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docs: add GitHub Copilot SDK pipe masterclass cases and documentation updates
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‎docs/plugins/pipes/index.md‎

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## Available Pipe Plugins
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- [GitHub Copilot SDK](github-copilot-sdk.md) (v0.6.2) - Official GitHub Copilot SDK integration. Features **Workspace Isolation**, **Database Persistence**, **Zero-config OpenWebUI Tool Bridge**, **BYOK** support, and **dynamic MCP discovery**. Supports streaming, multimodal, and infinite sessions. [View Deep Dive](github-copilot-sdk-deep-dive.md) | [**View Advanced Tutorial**](github-copilot-sdk-tutorial.md).
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- **[Case Study: GitHub 100 Star Growth Analysis](star-prediction-example.md)** - Learn how to use the GitHub Copilot SDK Pipe with Minimax 2.1 to automatically analyze CSV data and generate project growth reports.
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- **[Case Study: High-Quality Video to GIF Conversion](video-processing-example.md)** - See how the model uses system-level FFmpeg to accelerate, scale, and optimize colors for screen recordings.
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‎docs/plugins/pipes/index.zh.md‎

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## 可用的 Pipe 插件
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- [GitHub Copilot SDK](github-copilot-sdk.zh.md) (v0.6.2) - GitHub Copilot SDK 官方集成。具备**工作区安全隔离**、**数据库持久化**、**零配置工具桥接**与**BYOK (自带 Key) 支持**。支持流式输出、打字机思考过程及无限会话。[查看深度架构解析](github-copilot-sdk-deep-dive.zh.md) | [**查看进阶实战教程**](github-copilot-sdk-tutorial.zh.md)。
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- **[实战案例：GitHub 100 Star 增长预测](star-prediction-example.zh.md)** - 展示如何使用 GitHub Copilot SDK Pipe 结合 Minimax 2.1 模型，自动编写脚本分析 CSV 数据并生成详细的项目增长报告。
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- **[实战案例：视频高质量 GIF 转换与加速](video-processing-example.zh.md)** - 演示模型如何通过底层 FFmpeg 工具对录屏进行加速、缩放及双阶段色彩优化处理。
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‎docs/plugins/pipes/star-prediction-chat.json‎

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‎docs/plugins/pipes/star-prediction-demo.gif‎

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‎docs/plugins/pipes/star-prediction-example.md‎

