feat: sync content definitions from core repo
Copy all TOML content definitions from librefang core repo: - 33 agent definitions (agents/*/agent.toml) - 14 hand definitions with docs (hands/*/HAND.toml + SKILL.md) - 25 integration templates (integrations/*.toml) - 2 example skill definitions (skills/custom-skill-*) - 1 new provider (providers/vertex-ai.toml) Part of the framework-vs-content registry split (RFC v0.7).
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---
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name: analytics-hand-skill
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version: "1.0.0"
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description: "Expert knowledge for AI data analytics -- statistical methods, visualization best practices, pandas reference, and reporting patterns"
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runtime: prompt_only
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---
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# Data Analytics Expert Knowledge
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## pandas Quick Reference
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### Data Loading
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```python
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import pandas as pd
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# CSV
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df = pd.read_csv('data.csv')
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df = pd.read_csv('data.csv', parse_dates=['date_col'], index_col='id')
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# JSON
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df = pd.read_json('data.json')
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df = pd.read_json('data.json', orient='records')
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# Excel
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df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
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# From dict
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df = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']})
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```
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### Data Inspection
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```python
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df.shape # (rows, columns)
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df.dtypes # Column types
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df.info() # Summary including memory usage
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df.describe() # Statistical summary
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df.head(10) # First 10 rows
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df.isnull().sum() # Missing values per column
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df.duplicated().sum() # Number of duplicate rows
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df.nunique() # Unique values per column
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```
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### Data Cleaning
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```python
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# Handle missing values
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df.dropna() # Drop rows with any NaN
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df.fillna(0) # Fill NaN with 0
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df.fillna(df.mean()) # Fill with column means
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df['col'].interpolate() # Interpolate missing values
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# Remove duplicates
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df.drop_duplicates()
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df.drop_duplicates(subset=['col1', 'col2'])
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# Type conversion
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df['col'] = df['col'].astype(int)
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df['date'] = pd.to_datetime(df['date'])
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df['cat'] = df['cat'].astype('category')
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# Outlier removal (IQR method)
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Q1 = df['col'].quantile(0.25)
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Q3 = df['col'].quantile(0.75)
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IQR = Q3 - Q1
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df = df[(df['col'] >= Q1 - 1.5*IQR) & (df['col'] <= Q3 + 1.5*IQR)]
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```
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### Aggregation & Grouping
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```python
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# Group by
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df.groupby('category').agg({'value': ['mean', 'sum', 'count']})
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# Pivot table
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pd.pivot_table(df, values='value', index='row_cat', columns='col_cat', aggfunc='mean')
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# Cross tabulation
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pd.crosstab(df['cat1'], df['cat2'])
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# Rolling statistics
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df['rolling_mean'] = df['value'].rolling(window=7).mean()
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# Percentage change
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df['pct_change'] = df['value'].pct_change()
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```
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### Time Series
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```python
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# Set datetime index
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df.set_index('date', inplace=True)
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# Resample
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df.resample('W').mean() # Weekly average
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df.resample('M').sum() # Monthly sum
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df.resample('Q').count() # Quarterly count
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# Date range
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pd.date_range(start='2025-01-01', periods=30, freq='D')
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# Shift/Lag
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df['prev_value'] = df['value'].shift(1)
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df['next_value'] = df['value'].shift(-1)
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```
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---
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## Visualization Best Practices
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### matplotlib + seaborn Reference
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```python
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Set style
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sns.set_theme(style='whitegrid')
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plt.rcParams['figure.figsize'] = (10, 6)
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```
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### Chart Selection Guide
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| Data Type | Question | Chart Type |
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|-----------|----------|------------|
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| Categorical | Comparison | Bar chart |
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| Categorical | Proportion | Pie chart (if <6 categories) |
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| Numerical | Distribution | Histogram / Box plot |
