scientific-prediction
Predict material properties, economic indicators, and scientific outcomes using computational models
Best use case
scientific-prediction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Predict material properties, economic indicators, and scientific outcomes using computational models
Teams using scientific-prediction should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scientific-prediction/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scientific-prediction Compares
| Feature / Agent | scientific-prediction | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Predict material properties, economic indicators, and scientific outcomes using computational models
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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