数据分析师skill

数据分析师自动化工作流。从数据加载、质量审计、数据清洗、探索性分析(EDA)、统计建模到可视化HTML报告生成，覆盖完整数据分析管线。支持CSV/Excel/JSON/SQLite多格式输入，内置4层数据防御体系。触发词：分析数据、数据分析、帮我分析数据、数据报告、EDA、data analysis、analyze data、生成数据报告、数据可视化、探索性分析。

bettermen@bettermen

Install

openclaw skills install @bettermen/data-analyst-pipeline

数据分析师 (Data Analyst)

AI-powered data analysis workflow. Cover the full pipeline from data ingestion to interactive HTML report generation.

When to Use

Trigger when the user asks to:

Analyze a dataset (CSV / Excel / JSON / SQLite)
Generate a data analysis report
Do exploratory data analysis (EDA)
Clean or preprocess data
Create data visualizations
Understand data distributions and relationships

Workflow Overview

The skill follows a 7-phase CRISP-DM pipeline, executed automatically:

Data Loading — Auto-detect format, load into DataFrame
Data Audit — 4-layer defense: health check, structure, business rules, model readiness
Data Cleaning — Missing values, outliers, type conversion, dedup
EDA — Distribution analysis, correlation, group aggregation
Statistical Analysis — Descriptive stats, hypothesis tests, trend detection
Visualization — Charts for distributions, correlations, category breakdowns
Report Generation — Interactive HTML report with scorecards, charts, and insights

Usage

Quick Start

To analyze a data file:

bash

python {baseDir}/scripts/run_analysis.py <data_file> [--output report.html]

The script auto-detects the file format and runs the full pipeline.

Module-Level Usage

Each module can be used independently:

python

# Load data
from data_loader import load_data
df = load_data("sales.csv")

# Audit data quality
from data_auditor import audit_data
report = audit_data(df)

# Clean data
from data_cleaner import clean_data
df_clean = clean_data(df)

# Run EDA
from eda_runner import run_eda
eda_results = run_eda(df_clean)

# Generate report
from report_builder import build_report
build_report(df_clean, eda_results, "report.html")

Scripts Reference

Script	Purpose	Input	Output
`scripts/run_analysis.py`	Main entry — orchestrates full pipeline	data file path	HTML report
`scripts/data_loader.py`	Multi-format data loading	file path	pandas DataFrame
`scripts/data_auditor.py`	4-layer quality defense	DataFrame	audit dict
`scripts/data_cleaner.py`	Data cleaning & preprocessing	DataFrame	cleaned DataFrame
`scripts/eda_runner.py`	Exploratory data analysis	DataFrame	EDA results dict
`scripts/visualizer.py`	Chart generation	DataFrame + config	saved .png charts
`scripts/report_builder.py`	HTML report generation	Data + results	HTML report

Templates

templates/report.html — Jinja2 template for the final HTML report

Config

config/business_rules.yaml — Optional business validation rules

Dependencies

Install before first use: