據 AlphaMoat 統計,截至 2026-09,Data Analysis Pro 累計下載 2,590 次、獲 1 個星標,在全部 5,078 個 AI 技能中排名第 4,030 位,在「資料分析」分類(399 個)中排名第 306 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 37 | 1400 | - | 1 |
| SkillHub ↗ | 121 | 2590 | 1 | — |
各平台的計數口徑不同,統計時間不一致。
主文說明它支援 Text2SQL、Text2Data 和 Text2Code,並使用 ChartGen API 生成 Markdown 分析結果、圖表配置及可選 HTML 圖表;需要配置 CHARTGEN_API_KEY,呼叫會消耗額度,所提供的資料會傳送到 ChartGen 處理。只提交明確允許傳送的檔案,避免敏感或受監管資料,並使用範圍受限的金鑰;生成的 HTML 報告在審查前應視為不可信。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 2590 | — | 1 |
| 2026-08 | 2218 | +10.5% | 1 |
| 2026-07 | 2007 | +12.4% | 1 |
| 2026-06 | 1786 | +10.0% | 1 |
| 2026-05 | 1623 | — | 1 |
| 2026-03 | 646 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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 的取數層:把廣告與應用相關的查詢轉成介面呼叫,只回原始 JSON。
透過 Tavily 搜尋公開網頁,並回傳含連結與摘要的結構化結果。
把資料庫、表格與匯出的原始資料變成可決策結論的資料分析技能。
用 Yahoo Finance 資料做股票與加密貨幣分析、組合跟蹤的技能。
管理本地自選股清單,並按代碼彙總同花順行情與近期表現的技能。
知了標訊:查詢和分析招標、中標、供應商與採購市場資訊。
用命令列監控部落格與 RSS/Atom 源的更新。
彙總 Codex 或 Claude 按模型計費的使用成本。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。