Zafer-Liu/Data-Analysis-Agent

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SkillsMP 收录数据更新: 2026年11月12日 12:52

已收集 skills: 30

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English · <a href="./README_CN.md">简体中文</a> <h1 align="center">Data Analysis Agent</h1> Ask your data. Get SQL, charts, and business insights. A conversational workspace for business analysis, from spreadsheets to SQL databases. <div align="center"> <a href="https://trendshift.io/repositories/29216?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-29216" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/29216/daily?language=JavaScript" alt="Zafer-Liu%2FData-Analysis-Agent | Trendshift" width="250" height="55"/></a> [![Website](https://img.shields.io/badge/%F0%9F%8C%90%20Website-sage--agent.online-2F74FF.svg)](https://sage-agent.online/) ![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg) ![Flask](https://img.shields.io/badge/Backend-Flask-black.svg) ![Plotly](https://img.shields.io/badge/Visualization-Plotly-3F4F75.svg) ![LLM](https://img.shields.io/badge/LLM-OpenAI%20Compatible-green.svg) ![Charts](https://img.shields.io/badge/Charts-41_Types-orange.svg) ![License](https://img.shields.io/badge/License-CC%20BY--NC%204.0-blue.svg) </div> <a href="#sponsors">Sponsors</a> · <a href="#demo">Demo</a> · <a href="#quick-start">Quick start</a> · <a href="#capabilities">Capabilities</a> · <a href="#install">Installation</a> · <a href="https://sage-agent.online/">Website</a> · <a href="https://github.com/Zafer-Liu/Data-Analysis-Agent/releases/latest">Releases</a> <a href="https://discord.gg/EEG4Sw7tde">Discord</a> · <a href="https://t.me/+cdRNfS68u9BlYjJl">Telegram</a> · QQ: <code>991636855</code> <a id="sponsors"></a> ## 🙏 Sponsors Thank you to the sponsors supporting this project! ### ☁️ Sponsored by DolOffer <a href="https://doloffer.com/"><img src="./Information/assets/DolOffer.png" alt="DolOffer logo" height="80" /></a> Thank you to DolOffer for supporting this project! DolOffer is a platform focused on digital product recommendations and deal sharing, helping users quickly discover useful tools, services, and limited-time offers. The platform provides popular subscription services such as YouTube Premium, Claude, ChatGPT Plus, Spotify, Apple Music, and more, with prices as low as 30% of the official price or even lower. All services are genuine, stable, and backed by reliable after-sales support. Register through our exclusive link and use the coupon code **AI8888** when topping up to enjoy an additional 10% discount. 👉 **[Learn more](https://github.com/Doloffer-g/guide)** --- ### ☁️ Sponsored by Mozhenzhen <a href="https://mozhenzhen.com/go/data-analysis-agent"><img src="./Information/assets/Mozhenzhen.png" alt="Mozhenzhen logo" height="80" /></a> Mozhenzhen — a consumer anti-pitfall guide for developers and AI enthusiasts. Whether you are looking for real-time API price comparison, latency benchmarking and authenticity verification (no watered-down or fake models), comparing after-sales warranties, risk prevention and hidden traps among account / subscription top-up providers, or even discovering free community relays and backup nodes, Mozhenzhen uses transparent data to help you avoid pitfalls and spend every cent wisely. 👉 **[Visit Mozhenzhen](https://mozhenzhen.com/go/data-analysis-agent)** --- ### ☁️ Sponsored by Infistar <a href="https://www.infistar.cc/register?aff=U2DQQJPT&ref_source=link"><img src="./Information/assets/InfistarEN.png" alt="Infistar banner" height="160" /></a> Thank you to Infistar.cc for sponsoring Data-Analysis-Agent! It provides stable and efficient multi-model API services that fit perfectly into data cleaning, code generation, chart plotting and automated analysis report workflows. ⚙️ Compatible integration: select the OpenAI-compatible interface in the project, configure the Base URL and API Key, and you are ready to go. 🧩 Multi-model support: brings together top models such as Claude, GPT, Gemini and DeepSeek, making it easy to switch between them and compare analysis and code generation results. 🏷️ Pricing & service: selected models from as low as 1% of official pricing, with public and transparent multipliers, RMB settlement, invoicing and model verification. 🎁 Exclusive perk: sign up via our [exclusive promo link](https://www.infistar.cc/register?aff=U2DQQJPT&ref_source=link) and get $5 in free credits! 👉 **[Register on Infistar · Claim $5 credits](https://www.infistar.cc/register?aff=U2DQQJPT&ref_source=link)** --- <a id="demo"></a> ## See it in action <img src="./Information/assets/analysis-demo.gif" alt="Animated analysis workflow: connect sales data, ask a question, generate SQL, and reveal a chart and insights" width="100%" /> *Illustrated workflow with fictional sales data; not a live screen recording.