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predictor-hand-skill

Collect signals, construct reasoning chains, and calibrate predictions with Brier score tracking.

4|1|Updated Mar 3, 2026
Search Tags:#AI agent#data analysis#calibration#forecasting#prediction#superforecasting
One-click install
npx skills add https://github.com/aegntic/clawreform --skill predictor-hand-skill-aegntic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: predictor-hand-skill
Source: https://github.com/aegntic/clawreform/tree/main/crates/clawreform-hands/bundled/predictor
Command: npx skills add https://github.com/aegntic/clawreform --skill predictor-hand-skill-aegntic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles the inherent uncertainty of future events by providing a structured, data-driven approach to making and tracking predictions, moving beyond guesswork to calibrated forecasting.

Core Features & Use Cases

  • Signal Aggregation: Gathers diverse signals from news, social media, financial data, and academic sources.
  • Reasoning Chains: Constructs logical arguments connecting signals to potential outcomes, considering base rates and biases.
  • Accuracy Tracking: Rigorously scores past predictions using Brier scores and calibration analysis to improve future forecasts.
  • Use Case: Predict the likelihood of a specific technology reaching mass adoption within a year, detailing the supporting and contradicting evidence, and tracking its accuracy over time.

Quick Start

Use the predictor-hand-skill to make a prediction about the next major advancement in renewable energy technology.

Frequently Asked Questions about predictor-hand-skill

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

FAQPage Schema
How do I make calibrated predictions for future events using AI?▼

Calibrated predictions are generated by collecting diverse signals, constructing reasoning chains that weigh base rates and biases, and scoring past accuracy with Brier scores to improve future forecasts.

What is superforecasting and how does it apply to data analysis?▼

Superforecasting in data analysis involves aggregating signals from news, financial data, and academic sources to build logical reasoning chains, then calibrating confidence thresholds to produce accurate future event probabilities.

Can I forecast technology adoption or financial trends with autonomous AI agents?▼

Autonomous AI agents can forecast technology, finance, geopolitics, and climate trends by gathering domain-specific signals, applying contrarian mode logic, and tracking prediction accuracy over time.

What's the best way to track and score prediction accuracy over time?▼

Prediction accuracy tracking uses Brier scores and calibration analysis to rigorously evaluate past forecasts against actual outcomes, refining reasoning chains to improve future prediction reliability.

How do I build reasoning chains that connect signals to forecasting outcomes?▼

Reasoning chains are built by aggregating signals from diverse sources, mapping logical arguments to potential outcomes, adjusting for base rates and cognitive biases, and setting confidence thresholds for predictions.

Does this forecasting approach work for contrarian predictions?▼

Contrarian mode is supported alongside standard forecasting, allowing the AI agent to generate predictions that challenge consensus signals while still applying base rate analysis and accuracy tracking.

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