据 AlphaMoat 统计,截至 2026-09,Data Analysis Pro 累计下载 2,590 次、获 1 个星标,在全部 5,078 个 AI 技能中排名第 4,030 位,在「数据分析」分类(399 个)中排名第 306 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 37 | 1,400 | - | 1 |
| SkillHub ↗ | 121 | 2,590 | 1 | — |
各平台的计数口径不同,统计时间不一致。
主文说明它支持 Text2SQL、Text2Data 和 Text2Code,并使用 ChartGen API 生成 Markdown 分析结果、图表配置及可选 HTML 图表;需要配置 CHARTGEN_API_KEY,调用会消耗额度,所提供的数据会发送到 ChartGen 处理。只提交明确允许发送的文件,避免敏感或受监管数据,并使用范围受限的密钥;生成的 HTML 报告在审查前应视为不可信。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 2,590 | — | 1 |
| 2026-08 | 2,218 | +10.5% | 1 |
| 2026-07 | 2,007 | +12.4% | 1 |
| 2026-06 | 1,786 | +10.0% | 1 |
| 2026-05 | 1,623 | — | 1 |
| 2026-03 | 646 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: data-analysis-pro description: | Data analysis pro skill providing three core functions: data analysis, data interpretation, and data visualization. **Use Cases**: (1) Data Analysis - Statistics, filtering, aggregation, calculation (e.g., "Calculate total sales", "Filter data greater than 100") (2) Data Interpretation - Trend analysis, pattern discovery, report generation (e.g., "Analyze sales trends", "Interpret data changes") (3) Data Visualization - Chart generation, data display (e.g., "Draw a bar chart", "Generate a pie chart")
**Trigger Keywords**: analyze data, statistics, calculate, interpret trends, generate chart, visualize, plot
**Prerequisites**: Set environment variable CHARTGEN_API_KEY (obtain from chartgen.ai)
metadata:
openclaw:
requires:
env:
- CHARTGEN_API_KEY
---
# ChartGen Data Analysis
Data analysis skill based on ChartGen API, supporting natural language-based data analysis, interpretation, and visualization.
## Overview
This skill enables codeless data analysis through natural language interaction. It supports Text2SQL, Text2Data, and Text2Code analysis. Simply provide Excel/CSV files or JSON data to automatically execute data queries, data interpretation, and data visualization (ChatBI).
The skill will intelligently parse time, metrics, and analytical dimensions through conversational queries, then generate SQL queries for data, create interactive BI charts, structured analysis reports. Optimized for standardized vertical datasets, powered by enterprise-grade analytics engine for reliable results.
**API Service**: This skill uses the ChartGen API service hosted at [chartgen.ai](https://chartgen.ai). All data is sent to `https://chartgen.ai/api/platform_api/` for processing.
---
## Quick Start
### 1. Apply for an API Key
You can easily create and manage your API Key at [chartgen.ai](https://chartgen.ai). To begin with, you need to register for an account.
**Steps:**
1. Visit [chartgen.ai](https://chartgen.ai) and sign up for an account
2. Access the API management dashboard
3. Create a new API and set the credit consumption limit
4. Copy the API Key for use
### 2. Configure Environment Variable
```bash
export CHARTGEN_API_KEY="your-api-key-here"
```
### 3. Run Scripts
```bash
# Data Analysis
python scripts/data_analysis.py --query "Calculate total sales by region" --file sales.xlsx
# Data Interpretation
python scripts/data_interpretation.py --query "Analyze sales trends" --file sales.xlsx
# Data Visualization
python scripts/data_visualization.py --query "Draw a bar chart of sales by region" --file sales.xlsx
```
---
## Credit Rules
- Calling a single tool consumes 20 credits
- You get 200 free credits per month for free accounts
- When credits run out, you can purchase more or upgrade your account on the [chartgen.ai Billing page](https://chartgen.ai/billing)
---
## Scripts Reference
| Script | Function | Use Case |
|--------|----------|----------|
| `data_analysis.py` | Data Analysis | Statistics, filtering, aggregation, calculation |
| `data_interpretation.py` | Data Interpretation | Trend analysis, pattern discovery, report generation |
| `data_visualization.py` | Data Visualization | Chart generation, data display |
---
## Parameters
### Common Parameters
| Parameter | Required | Description |
|-----------|----------|-------------|
| `--query` | ✅ | Natural language query statement |
| `--file` | ❌ | Local file path (.xlsx/.xls/.csv), mutually exclusive with --json |
| `--json` | ❌ | JSON data (string or file path), mutually exclusive with --file |
### Visualization Specific Parameters
| Parameter | Description |
|-----------|-------------|
| `--output, -o` | Output HTML file path (defaults to /tmp/openclaw/charts/) |
---
## Data Format
### File Format
Supports `.xlsx`, `.xls`, `.csv` Excel and CSV files.