* [View the still preview](./Information/assets/analysis-demo.png) --- <a id="features"></a> ## Why Data Analysis Agent? | Capability | What you can do | | --- | --- | | **Ask in plain language** | Turn a business question into SQL, a chart, and an explanation. | | **Bring your data** | Upload Excel / CSV files or connect a SQL database. | | **Follow the analysis** | Watch schema discovery, queries, and results stream over SSE. | | **Visualize and export** | Choose from 41 chart types and export Excel, Word, or PowerPoint deliverables. | | **Choose your model** | Connect OpenAI-compatible APIs or discover compatible local services. | | **Extend the workflow** | Add business knowledge, analysis Skills, MCP tools, or Feishu Bitable integration. | --- <a id="quick-start"></a> ## Quick start Requires **Python 3.10+**. Run in a dedicated virtual environment: ```bash git clone https://github.com/Zafer-Liu/Data-Analysis-Agent.git cd Data-Analysis-Agent python -m venv .venv ``` Activate the environment: ```powershell # Windows PowerShell .\.venv\Scripts\Activate.ps1 ``` ```bash # macOS / Linux source .venv/bin/activate ``` ```bash python -m pip install -r requirements.txt python app.py ``` Open [localhost:5001](http://localhost:5001), configure your model API key, upload Excel / CSV or connect a database, and ask: ```text Show monthly sales and plot the trend. ``` Running the source directly requires no cloud login. See [installation options](#install) and [model configuration](#llm-config) for more details. Optional machine-learning, deep-learning, packaging, and test dependencies live in [`requirements/`](./requirements/README.md). See the [product specification](./Information/PRODUCT.md) and [frontend build guide](./frontend/README.md) for development details. --- <details> <summary>Table of contents</summary> - [Sponsors](#sponsors) - [Capabilities](#capabilities) - [Installation](#install) - [Slash commands](#commands) - [Examples](#examples) - [LLM configuration](#llm-config) - [Agent Manager](#agent-manager) - [Changelog](#changelog) - [FAQ](#faq) - [Contributing](#contributing) - [License](#license) - [Project goal](#goal) </details> --- <a id="capabilities"></a> ## 🧠 Core Capabilities ### 1️⃣ Natural Language Data Analysis No SQL required — just type in plain language: ```text Monthly order volume trend for this year ``` The system will automatically: - Generate SQL - Query the data - Recommend a chart - Summarize the analysis ![Data Query](./Information/assets/Data_query.png) ### 2️⃣ Multi-Source Data Support Supports uploading and connecting to multiple data sources: - **Files**: Excel / CSV - **Databases**: SQLite, MySQL, PostgreSQL, SQL Server - **Planned**: DuckDB, Spark ![Data Preview](./Information/assets/Data_preview.png) ### 3️⃣ Intelligent Chart System Based on your query results, the system automatically recommends the most appropriate chart from 6 major categories: | Category | Chart Types | |---|---| | **COMPARING** | Marimekko_ABS, Marimekko_PCT, Bar_Chart, Grouped_Bar_Chart, Stacked_Bar_Chart, Diverging_Bar_Chart, Dot_Plot, Waffle, Bullet_Chart, Sankey_Chart, Heatmap, Waterfall | | **TIME** | Line_Chart, Circular_Line_Chart, Slope_Chart, Sparkline, Bump_Chart, Cycle_Chart, Area_Chart, Stacked_Area_Chart, Horizon_Chart, Connected_Scatter | | **DISTRIBUTION** | Histogram_Pareto_chart, Pyramid_Chart, Error_Bar_Chart, Box-and-Whisker_Plot, Violin_Chart, Ridgeline_Plot, Beeswarm_Plot, stem_leaf | | **GEOSPATIAL** | Flow_Map, Dot_Density_Map, Choropleth_Map | | **RELATIONSHIP** | Scatter_Plot, Bubble_Plot, Radar_Charts, Chord_Diagram, Arc_Chart, Network_Diagram, Parallel_Coordinates_Plot | | **PART-TO-WHOLE** | Treemap, Sunburst_Diagram, Nightingale_Chart, Pie_Chart | ![Auto Generated](./Information/assets/Auto_generated_image.png) ### 4️⃣ SSE Streaming Analysis Experience The analysis process is visible in real time: ```text [1/4] Reading data structure... [2/4] Generating SQL... [3/4] Executing query... [4/4] Generating chart and insights... ``` More transparent and interactive than traditional BI tools. ### 5️⃣ Multi-Model Compatibility Supports the following model providers: - DeepSeek - OpenAI - AtlasCloud - Any OpenAI SDK-compatible API Supports custom `base_url`, `model`, and `api_key`. Default configurations: | Provider | Default Model | |---|---| | DeepSeek | `deepseek-v4-flash` | | OpenAI | `gpt-4o-mini` | | AtlasCloud | `deepseek-v4-pro` | ### 6️⃣ Data Analysis Currently supported analysis features: - Outlier handling (trimming and winsorizing) - Decile grouping analysis - K-Means clustering - Decision tree modeling - And more... ### 7️⃣ Report Generation Supports exporting: - Formatted Excel spreadsheets - Reports in `.docx` format - Built-in styled PowerPoint presentations ### 8️⃣ MCP Extensions Supports connecting to local or remote MCP servers to expand the Agent's capabilities. ![MCP](./Information/assets/MCP1.png) - Tutorial: [MCP_tutorial](./Information/MCP_tutorial.md) ### 9️⃣ Knowledge Base Input Supports uploading business knowledge to help the Agent better understand your data. ![repository](./Information/assets/repository2.png) - Tutorial: [repository_tutorial](./Information/repository_tutorial.md) --- <a id="install"></a> ## ⚙️ Installation ### 🖥️ Option 1: Windows Installer (Easiest — Recommended) Download the latest version from [GitHub Releases](https://github.com/Zafer-Liu/Data-Analysis-Agent/releases/latest): | Platform | File | |---|---| | Windows | Latest Windows `.exe` installer | > **Prerequisites: Python 3.10+ and 64-bit Windows 10 / 11.