alejandroll10

alejandroll10 / idea-evaluation-pipeline

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8-step pipeline for evaluating PhD research ideas to top-3 finance journal quality. Works with any AI coding assistant.

55
9
100% credibility
Found Mar 13, 2026 at 53 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A collection of prompts and instructions for PhD students to iteratively evaluate, refine, and validate research ideas in finance and economics using AI assistants until they achieve high journal quality.

How It Works

1
💡 Discover the Pipeline

You hear about a helpful guide that uses AI to test and improve your research ideas until they're ready for top journals.

2
📁 Set Up Your Idea

Create a simple folder and write down your research question, guess on results, data plans, and the three most similar papers.

3
🔍 Get First Evaluation

Share your idea with an AI helper, which scores it and points out strengths and weaknesses.

4
Check the Score
🔄
Score Too Low

Tweak your idea to make it stronger and re-check the score.

✅
Score Good Enough

Move forward to explore related studies.

5
📚 Review Related Papers

Use AI to find and check more papers that might challenge your idea, making sure everything is real and linked.

6
🏆 Final Check

Get the ultimate score and feedback on your refined idea after all the reviews.

🎉 Idea Approved!

Your research idea hits the target score, ready to pursue for top finance or economics journals.

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AI-Generated Review

What is idea-evaluation-pipeline?

This is an 8-step pipeline for evaluating PhD research ideas in finance and economics, iterating through critique, pivots, literature reviews, and final verdicts until they hit top-3 finance journal quality like JF, JFE, or RFS. It works with any AI coding assistant—Claude Code, Cursor, or others—or manually via copy-paste prompts into LLMs with web search. PhD students get a repeatable process to score ideas out of 10, dodging sunk-cost traps on weak hypotheses.

Why is it gaining traction?

It stands out with built-in loops for unfair critiques, pivot suggestions, and citation verification to catch AI hallucinations, forcing rigorous novelty checks against the closest threatening papers. The hook is quick setup: drop your idea with three key papers, run steps, and get actionable feedback without coding. At 43 stars, it's pulling devs who want structured evaluation over vague brainstorming.

Who should use this?

Finance PhD students testing hypotheses with ID strategies and data plans before diving into regressions. Economics grad students targeting top-5 journals, needing lit review threats surfaced fast. Any researcher using AI assistants for idea quality checks, especially those burned by unverified citations.

Verdict

Worth a spin for PhD idea triage—solid prompts and docs make it immediately usable despite low 1.0% credibility score and 43 stars signaling early maturity. No tests or automation yet, so pair it with a strong LLM for best results; skip if you need production-grade tooling.

(178 words)

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