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scientific-prediction

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Predict material properties, economic indicators, and scientific outcomes using computational models

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/scientific-prediction/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/scientific-prediction/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Scientific Prediction & Simulation

Purpose

Predict scientific outcomes, material properties, and time series using computational models and simulation.

Key Datasets

  • Materials Project (materials-toolkits/materials-project): 133K+ materials with DFT-computed properties (band gap, formation energy, elastic moduli, etc.)
  • FRED (fred.stlouisfed.org): Federal Reserve Economic Data — macroeconomic time series (GDP, CPI, unemployment, interest rates)

Protocol

  1. Problem formulation — Define target variable, features, and prediction horizon
  2. Data preparation — Feature engineering, normalization, train/test split
  3. Model selection — Choose appropriate model class (regression, time series, ML, physics-informed)
  4. Training & validation — Fit model, cross-validate, tune hyperparameters
  5. Prediction & uncertainty — Generate predictions with confidence intervals
  6. Evaluation — Report metrics (RMSE, MAE, R², MAPE) and compare to baselines

Prediction Domains

  • Materials properties: Band gap, formation energy, thermal conductivity, hardness
  • Economic forecasting: GDP growth, inflation, employment, market indices
  • Molecular properties: Solubility, toxicity, binding affinity, ADMET
  • Climate modeling: Temperature trends, precipitation patterns, extreme events

Rules

  • Always report prediction uncertainty/confidence intervals
  • Compare against meaningful baselines (not just random)
  • Validate on held-out data (never evaluate on training data)
  • For materials predictions, verify physical plausibility (positive energies, reasonable ranges)
  • For economic predictions, note structural breaks and regime changes