Note: Only one of --file or --json is needed. If both are provided, --file takes precedence. File types support both row-metric-column data files and column-metric-row data files.
### JSON Format
JSON data should be an array format, where each element is a row of data:
```json
[
{"name": "Product A", "sales": 1000, "region": "East"},
{"name": "Product B", "sales": 1500, "region": "North"},
{"name": "Product C", "sales": 800, "region": "South"}
]
```
Or pass via file:
```bash
python scripts/data_analysis.py --query "Analyze the data" --json data.json
```
---
## Usage Examples
### Data Analysis
```bash
# Statistical calculation
python scripts/data_analysis.py --query "Calculate total and average sales by region" --file sales.xlsx
# Data filtering
python scripts/data_analysis.py --query "Filter products with sales greater than 1000" --file sales.xlsx
# Sorting
python scripts/data_analysis.py --query "Sort by sales in descending order" --file sales.xlsx
```
### Data Interpretation
```bash
# Trend analysis
python scripts/data_interpretation.py --query "Analyze monthly sales trends" --file monthly_sales.xlsx
# Anomaly detection
python scripts/data_interpretation.py --query "Find and explain anomalies in the data" --file data.xlsx
# Comprehensive interpretation
python scripts/data_interpretation.py --query "Provide a comprehensive analysis of this data with key insights" --file report.xlsx
```
### Data Visualization
```bash
# Bar chart
python scripts/data_visualization.py --query "Draw a bar chart of sales by product" --file sales.xlsx
# Line chart
python scripts/data_visualization.py --query "Draw a line chart of sales trends" --file trends.xlsx
# Pie chart
python scripts/data_visualization.py --query "Draw a pie chart of sales by region" --file sales.xlsx
# Save to specific path
python scripts/data_visualization.py --query "Draw a scatter plot" --file data.xlsx -o /path/to/chart.html
```
---
## Output Description
### Data Analysis & Data Interpretation
Returns Markdown format text results, including analysis conclusions, data tables, etc.
### Data Visualization
1. **Console Output**: ECharts configuration JSON
2. **HTML File**: Can be opened in browser to view the chart
---
## Error Handling
Common errors and solutions:
| Error Message | Cause | Solution |
|---------------|-------|----------|
| `CHARTGEN_API_KEY not set` | Environment variable not set | `export CHARTGEN_API_KEY="your-key"` |
| `API request timeout` | Request timeout | Check network connection and retry |
| `File not found` | File does not exist | Check if file path is correct |
| `credits are insufficient` | Insufficient credits | Recharge or contact administrator |
---
## Technical Details
- **API Base URL**: `https://chartgen.ai/api/platform_api/`
- **Authentication**: Header `Authorization: <api-key>`
- **Request Format**: JSON
- **Timeout**: 60 seconds
- **Required Environment Variable**: `CHARTGEN_API_KEY`
See `scripts/chartgen_api.py` for implementation details.
---
## Privacy Notice
**Data sent to remote API**: This skill reads your provided data files (CSV/XLSX/JSON), base64-encodes them, and sends them to the ChartGen API at `https://chartgen.ai/api/platform_api/` for analysis and chart generation. Your data will leave your machine.
**Recommendations**:
- Do not upload sensitive or regulated data
- Use a dedicated API key with limited scope/credits
- Review the privacy practices at [chartgen.ai](https://chartgen.ai) before use
AdMapix 的取数层:把广告与应用相关的查询转成接口调用,只回原始 JSON。
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把数据库、表格与导出的原始数据变成可决策结论的数据分析技能。
用 Yahoo Finance 数据做股票与加密货币分析、组合跟踪的技能。
管理本地自选股清单,并按代码汇总同花顺行情与近期表现的技能。
知了标讯:查询和分析招标、中标、供应商与采购市场信息。
用命令行监控博客与 RSS/Atom 源的更新。
汇总 Codex 或 Claude 按模型计费的使用成本。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。