research

Identify and synthesize prediction market data with embeddings-backed search.

71|22|Updated Apr 6, 2020
One-click install
npx skills add https://github.com/nirholas/agenti --skill research-nirholas
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/nirholas/agenti/tree/main/packages/protocols/x402-cloddsbot/src/skills/bundled/research
Command: npx skills add https://github.com/nirholas/agenti --skill research-nirholas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prediction markets often lack quick, structured access to base-rate estimates, resolution criteria, and relevant historical context, making it hard to calibrate expectations or decisions. This skill provides a concise, guided workflow to surface base rates, rules, and historical analogies for decision-making across political, economic, and event markets.

Core Features & Use Cases

  • Base-rate lookups for similar markets with concise contextual factors.
  • Explanation of resolution criteria and typical outcomes with historical references.
  • Use Case: quickly compare a market's mechanics and likely outcomes to past events.

Quick Start

Ask the agent to research a market or fetch base-rate data.

Frequently Asked Questions about research

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

FAQPage Schema
How do I find base rates for prediction markets?

Base rates for prediction markets are identified by querying a market index and using embeddings-backed search to surface concise contextual factors and typical outcomes for similar past events.

What's the best way to check resolution criteria for event markets?

The best way to check resolution criteria for event markets is by synthesizing market rules and historical references to explain typical outcomes and clarify how specific market mechanics resolve.

How do I compare current prediction market mechanics to historical analogs?

To compare prediction market mechanics to historical analogs, use embeddings-backed search to surface and summarize past events, allowing you to evaluate current political or economic markets against historical data.

Does this approach work for political and economic prediction markets?

Yes, this approach works for political, economic, and event prediction markets by relying on a market index to provide reference-grade insights and calibrate expectations across various market types.

Why do I need base rates and historical analogies for prediction market research?

You need base rates and historical analogies for prediction market research to calibrate expectations and decisions, solving the problem of quick, structured access to reference-grade data for likely outcomes.