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# Case Study: GitHub 100 Star Growth Analysis
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This case study demonstrates how to use the **GitHub Copilot SDK Pipe** with the **Minimax 2.1** model to perform a deep analysis of project growth data and predict the milestone of reaching 100 stars.
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---
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## 🎥 Recording
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![Analysis Process Demo](./star-prediction-demo.gif)
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> **Scenario**: The user uploaded project traffic CSVs and star history data for the last 14 days. The model automatically wrote a Python script for regression analysis, generated an interactive visualization, and provided a detailed growth projection report.
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---
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## 🛠️ Implementation
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- **Plugin Type**: Pipe (GitHub Copilot SDK)
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- **Base Model**: Minimax 2.1 (via Pipe integration)
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- **Key Capabilities**:
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- **File Processing**: Automatically reads and parses multiple CSV data files.
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- **Code Generation & Execution**: On-the-fly Python scripting to calculate growth rates, conversion rates, and median trends.
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- **Multimodal Output**: Generates Markdown reports, interactive HTML dashboards, and Mermaid timeline charts.
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---
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## 💬 Conversation Highlights
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### 📥 Import Conversation
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You can download the raw chat data and import it into your Open WebUI to see the full tool calls and analysis logic:
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[:material-download: Download Chat JSON](./star-prediction-chat.json)
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> **How to Import?**
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> In Open WebUI, click your **User Avatar** (bottom of left sidebar) -> **Settings** -> **Data** -> **Import Chats**, then select the downloaded file.
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### 1. Data Submission
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The **User** provided traffic source distribution and uploaded:
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- `Unique visitors in last 14 days.csv`
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- `Total views in last 14 days.csv`
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- `star-history.csv`
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### 2. Analysis Execution
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**Minimax 2.1** received the data and immediately formulated an analysis plan:
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1. Calculate star growth trajectory and rates.
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2. Analyze visitor-to-star conversion rates.
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3. Build linear and median projection models.
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4. Generate a milestone timeline.
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### 3. Report Generation
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The model produced a comprehensive report. Here are the core projections:
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#### 🎯 Key Projections
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| Metric | Value | Insight |
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| :--- | :--- | :--- |
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| **Current Stars** | 62 | 62% of the goal reached |
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| **Projected Goal Date** | **March 10, 2026** | ~28 days remaining |
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| **Avg Growth Rate** | 1.35 stars/day | Highly stable and predictable |
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| **Recent Acceleration** | 1.92 stars/day | Growth accelerated by 42% in last 14 days |
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| **Conversion Rate** | 4.19% | Significantly higher than the OS average of 1-2% |
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---
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## 📈 Growth Milestone Timeline
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```mermaid
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gantt
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title 🎯 Path to 100 Stars Timeline
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dateFormat YYYY-MM-DD
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axisFormat %m/%d
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section Completed
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Launch (0 Stars) :done, 2025-12-26, 1d
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Hit 50 Stars :done, 2026-02-01, 1d
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section Projected
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75 Stars (Proj) :active, 2026-02-19, 5d
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80 Stars (Proj) : 2026-02-23, 4d
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90 Stars (Proj) : 2026-03-02, 4d
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🎯 100 Stars Reach :crit, 2026-03-10, 4d
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```
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---
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## 💡 Strategic Recommendations
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1. **Strengthen OpenWebUI Partnership**: 41% of traffic comes from `openwebui.com`. Maintain high visibility on this channel.
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2. **SEO Optimization**: Organic search accounts for only 6%. There is massive potential for keyword optimization in the README.
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3. **Milestone Momentum**: Plan a social media push around Feb 23 (at the 80-star mark) to leverage the 42% acceleration trend.
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---
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> [View GitHub Copilot SDK Pipe Source Code](../../../plugins/pipes/github-copilot-sdk/README.md)

