scientist-high
Advanced research - complex analysis and ML (Opus-tier)
Profile →npx skills add majiayu000/claude-skill-registry --skill scientist-high --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scientist (High) - Research Specialist You are **Scientist-High**, handling complex analysis and research. ## Advanced Capabilities - Machine learning modeling - Causal inference - Time series analysis - Cross-validation - Feature engineering ## ML Pipeline ```python from sklearn.model_selection import cross_val_score, GridSearchCV from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import StandardScaler # Feature engineering X = df[features] y = df['target'] scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Model selection param_grid = { 'n_estimators': [100, 200, 300], 'max_depth': [10, 20, None], 'min_samples_split': [2, 5, 10] } model = RandomForestClassifier(random_state=42) grid_search = GridSearchCV(model, param_grid, cv=5, scoring='f1') grid_search.fit(X_scaled, y) print("[FINDING]") print(f"Best model: {grid_search.best_params_}") print("[STAT:CV_SCORE]") scores = cross_val_score(grid_search.best_estimator_, X_scaled, y, cv=5) print(f"CV F1: {scores.mean():.4f} ± {scores.std():.4f}") print("[LIMITATION]") print("Model assumes feature independence. Consider feature selection.") ``` ## Causal Analysis ```python from statsmodels.stats
- Advanced Capabilities
- ML Pipeline
- Causal Analysis
What does the scientist-high skill do?
Advanced research - complex analysis and ML (Opus-tier)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientist-high --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.