked pages directly.
2. If the tool has local files because the repo is installed or cloned, it can read the referenced Markdown files.
3. If the tool has neither, ask the user to paste the relevant README or skill block.

Do not pretend to have read a linked page if the tool cannot access it. Say what is needed and ask the user to paste it.

When A Bigger System Is Needed

Use this SKILL.md for lightweight routing and direct use.

Use a dedicated MCP server only if users need searchable, tool-call access to the repo, for example:

• search_handbook(query)
• list_skills(task)
• read_skill(skill_id)
• read_dataset_guidance(data_type)
• get_chinese_page(section)

An MCP server is more powerful but requires installation, permissions, hosting or local setup, and maintenance. Start with this skill router first.