predictor-hand-skill

Structure calibrated forecasts with superforecasting principles and signal taxonomy.

Updated Mar 12, 2026

Search Tags:#calibration #uncertainty #forecasting #risk-analysis #signal-taxonomy #base-rate #reasoning-chain

Ready to run

npx skills add https://github.com/thepradip/openfangclaw --skill predictor-hand-skill-thepradip

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: predictor-hand-skill
Source: https://github.com/thepradip/openfangclaw/tree/main/crates/openfang-hands/bundled/predictor
Command: npx skills add https://github.com/thepradip/openfangclaw --skill predictor-hand-skill-thepradip

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Forecasting expert knowledge provides AI agents with a rigorous framework for producing calibrated, testable predictions and documenting reasoning.

Core Features & Use Cases

Superforecasting principles for decomposing problems and updating beliefs
Signal taxonomy and base-rate reasoning to weight evidence
Confidence calibration and logging for trackable accuracy
Reasoning chains that connect signals to predictions
Accurately track and audit forecast performance across domains (tech, finance, geopolitics, climate)

Quick Start

Provide a concise, testable forecast using the included superforecasting principles and signal taxonomy, and log it with an initial confidence and a prediction trace.

Frequently Asked Questions about predictor-hand-skill

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

FAQPage Schema

How do I structure AI agent predictions for accurate risk analysis?▼

You can structure AI agent predictions using a superforecasting framework that applies base-rate reasoning and signal taxonomy. This decomposes problems into testable forecasts with traceable reasoning chains for calibrated risk analysis.

What is base-rate reasoning in forecasting and how does it improve accuracy?▼

Base-rate reasoning establishes initial probability estimates using historical reference classes before adjusting for specific signals. This superforecasting technique prevents overconfidence and produces calibrated, testable predictions across domains.

How do I track and audit forecast performance across different domains?▼

You track forecast performance by logging initial confidence levels and prediction traces within a structured framework. This enables accuracy tracking and auditing across technology, finance, geopolitics, and climate scenarios.

Can I use superforecasting principles for geopolitical and climate risk assessment?▼

Yes, superforecasting principles apply directly to geopolitical and climate risk assessment. The framework uses signal-driven adjustments and base-rate reasoning to guide decisions and form testable predictions in these domains.

How do I decompose forecasting problems to update beliefs with new evidence?▼

Decompose forecasting problems by breaking them into base-rate estimates and specific signals, then update beliefs by weighting new evidence. This creates traceable reasoning chains that connect signals to adjusted predictions.

What is the best way to calibrate confidence levels for testable predictions?▼

The best way to calibrate confidence is using a structured forecasting framework that logs predictions and tracks accuracy over time. This superforecasting approach ensures confidence levels match actual outcomes through traceable reasoning chains.