** Run the installer and follow the setup wizard. After installation, launch **Business Analytics Agent** from the desktop or Start Menu. Desktop packages built from the current source open in local mode without a platform account. Configure your own model API key in Settings. Existing published installers are unchanged until a new release is built; v1.5.0 still depends on the retired cloud login service. --- ### Option 2: Download the ZIP (Recommended for Beginners, Cross-Platform) > **Prerequisite: Python 3.10+** > Don't have it? [Download here](https://www.python.org/downloads/) (Windows: check **"Add Python to PATH"** during install) **Step 1: Download and extract** ![Download installation package](Information/assets/package.png) **Step 2: Double-click to launch** | OS | Action | |---|---| | **Windows** | Double-click `start.bat` | | **macOS** | ① Open Terminal (Command + Space → type Terminal → Enter) ② Run the following (replace the path with your actual extraction location): `chmod +x ~/Downloads/Data-Analysis-Agent/start.command` then `xattr -d com.apple.quarantine ~/Downloads/Data-Analysis-Agent/start.command` ③ Double-click `start.command` | > **First launch** will automatically create a virtual environment and install dependencies — this takes about 3–5 minutes. **Subsequent launches are instant.** **Step 3: Browser auto-opens** at `http://localhost:5001` ![Download installation package2](./Information/assets/package2.png) **Step 4: Configure your API Key** ![Configure the API3](./Information/assets/Deepseek3.png) **Step 5: Future updates** ![Update](./Information/assets/Update.png) --- ### Option 3: One-Click Online Install **Windows (run in PowerShell):** ```powershell iwr -useb https://raw.githubusercontent.com/Zafer-Liu/Data-Analysis-Agent/main/install.ps1 | iex ``` After installation, double-click `data-analysis-agent.bat` on your desktop, or run: ```powershell cd $env:USERPROFILE\.data-analysis-agent\Data-Analysis-Agent .\.venv\Scripts\activate python app.py ``` **macOS / Linux (run in Terminal):** ```bash curl -fsSL https://raw.githubusercontent.com/Zafer-Liu/Data-Analysis-Agent/main/install.sh | sh ``` After installation, run: ```bash data-analysis-agent ``` If you see `command not found`, add the following to `~/.zshrc` or `~/.bashrc`, then restart Terminal: ```bash export PATH="$HOME/.local/bin:$PATH" ``` --- ### Option 4: Clone from GitHub ```bash git clone https://github.com/Zafer-Liu/Data-Analysis-Agent.git cd Data-Analysis-Agent pip install -r requirements.txt python app.py ``` Open `http://localhost:5001` in your browser and configure your API Key. --- <a id="commands"></a> ## 🛠 Slash Commands | Command | Status | Description | |---|---|---| | `/chart` | ✅ | Force chart generation as the priority output | | `/sql` | ✅ | Execute SQL directly | | `/analyze` | ✅ | Deep statistical analysis | | `/tree` | ✅ | Decision tree analysis | | `/kmeans` | ✅ | K-Means clustering analysis | | `/data` | ✅ | Data exploration and preview | | `/inset` | ✅ | Missing value imputation | | `/winsorize` | ✅ | Winsorizing (replace extreme values) | | `/trimming` | ✅ | Trimming (remove extreme values) | | `/export` | ✅ | Export data file | | `/report` | ✅ | Export Word/PDF report | | `/ppt` | ✅ | Export PowerPoint presentation | | `/status` | ✅ | View task status | --- <a id="examples"></a> ## 📈 Usage Examples ### Example 1: Trend Analysis User input: ```text Sales trend for the last 12 months ``` System output: - SQL query - Trend line chart - Sales growth analysis --- ### Example 2: Regional Analysis User input: ```text Which region has the highest profit? ``` System output: - Regional profit rankings - Bar chart - Regional business insights --- ### Example 3: Chart-First Mode User input: ```text /chart User growth overview ``` The system will prioritize generating a visualization. --- <a id="llm-config"></a> ## 🤖 LLM Configuration In the sidebar ⚙, fill in: ```text API Key Base URL Model ``` to switch between models. --- <a id="agent-manager"></a> ## 🧩 Recommended Management: Agent Manager Business Analytics Agent can run independently or with **Agent Manager** for a more convenient desktop management experience. <details> <summary>Learn how Agent Manager manages this project</summary> **Agent Manager** is a unified control center for local AI Agent projects. After adding Business Analytics Agent, you can: * Start or stop the Agent with one click * View logs and port status in real time * Open the web analysis interface inside the desktop app * Ask the Manager Agent to start or open this project in natural language * Create a temporary public sharing link for demos ```text Example: Start Business Analytics Agent and open its interface. ``` This is especially useful when you frequently demonstrate, debug, or run multiple Agent projects. 