prediction-market-analysis

Generate charts, tables, and JSON files from Polymarket and Kalshi datasets.

Updated Feb 4, 2026

Search Tags:#data analysis #financial analysis #market data #reproducible research #prediction market

Ready to run

npx skills add https://github.com/rockomatthews/molt-scout --skill prediction-market-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prediction-market-analysis
Source: https://github.com/rockomatthews/molt-scout/tree/main/skills/prediction-market-analysis
Command: npx skills add https://github.com/rockomatthews/molt-scout --skill prediction-market-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires uv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Automates the generation of exportable research artifacts from Polymarket and Kalshi datasets, enabling paid, reproducible research without trading.

Core Features & Use Cases

Data Access: Provides access to large public datasets from Polymarket and Kalshi.
Analysis Framework: Offers a Python-based analysis framework for in-depth data exploration.
Exportable Artifacts: Generates charts, tables, and JSON files for paid research outputs.
Use Case: Conduct market analysis for a financial institution that requires reproducible research without executing trades.

Quick Start

Run the 'make analyze' command in the cloned repository directory to generate analysis artifacts.

Frequently Asked Questions about prediction-market-analysis

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

FAQPage Schema

How do I generate reproducible research artifacts from Polymarket and Kalshi datasets?▼

To generate reproducible research artifacts from Polymarket and Kalshi datasets, this skill uses a Python-based analysis framework to process public market data. It outputs exportable charts, tables, and JSON files without executing any trades.

Can I analyze prediction market data for financial research without executing trades?▼

Yes, you can analyze prediction market data for financial research without executing trades. This skill specifically accesses public datasets from Polymarket and Kalshi to generate market analysis artifacts, ensuring research reproducibility for educational purposes.

What do I need to run Python-based prediction market analysis using public datasets?▼

To run Python-based prediction market analysis, you need Python 3.9 or higher and the uv package installed for dataset synchronization. Once configured, execute the 'make analyze' command in the cloned repository to generate the analysis artifacts.

What is the best way to export charts and tables from prediction market data?▼

The best way to export charts and tables from prediction market data is by running the 'make analyze' command. This framework automates artifact generation from Polymarket and Kalshi datasets, creating exportable files suitable for paid research outputs.

Does this prediction market analysis framework support automated dataset synchronization?▼

Yes, this prediction market analysis framework supports automated dataset synchronization. It requires the uv package to manage and sync large public datasets from Polymarket and Kalshi, ensuring your financial research outputs remain reproducible.