Oraclaw Calibrate
Skill Verified ActivePrediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.
To give AI agents precise, mathematical tools for evaluating the accuracy of their predictions and the agreement between different information sources.
Features
- Score prediction accuracy (Brier, log score)
- Analyze multi-source agreement/convergence
- Detect outlier data sources
- Provide deterministic algorithmic answers
- Offer as MCP server, REST API, and SDK
Use Cases
- Score how accurate past predictions were
- Check if multiple data sources agree on forecasts
- Find the outlier source that disagrees with consensus
- Compare forecast quality across different models
Non-Goals
- Providing the predictions themselves
- Performing generic AI agent reasoning
- Acting as a replacement for primary LLM capabilities
Installation
npx skills add Whatsonyourmind/oraclawRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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