👉 Project: [Agent Manager](https://github.com/Zafer-Liu/Agent_Manager) </details> --- <a id="changelog"></a> ## 📝 Changelog > **Current source version `v1.4.0`** · October 16, 2026 · Check [Releases](https://github.com/Zafer-Liu/Data-Analysis-Agent/releases/latest) for published installers. The public source now includes the existing private-edition features: the read-only administration console, desktop cloud authentication and model gateways, referral and quota ledgers, browser/table ingestion, browser-local persistence, PyTorch GRU/LSTM, chart themes and PNG export, in-memory SQL transformations and numeric provenance checks, canvas save recovery, Skill tags, and SSE reliability improvements. Direct `python app.py` launches and default desktop packages require no cloud login and use your own model configuration. Hosted services retain account authentication; desktop cloud authentication is enabled only by an explicitly configured `BAA_CLOUD_AUTH_URL`. See the [sync inventory](./docs/feature-sync-1.4.md) and [hosted configuration](./docs/hosted-configuration.md). ### Local models and services (source update) Manage HTTP and Docker connections in **Settings → Local services**. Custom HTTP connections support health checks and manual JSON requests; OpenAI-compatible services can discover models and add them to the LLM list. TimesFM is a forecasting adapter with `run_analysis`, backtesting, result tables, and chart export. See the [local services guide](./deploy/local-services/README.md) for configuration and capability boundaries. No models, model dependencies, or container images are downloaded automatically. For existing model directories, explicit downloads, Docker templates, and a three-product example, see the [TimesFM guide](./deploy/timesfm/README.md). The main application environment stays lightweight; TimesFM weights are limited to non-commercial, non-production research. ### v1.3.0 history v1.3.0 enters the Feishu AI Skills Hackathon, adds a Feishu bot and built-in Bitable analysis, and delivers targeted feature improvements: 1. **Feishu bot and Bitable loop**: Mention the bot in a group to read a Feishu Bitable, load records into DuckDB / SQL analysis, and reply in the same group. Creating result tables or changing records always requires an explicit request. 2. **More dependable long-term memory**: Fixes extraction failures caused by thinking-model output, JSON formatting, and scope detection; adds result notices, 24-hour consolidation, safer session restore, and a global memory switch. 3. **Trustworthy knowledge-retrieval status**: Repairs local Embedding model download/compatibility and makes cloud retrieval status an explicit real check. The former Hash mode is now labelled **Basic** in the UI. 4. **Clearer settings and reading experience**: Unifies settings headers, cards, and spacing; fixes LLM provider-row inset, hover, spacing, and long-content clipping; wide chat layouts now use available space more effectively. 5. **Compatibility and delivery reliability**: Clarifies SQL Server connection/error boundaries and workflow observability. GPU compute is labelled **Planned** until end-to-end validation is complete; existing implementation and configuration remain preserved. ### 📖 Detailed Changelog - [Version Update Log (中文)](./Information/Version_Update_Log.md) - [Version Update Log (English)](./Information/Version_Update_Log_EN.md) --- <a id="faq"></a> ## ❓ FAQ <details> <summary>📦 Installation & Startup</summary> <details> <summary>Network timeout while installing dependencies?</summary> The script will automatically switch to the Tsinghua mirror and retry. If it still fails, run manually: ```bash pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` </details> <details> <summary>pip install error / dependency installation failed?</summary> The script automatically retries using a domestic mirror (Tsinghua). If it still fails, specify the mirror manually: ```bash pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` Also ensure at least **2 GB** of free disk space is available. </details> <details> <summary>Wrong Python version (requires 3.10+)?</summary> Check your current version: ```bash python --version ``` If below 3.10, download the latest version at: https://www.python.org/downloads/ </details> <details> <summary>Double-clicking start.bat does nothing or flashes briefly?</summary> Python is not correctly added to the system PATH. Reinstall Python and check **"Add Python to PATH"**, then restart your computer and try again. </details> <details> <summary>macOS blocks start.command from running?