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| Two numerical | Relationship | Scatter plot |
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| Time series | Trend | Line chart |
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| Matrix | Correlation | Heatmap |
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| Categories + values | Comparison | Grouped bar / Stacked bar |
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| Geographical | Location | Map / Choropleth |
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### Chart Templates
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**Bar Chart**:
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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data = df['category'].value_counts()
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data.plot(kind='bar', ax=ax, color='steelblue')
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ax.set_title('Distribution by Category', fontsize=14, fontweight='bold')
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ax.set_xlabel('Category')
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ax.set_ylabel('Count')
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plt.xticks(rotation=45, ha='right')
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plt.tight_layout()
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plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
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plt.close()
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```
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**Line Chart (Time Series)**:
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```python
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fig, ax = plt.subplots(figsize=(12, 6))
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ax.plot(df.index, df['value'], linewidth=2, color='steelblue')
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ax.fill_between(df.index, df['value'], alpha=0.1, color='steelblue')
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ax.set_title('Trend Over Time', fontsize=14, fontweight='bold')
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ax.set_xlabel('Date')
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ax.set_ylabel('Value')
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plt.tight_layout()
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plt.savefig('line_chart.png', dpi=150, bbox_inches='tight')
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plt.close()
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```
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**Correlation Heatmap**:
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```python
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fig, ax = plt.subplots(figsize=(10, 8))
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corr = df.select_dtypes(include='number').corr()
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sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0, ax=ax)
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ax.set_title('Correlation Matrix', fontsize=14, fontweight='bold')
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plt.tight_layout()
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plt.savefig('heatmap.png', dpi=150, bbox_inches='tight')
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plt.close()
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```
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**Scatter Plot**:
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.scatter(df['x'], df['y'], alpha=0.6, edgecolors='black', linewidth=0.5)
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ax.set_title('X vs Y', fontsize=14, fontweight='bold')
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ax.set_xlabel('X Variable')
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ax.set_ylabel('Y Variable')
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plt.tight_layout()
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plt.savefig('scatter.png', dpi=150, bbox_inches='tight')
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plt.close()
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```
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### Visualization Do's and Don'ts
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**Do**:
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- Start y-axis at 0 for bar charts
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- Use consistent colors across related charts
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- Label axes clearly with units
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- Add titles that describe the insight, not just the data
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- Use appropriate scales (log scale for exponential data)
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**Don't**:
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- Use 3D charts (distorts perception)
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- Use more than 6-7 colors in one chart
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- Truncate axes to exaggerate differences
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- Use pie charts for more than 5 categories
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- Add unnecessary chart junk (borders, backgrounds, grids)
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---
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## Statistical Methods
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### Descriptive Statistics
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| Measure | pandas | Purpose |
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|---------|--------|---------|
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| Mean | `df['col'].mean()` | Central tendency |
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| Median | `df['col'].median()` | Robust central tendency |
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| Std Dev | `df['col'].std()` | Variability |
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| Skewness | `df['col'].skew()` | Distribution symmetry |
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| Kurtosis | `df['col'].kurtosis()` | Distribution tails |
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| Percentiles | `df['col'].quantile([0.25, 0.5, 0.75])` | Distribution spread |
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### Correlation Analysis
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```python
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# Pearson correlation (linear)
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df['col1'].corr(df['col2'])
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# Spearman correlation (monotonic)
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df['col1'].corr(df['col2'], method='spearman')
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# Full correlation matrix
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df.select_dtypes(include='number').corr()
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```
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Interpretation:
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- |r| > 0.7: Strong correlation
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- 0.4 < |r| < 0.7: Moderate correlation
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- |r| < 0.4: Weak correlation
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- Correlation != Causation
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### Hypothesis Testing (scipy)
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```python
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from scipy import stats
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# T-test (compare two group means)
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t_stat, p_value = stats.ttest_ind(group1, group2)
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# Chi-squared test (categorical independence)