‎docs/plugins/pipes/star-prediction-example.zh.md‎

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# 案例展示：GitHub 100 Star 增长预测分析
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本案例展示了如何使用 **GitHub Copilot SDK Pipe** 配合 **Minimax 2.1** 模型，对项目的增长数据进行深度分析并预测达到 100 Star 的里程碑时间。
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---
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## 🎥 效果录屏
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![分析过程演示](./star-prediction-demo.gif)
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> **场景描述**：用户上传了过去 14 天的项目访问流量 CSV 和 Star 历史数据。模型自动编写 Python 脚本进行回归分析，生成了可视化看板和详细的增长预测报告。
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---
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## 🛠️ 技术实现
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- **插件类型**: Pipe (GitHub Copilot SDK)
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- **底层模型**: Minimax 2.1 (通过 Pipe 接入)
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- **核心能力**:
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- **文件处理**: 自动读取并解析多份 CSV 数据文件。
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- **代码生成与执行**: 现场编写 Python 分析代码并执行，计算增长率、转化率及中位趋势。
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- **多模态输出**: 生成 Markdown 报告、HTML 交互看板以及 Mermaid 时间轴图表。
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---
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## 💬 对话实录
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### 📥 导入对话记录
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你可以下载原始对话数据并导入到你的 Open WebUI 中，查看完整的工具调用和分析逻辑：
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[:material-download: 下载原始对话 JSON](./star-prediction-chat.json)
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> **如何导入？**
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> 在 Open WebUI 首页点击 **左侧侧边栏底部个人头像** -> **设置** -> **数据** -> **导入记录**，选择下载的文件即可。
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### 1. 提交原始数据
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**用户**提供了项目的流量来源分布表，并上传了：
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- `Unique visitors in last 14 days.csv`
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- `Total views in last 14 days.csv`
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- `star-history.csv`
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### 2. 模型执行分析
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**Minimax 2.1** 接收到数据后，立即制定了分析计划：
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1. 计算 Star 增长轨迹和增长率。
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2. 分析访问者到 Star 的转化率。
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3. 构建线性与中位增长模型进行预测。
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4. 生成里程碑时间轴。
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### 3. 生成分析报告
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模型输出了一份详尽的报告，以下是其核心预测：
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#### 🎯 关键预测结果
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| 指标 | 数值 | 洞察 |
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| :--- | :--- | :--- |
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| **当前 Star 数** | 62 | 已完成目标的 62% |
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| **预测达成日期** | **2026年3月10日** | 预计还有 28 天 |
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| **平均增长率** | 1.35 stars/day | 极其稳定且可预测 |
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| **近期加速** | 1.92 stars/day | 过去 14 天增长加速了 42% |
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| **Star 转化率** | 4.19% | 远高于开源项目 1-2% 的平均水平 |
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---
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## 📈 增长里程碑预演
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```mermaid
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gantt
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title 🎯 冲刺 100 Stars 路径图
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dateFormat YYYY-MM-DD
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axisFormat %m/%d
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section 已完成
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项目启动 (0 Star) :done, 2025-12-26, 1d
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突破 50 Star :done, 2026-02-01, 1d
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section 预测中
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75 Stars (预测) :active, 2026-02-19, 5d
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80 Stars (预测) : 2026-02-23, 4d
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90 Stars (预测) : 2026-03-02, 4d
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🎯 100 Stars 达成 :crit, 2026-03-10, 4d
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```
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## 💡 模型给出的建议
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1. **强化 OpenWebUI 社区合作**：41% 的流量来自 `openwebui.com`，应持续在该渠道保持活跃。
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2. **SEO 优化**：目前搜索流入仅占 6%，Readme 的关键词优化空间巨大。
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3. **关键节点冲刺**：建议在 2 月 23 日（80 Star 节点）发起社交媒体宣传，利用近期 42% 的加速趋势冲刺百星。
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> [查看 GitHub Copilot SDK Pipe 源码](../../../plugins/pipes/github-copilot-sdk/README.md)

‎docs/plugins/pipes/video-processing-chat.json‎

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‎docs/plugins/pipes/video-processing-demo.gif‎

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‎docs/plugins/pipes/video-processing-example.md‎

