Scientific Prediction
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scientific-prediction is a Claude skill that predicts scientific outcomes, material properties, and time series using computational and ML models with uncertainty estimates.
About
This skill predicts scientific outcomes, material properties, and time series using computational and ML models. A developer uses it to formulate a prediction problem, prepare data, select and train a model, and report predictions with confidence intervals. It enforces held-out validation, baseline comparison, and physical-plausibility checks.
- Six-step protocol from problem formulation to metric-based evaluation
- Domains: materials properties, economic forecasting, molecular properties, climate
- Requires uncertainty intervals and held-out validation against baselines
Scientific Prediction by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,318 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
scientific-prediction capabilities & compatibility
- Capabilities
- data analysis
- Use cases
- data analysis · research
- Pricing
- Free
What scientific-prediction says it does
Predict scientific outcomes, material properties, and time series using computational models and simulation.
Always report prediction uncertainty/confidence intervals
Validate on held-out data (never evaluate on training data)
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Build a predictive model for material properties or economic indicators with uncertainty intervals and baselines.
Who is it for?
Forecasting scientific or economic quantities with uncertainty and baseline comparison.
Skip if: Classifying discrete objects (use scientific-classification) or verifying claims.
When should I use this skill?
Predicting material properties, economic indicators, molecular properties, or climate trends.
What you get
Predictions with confidence intervals, evaluation metrics, and comparison against meaningful baselines on held-out data.
- predictions with confidence intervals
- evaluation metrics
- baseline comparison
By the numbers
- six-step prediction protocol
- Materials Project 133K+ materials referenced
Files
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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Does it report uncertainty?
Yes. It always reports prediction uncertainty or confidence intervals and compares against meaningful baselines.
How is it validated?
On held-out data only, never on the training data, with metrics such as RMSE, MAE, R2, and MAPE.