idea-generator

Generates causal X→Y hypotheses from user-provided data inputs for economics and management research papers.

Updated Apr 15, 2026

Search Tags:#economics #management #hypotheses #data-exploration #causal #research-design #idea-generation

Ready to run

npx skills add https://github.com/sheehe/coase --skill idea-generator-sheehe

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: idea-generator
Source: https://github.com/sheehe/coase/tree/main/resources/plugins/coase-builtin/skills/idea-generator
Command: npx skills add https://github.com/sheehe/coase --skill idea-generator-sheehe

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The idea-generator helps management and economics researchers rapidly convert data inputs into testable causal hypotheses, reducing lengthy brainstorming and aligning ideas with publication-ready standards.

Core Features & Use Cases

Caused-based hypotheses: Generates causal X → Y propositions with directional arrows.
Data-alignment guidance: Scans user-provided data descriptions or samples to ground hypotheses in available evidence.
Journal-ready framing: Proposes hypotheses compatible with high-impact journals (SMJ, OrgSci, AMJ, JIBS, SEJ, JBV) and suggests minimal identification designs.

Quick Start

Provide your data sample or description to let the skill generate 3–5 causal hypotheses.

Frequently Asked Questions about idea-generator

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

FAQPage Schema

How do I generate testable causal hypotheses from my economics data?▼

To generate causal hypotheses from economics data, provide your data sample or description to produce 1–5 testable X → Y propositions grounded in available evidence and standard research designs.

What is a causal hypothesis in management research?▼

A causal hypothesis in management research is a directional X → Y proposition identifying a clear relationship between variables, structured for evaluation using standard research designs and identification strategies.

Can I use this for framing management research for journals like SMJ or AMJ?▼

Yes, you can use this for framing management research for journals like SMJ, AMJ, OrgSci, JIBS, SEJ, and JBV by producing publication-ready causal hypotheses with suggested minimal identification designs.

How do I align my data exploration with research design for idea generation?▼

Align data exploration with research design by submitting data descriptions for intake, direction alignment, and hypothesis evaluation to ensure generated testable propositions match your available dataset.

What are the limitations of using automated idea generation for research hypotheses?▼

Automated idea generation for research hypotheses is limited to producing 1–5 candidates based strictly on provided data inputs, requiring you to manually validate identification strategies and journal fit.