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market-researcher

Gather cross-platform signals and base-rate data for Polymarket prediction questions.

Updated Mar 18, 2026
Search Tags:#research#signals#metaculus#cross-reference#prediction-markets#polymarket#base-rates
One-click install
npx skills add https://github.com/mdbh202/polymarket-bot --skill market-researcher-mdbh202
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: market-researcher
Source: https://github.com/mdbh202/polymarket-bot/tree/main/.gemini/skills/market-researcher
Command: npx skills add https://github.com/mdbh202/polymarket-bot --skill market-researcher-mdbh202

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Gather and synthesize evidence for prediction-market events to support calibrated probability estimates and informed decision-making.

Core Features & Use Cases

  • Systematically collect signals from multiple sources (Metaculus, Kalshi, Manifold, and news feeds) to inform Polymarket questions.
  • Cross-reference platform data to identify consensus, divergence, and base-rate context for binary outcomes.
  • Produce structured signal reports suitable for human review and AI-assisted forecasting.

Quick Start

Ask the AI to assemble a cross-platform signal dossier for a specified Polymarket event.

Frequently Asked Questions about market-researcher

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

FAQPage Schema
How do I aggregate prediction-market signals across multiple platforms for a Polymarket event?▼

Base-rate data provides historical frequency context for prediction-market events, allowing you to calibrate probability estimates by comparing current signals from platforms like Metaculus and Kalshi against historical outcomes for binary events.

Does cross-referencing prediction markets work for events outside of politics and crypto?▼

Cross-referencing prediction markets works across binary-outcome events in politics, economics, crypto, sports, and science, systematically collecting signals from multiple platforms to produce structured reports for any supported domain.

What's the best way to identify consensus and divergence between Polymarket and Metaculus forecasts?▼

The best way to identify consensus and divergence is to cross-reference platform data from Polymarket, Metaculus, and Manifold, aggregating evidence to compare binary outcome probabilities and detect alignment or conflicting signals.

Can I use cross-referenced signal reports for AI-assisted forecasting on Kalshi?▼

Cross-referenced signal reports are structured for human review and AI-assisted forecasting, aggregating data from Kalshi, Manifold, and news feeds to support calibrated probability estimates for prediction-market events.

What are the limitations of using cross-platform signals for prediction-market forecasting?▼

Cross-platform signals are limited to binary-outcome events and depend on data availability from Metaculus, Kalshi, and Manifold; complex multi-outcome events or insufficient platform data may reduce the accuracy of aggregated signal reports.

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