</summary> Run this in Terminal to remove the restriction: ```bash xattr -d com.apple.quarantine /your/path/to/start.command ``` If you see: > "Cannot be opened because the developer cannot be verified" You can: 1. Right-click `start.command` 2. Select "Open" 3. Click "Open" again </details> </details> --- <details> <summary>🔑 API Configuration</summary> <details> <summary>Prompted that LLM is not configured?</summary> Enter your API Key in the sidebar ⚙ and save. </details> <details> <summary>How do I get an API Key?</summary> Using DeepSeek as an example: ![Configure the API1](./Information/assets/Deepseek1.png) ![Configure the API2](./Information/assets/Deepseek2.png) ![Configure the API3](./Information/assets/Deepseek3.png) </details> </details> --- <details> <summary>🗄️ Database Connection</summary> <details> <summary>How do I connect to a SQL database?</summary> Use the following connection format: ```text mysql+pymysql://username:password@host:port/database_name ``` Example: ❌ Wrong: ```text mysql://user:pass@host:3306/dbname ``` ✅ Correct: ```text mysql+pymysql://user:pass@host:3306/dbname ``` </details> </details> --- <details> <summary>📊 Charts & Files</summary> <details> <summary>Chart links broken after restart?</summary> Generated charts are saved locally at: ```text outputs/charts ``` You can open the corresponding HTML files directly in your browser. </details> </details> --- <a id="contributing"></a> ## 🤝 Contribution Guide We welcome every kind of contribution, whether you are fixing a bug, improving documentation, or adding a feature. ### Contribution Workflow 1. **Fork this repository** 2. **Create a feature branch** ```bash git checkout -b feature/your-feature-name ``` 3. **Commit your changes** ```bash git add . git commit -m "feat: add your feature" ``` We recommend following the [Conventional Commits](https://www.conventionalcommits.org/) specification. 4. **Push your branch** ```bash git push origin feature/your-feature-name ``` 5. **Open a Pull Request** - Describe your changes - Include screenshots or a recording for new UI features - Link related issues, if any ### Code Standards - Follow [PEP 8](https://pep8.org/) for Python code - Run `flake8` before submitting changes - Add relevant tests for new features - Keep commit messages clear and concise ### Reporting Bugs Report bugs through [GitHub Issues](https://github.com/Zafer-Liu/Data-Analysis-Agent/issues) and include: - 📋 Steps to reproduce - 🎯 Expected behavior - ⚠️ Actual behavior - 📸 Screenshots, when applicable - 💻 Environment details, including OS and Python version --- ### 🚀 Looking for Contributors A great open-source project is never a solo act. We're building a **data tool that can genuinely handle complex business scenarios** — one that races through massive datasets, navigates multi-table logic with ease, and surfaces insights on visual dashboards. We've hit a few deeply challenging, high-value problems. If you love solving hard technical problems, we need you: --- ### Key challenges we'd love your help with: - **Multi-sheet inter-table logic optimization** — How do you intelligently map dependencies and calculations across dozens of sheets? - **Visualization dashboard interactivity & performance** — Making data stories flow more smoothly, intuitively, and powerfully. - **Model capability enhancement for specialized business scenarios** — The edge cases that general-purpose tools can't handle. - **Remote server invocation** — Building a framework for remote GPU calls. --- ### Why is it worth joining? - You'll tackle **real, deep, non-toy technical challenges** - Your code will directly impact the productivity of **front-line business users** - Contribute freely, collaborate flexibly — submit a PR or reach out directly, entirely up to you - Outstanding contributors may be invited to become project Committers --- ### How to join? - Open a **Pull Request** directly — we review within 24 hours - Or contact us at: `rusboldtshanti34@gmail.com` (please note "Contributor + area of expertise") - Join the official community: QQ Group `991636855` or [Telegram Group](https://t.me/+cdRNfS68u9BlYjJl) or [Discord](https://discord.gg/EEG4Sw7tde) --- <a id="license"></a> ## 📄 License This software is licensed under **CC BY-NC 4.0** and a Chinese software copyright registration has been applied for (Acceptance No. 2026R11S0817877, Software: Natural-Language Interactive Business Data Analysis Software V1.0). See the [LICENSE](./LICENSE) file for the full terms. > Unauthorized commercial use is prohibited; contact the author for a separate commercial license. --- <a id="goal"></a> ## ⭐ Project Goal Leave the process to Business Analytics Agent, and save your time for thinking. --- <div align="center"> ⭐️ If this project helps you, please give it a star! Made with ❤️ by [Zafer-Liu](https://github.com/Zafer-Liu) </div>

