Data Analysis - Statistical Computing & Insights

When to use this skill

Activate this skill when:

• User mentions "数据分析", "统计", "计算指标", "数据洞察"

• Need to analyze structured data (CSV, JSON, database)

• Calculate statistics, trends, patterns

• Financial analysis (returns, volatility, technical indicators)

• Business analytics (sales, user behavior, KPIs)

• Scientific data processing and hypothesis testing

Workflow

1. Get data

⚠️ IMPORTANT: File naming requirements

• File names MUST NOT contain Chinese characters or non-ASCII characters

• Use only English letters, numbers, underscores, and hyphens

• Examples: data.csv , sales_report_2025.xlsx , analysis_results.json

• ❌ Invalid: 销售数据.csv , 数据文件.xlsx , 報表.json

• This ensures compatibility across different systems and prevents encoding issues

If data already exists:

• Read from file (CSV, JSON, Excel)

• Query database if available

If file names contain Chinese characters:

• Ask the user to rename the file to English/ASCII characters

• Or rename the file when saving it to the agent directory

If no data:

• Automatically activate data-base skill

• Scrape/collect required data

• Save to structured format

2. Understand requirements

Ask the user:

• What questions do you want to answer?

• What metrics are important?

• What format for results? (summary, chart, report)

• Any specific statistical methods?

3. Analyze

General analysis:

• Descriptive statistics (mean, median, std, percentiles)

• Distribution analysis (histograms, box plots)

• Correlation analysis

• Group comparisons

Financial analysis:

• Return calculation (simple, log, cumulative)

• Risk metrics (volatility, VaR, Sharpe ratio)

• Technical indicators (MA, RSI, MACD)

• Portfolio analysis

Business analysis:

• Trend analysis (growth rates, YoY, MoM)

• Cohort analysis

• Funnel analysis

• A/B testing

Scientific analysis:

• Hypothesis testing (t-test, chi-square, ANOVA)

• Regression analysis

• Time series analysis

• Statistical significance

4. Output

Generate results in:

• Summary statistics: Tables with key metrics

• Charts: Save as PNG files

• Report: Markdown with findings

• Data: Processed CSV/JSON for further use

Python Environment

Auto-initialize virtual environment if needed, then execute:

cd skills/data-analysis

if [ ! -f ".venv/bin/python" ]; then echo "Creating Python environment..." ./setup.sh fi

.venv/bin/python your_script.py

The setup script auto-installs: pandas, numpy, scipy, scikit-learn, statsmodels, with Chinese font support.

Analysis scenarios

General data

import pandas as pd

Load and summarize

df = pd.read_csv('data.csv') summary = df.describe() correlations = df.corr()

Financial data

Calculate returns

df['return'] = df['price'].pct_change()

Risk metrics

volatility = df['return'].std() * (252 ** 0.5) sharpe = df['return'].mean() / df['return'].std() * (252 ** 0.5)

Business data

Group by category

grouped = df.groupby('category').agg({ 'revenue': ['sum', 'mean', 'count'] })

Growth rate

df['growth'] = df['revenue'].pct_change()

Scientific data

from scipy import stats

T-test

t_stat, p_value = stats.ttest_ind(group_a, group_b)

Regression

from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y)

File path conventions

Temporary output (session-scoped)

Files written to the current directory will be stored in the session directory:

import time from datetime import datetime

Use timestamp for unique filenames (avoid conflicts)

timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')

Charts and temporary files

plt.savefig(f'analysis_{timestamp}.png') # → $KODE_AGENT_DIR/analysis_20250115_143022.png df.to_csv(f'results_{timestamp}.csv') # → $KODE_AGENT_DIR/results_20250115_143022.csv

Always use unique filenames to avoid conflicts when running multiple analyses:

• Use timestamps: analysis_20250115_143022.png

• Use descriptive names + timestamps: sales_report_q1_2025.csv

• Use random suffix for scripts: script_{random.randint(1000,9999)}.py

User data (persistent)

Use $KODE_USER_DIR for persistent user data:

import os user_dir = os.getenv('KODE_USER_DIR')

Save to user memory

memory_file = f"{user_dir}/.memory/facts/preferences.jsonl"

Read from knowledge base

knowledge_dir = f"{user_dir}/.knowledge/docs"

Environment variables

• KODE_AGENT_DIR : Session directory for temporary output (charts, analysis results)

• KODE_USER_DIR : User data directory for persistent storage (memory, knowledge, config)

Best practices

• File names MUST be ASCII-only: No Chinese or non-ASCII characters in filenames

• Always inspect data first: df.head() , df.info() , df.describe()

• Handle missing values: Drop or impute based on context

• Check assumptions: Normality, independence, etc.

• Visualize: Charts reveal patterns tables hide

• Document findings: Explain metrics and their implications

• Use correct paths: Temporary outputs to current dir, persistent data to $KODE_USER_DIR

Quick reference

• REFERENCE.md - pandas/numpy API reference

• references/financial.md - Financial analysis recipes

• references/business.md - Business analytics recipes

• references/scientific.md - Statistical testing methods

• references/templates.md - Code templates

Environment setup

This skill uses Python scripts. To set up the environment:

Navigate to the skill directory

cd apps/assistant/skills/data-analysis

Run the setup script (creates venv and installs dependencies)

./setup.sh

Activate the environment

source .venv/bin/activate

The setup script will:

• Create a Python virtual environment in .venv/

• Install required packages (pandas, numpy, scipy, scikit-learn, statsmodels)

To run Python scripts with the skill environment:

Use the virtual environment's Python

.venv/bin/python script.py

Or activate first, then run normally

source .venv/bin/activate python script.py