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chi2, p_value, dof, expected = stats.chi2_contingency(contingency_table)
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# Significance: p < 0.05 is commonly used threshold
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# Mann-Whitney U test (non-parametric alternative to t-test)
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u_stat, p_value = stats.mannwhitneyu(group1, group2, alternative='two-sided')
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# One-way ANOVA (compare 3+ group means)
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f_stat, p_value = stats.f_oneway(group1, group2, group3)
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# Normality check (determines which test to use)
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shapiro_stat, p_value = stats.shapiro(data) # p > 0.05 means normal
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```
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### Statistical Significance Decision Guide
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**Test selection flowchart:**
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| Data Situation | Normal Distribution? | Test to Use |
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|---------------|---------------------|-------------|
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| Compare 2 group means | Yes | Independent t-test (`ttest_ind`) |
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| Compare 2 group means | No | Mann-Whitney U (`mannwhitneyu`) |
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| Compare 3+ group means | Yes | One-way ANOVA (`f_oneway`) |
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| Compare 3+ group means | No | Kruskal-Wallis (`kruskal`) |
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| Compare paired samples | Yes | Paired t-test (`ttest_rel`) |
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| Compare paired samples | No | Wilcoxon signed-rank (`wilcoxon`) |
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| Test categorical independence | N/A | Chi-squared (`chi2_contingency`) |
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| Test correlation | Yes | Pearson (`pearsonr`) |
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| Test correlation | No | Spearman (`spearmanr`) |
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**P-value interpretation:**
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| p-value | Interpretation | Action |
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|---------|---------------|--------|
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| p < 0.01 | Strong evidence against null hypothesis | Report as statistically significant |
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| 0.01 ≤ p < 0.05 | Moderate evidence | Report as significant with caveat |
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| 0.05 ≤ p < 0.10 | Weak evidence | Report as marginally significant |
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| p ≥ 0.10 | Insufficient evidence | Do not claim significance |
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**Practical significance — always report effect size:**
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```python
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# Cohen's d for comparing two means
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def cohens_d(group1, group2):
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n1, n2 = len(group1), len(group2)
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var1, var2 = group1.var(), group2.var()
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pooled_std = ((n1 - 1) * var1 + (n2 - 1) * var2) / (n1 + n2 - 2)
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return (group1.mean() - group2.mean()) / (pooled_std ** 0.5)
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# Interpretation: |d| < 0.2 = negligible, 0.2-0.5 = small, 0.5-0.8 = medium, > 0.8 = large
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```
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**Sample size awareness:**
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- n < 30: Use non-parametric tests; results are exploratory
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- 30 ≤ n < 100: Parametric tests OK if normality holds; moderate confidence
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- n ≥ 100: Central Limit Theorem applies; high confidence in parametric tests
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- Always report sample size alongside p-values
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**Confidence threshold mapping:**
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| Setting | p-value threshold | Minimum effect size | Minimum sample size |
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|---------|------------------|--------------------|--------------------|
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| High | p < 0.01 | Cohen's d ≥ 0.5 | n ≥ 100 |
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| Medium | p < 0.05 | Cohen's d ≥ 0.3 | n ≥ 30 |
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| Low | p < 0.10 | Any | Any |
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---
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## Report Structure Best Practices
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### CRISP-DM Framework
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1. **Business Understanding**: What question are we answering?
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2. **Data Understanding**: What data do we have? Quality?
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3. **Data Preparation**: Cleaning, transformation, feature engineering
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4. **Modeling**: Statistical analysis, ML models
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5. **Evaluation**: Are results valid and useful?
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6. **Deployment**: Reports, dashboards, recommendations
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### Insight Hierarchy
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```
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Level 1: What happened (descriptive)
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"Revenue increased 15% last quarter"
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Level 2: Why it happened (diagnostic)
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"Revenue increase driven by 30% growth in enterprise segment"
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Level 3: What will happen (predictive)
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"Based on current trends, Q2 revenue projected at $X"
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Level 4: What to do (prescriptive)
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"Invest in enterprise sales team to capitalize on growth trajectory"
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```
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### Data Quality Assessment Template
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```
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| Dimension | Score | Details |
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|-----------|-------|---------|
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| Completeness | 85% | 15% missing values in 'email' column |
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| Accuracy | High | Validated against source system |
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| Consistency | Medium | Date formats vary across sources |
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| Timeliness | Current | Data refreshed daily |
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| Uniqueness | 99% | 1% duplicate records found |
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```
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