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skill

职业分类

描述

更新

ab-test-analysis

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分析已有 A/B 实验数据的分组质量、转化或连续指标提升、统计显著性与置信区间，并输出审慎上线建议。用于用户要求 A/B 测试、实验效果、显著性检验、转化率对比、对照组与实验组评估时。

2026-10-24

arima

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使用 ARIMA 进行单变量时间序列预测（forecast）

原文语言：多语言混合

2026-10-24

bcg-matrix

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波士顿矩阵（BCG Matrix）分析，按市场增长率和相对市场份额将业务单元分为明星、问题、现金牛、瘦狗四类（portfolio 产品组合）

2026-10-24

未分类

使用 SQL 安全查询当前已选择的数据表（query 数据查询）

原文语言：多语言混合

2026-10-24

swot-analysis

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SWOT 分析，从内部优势/劣势和外部机会/威胁四个维度评估企业或产品的战略态势（strategy 战略分析）

2026-10-24

未分类

Ad-Lib 价值主张速写，用一句填空句式把价值主张拆成六个可验证的组成部分（value prop 价值主张）

2026-10-24

var

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使用 VAR 分析多个时间序列的动态关系（forecast 向量自回归）

原文语言：多语言混合

2026-10-24

web-table

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把网页、JSON、CSV、PDF 链接里的表格抓取注册为可查询数据源（数据获取入口）

2026-10-24

winsorize

未分类

对极端值执行缩尾处理并验证影响（outlier 缩尾）

原文语言：多语言混合

2026-10-24

已展示 30 / 30 个已收集 Skill。