komako-workshop/digital-oracle

What it solves

Digital Oracle addresses the problem of "noise" in financial forecasting, where social media and emotional predictions often overshadow objective data. It provides a way for AI agents to derive rational, data-driven probability estimates for macro events—such as housing market trends, geopolitical conflicts, or asset bubbles—by analyzing actual trading data rather than news or opinions.

How it works

The tool functions as a "Skill" for AI agents (like OpenClaw, Claude Code, or Cursor). It follows a five-step process:

1. Problem Decomposition: It breaks down the user's question into core variables and timeframes.
2. Signal Selection: It selects three or more independent financial data sources based on the query.
3. Parallel Retrieval: It fetches data simultaneously from these providers using asynchronous calls.
4. Contradictory Reasoning: It analyzes discrepancies between different markets to explain potential outcomes.
5. Reporting: It generates a structured report featuring signal tables, probability estimates, and scenario analyses.

Who it’s for

It is designed for AI agents and the users who operate them to perform macro-economic analysis and event probability forecasting based on market pricing.

Highlights

• Diverse Data Integration: Connects to 13 authoritative sources, including prediction markets (Polymarket, Kalshi), treasury yields, institutional holdings (CFTC), and insider trading (SEC).
• Zero-Dependency Focus: 12 of the 13 data providers rely solely on the Python standard library, minimizing installation overhead.
• No API Keys Required: All integrated APIs are free and do not require authentication keys.
• Resilient Design: Implements partial failure tolerance and snapshot testing for reliable performance.