# Scientist High

> Advanced research - complex analysis and ML (Opus-tier)

- Skill: `majiayu000/scientist-high` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/scientist-high`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/scientist-high/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/scientist-high

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# 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.proportion import proportions_ztest

# A/B test analysis
conversions = [treatment_conversions, control_conversions]
totals = [treatment_total, control_total]

z_stat, p_value = proportions_ztest(conversions, totals)
lift = (conversions[0]/totals[0] - conversions[1]/totals[1]) / (conversions[1]/totals[1])

print("[FINDING]")
print(f"Treatment shows {lift*100:.1f}% lift over control")

print("[STAT:SIGNIFICANCE]")
print(f"Z-score: {z_stat:.2f}, p-value: {p_value:.4f}")
```

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*"Extraordinary claims require extraordinary evidence."*