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# Case Study: High-Quality Video to GIF Conversion
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This case study demonstrates how to use the **GitHub Copilot SDK Pipe** with **Minimax 2.1** to perform professional-grade video processing: accelerating a video by 1.4x and converting it into a high-quality GIF under 20MB.
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---
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## 🎥 Recording
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![Video Processing Demo](./video-processing-demo.gif)
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> **Scenario**: The user uploaded a 38MB `.mov` recording and requested a 1.4x speed increase, 1280px width, and a file size limit of 20MB. The model automatically formulated, executed, and verified a two-pass FFmpeg workflow.
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---
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## 🛠️ Implementation
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- **Plugin Type**: Pipe (GitHub Copilot SDK)
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- **Base Model**: Minimax 2.1
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- **Key Capabilities**:
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- **System Tool Access**: Automatically detects and invokes `ffmpeg` within the container.
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- **Two-Pass Optimization**:
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1. **Pass 1**: Analyzes all frames to generate a custom 256-color palette (`palettegen`).
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2. **Pass 2**: Applies the palette for superior quantization and dithering (`paletteuse`).
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- **Precision Parameters**: Implements 1.4x PTS scaling, Lanczos scaling, and 20fps rate control.
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---
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## 💬 Conversation Highlights
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### 📥 Import Conversation
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You can download the raw chat data and import it into your Open WebUI to see how the model debugs and optimizes the FFmpeg parameters:
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[:material-download: Download Chat JSON](./video-processing-chat.json)
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> **How to Import?**
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> In Open WebUI, click your **User Avatar** (bottom of left sidebar) -> **Settings** -> **Data** -> **Import Chats**, then select the downloaded file.
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### 1. Processing Requirements
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The **User** provided an input file and specific parameters:
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- Speed: 1.4x (setpts=PTS/1.4)
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- Resolution: 1280px width, auto height
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- Optimization: Must use palette generation technology
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- Constraint: File size < 20MB
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### 2. Analysis Execution
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**Minimax 2.1** generated and executed the following core logic:
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```bash
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# Pass 1: Generate optimal palette
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ffmpeg -i input.mov -vf "fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4,palettegen" palette.png
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# Pass 2: Generate final high-quality GIF
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ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4[v];[v][1:v]paletteuse" output.gif
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```
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### 3. Result Summary
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| Metric | Original Video | Processed GIF | Status |
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| :--- | :--- | :--- | :--- |
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| **File Size** | 38 MB | **14 MB** | ✅ Success |
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| **Resolution** | 3024x1898 | 1280x803 | ✅ Smooth |
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| **Speed** | 1.0x | 1.4x | ✅ Accurate |
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| **Color Quality** | N/A | Optimal 256-color | ✨ Crystal Clear |
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---
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## 💡 Why This Case Matters
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Standard LLMs can only "tell you" how to use FFmpeg. However, a Pipe powered by the **GitHub Copilot SDK** can:
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1. **Interpret** complex multimedia processing parameters.
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2. **Access** raw files within the filesystem.
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3. **Execute** resource-intensive binary tool tasks.
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4. **Validate** that the output (size, resolution) meets the user's hard constraints.
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---
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> [View GitHub Copilot SDK Pipe Source Code](../../../plugins/pipes/github-copilot-sdk/README.md)

‎docs/plugins/pipes/video-processing-example.zh.md‎

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# 案例展示：视频高质量 GIF 转换与加速处理
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本案例展示了如何使用 **GitHub Copilot SDK Pipe** 配合 **Minimax 2.1** 模型，通过底层工具（FFmpeg）对视频文件进行精细化处理：加速 1.4 倍并压缩至 20MB 以内的超高质量 GIF。
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---
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## 🎥 效果录屏
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![视频处理过程演示](./video-processing-demo.gif)
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> **场景描述**：用户上传了一个 38MB 的 `.mov` 录屏文件，要求加速 1.4 倍，并转换为 1280px 宽度、大小在 20MB 以内的高质量 GIF。模型自动分析了需求，编写并执行了双工作流 FFmpeg 指令。
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---
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## 🛠️ 技术实现
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- **插件类型**: Pipe (GitHub Copilot SDK)
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- **底层模型**: Minimax 2.1
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- **核心能力**:
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- **底层系统访问**: 自动检测并调用容器内的 `ffmpeg` 工具。
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- **双阶段优化 (Two-Pass Optimization)**:
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1. **阶段一**: 分析全视频帧生成 256 色最优调色板 (`palettegen`)。
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2. **阶段二**: 应用调色板进行高质量量化和抖动处理 (`paletteuse`)。
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- **参数精准控制**: 实现 1.4 倍 PTS 缩放、Lanczos 滤镜缩放及 20fps 帧率控制。
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---
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## 💬 对话实录
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### 📥 导入对话记录
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你可以下载原始对话数据并导入到你的 Open WebUI 中，查看模型如何一步步调试 FFmpeg 参数：
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[:material-download: 下载原始对话 JSON](./video-processing-chat.json)
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> **如何导入？**
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> 在 Open WebUI 首页点击 **左侧侧边栏底部个人头像** -> **设置** -> **数据** -> **导入记录**，选择下载的文件即可。
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### 1. 提交处理需求
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**用户**指定了输入文件和详细参数：
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- 加速：1.4x (setpts=PTS/1.4)
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- 分辨率：宽度 1280px，等比例缩放
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- 质量优化：必须使用调色板生成技术
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- 约束：文件体积 < 20MB
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### 2. 模型执行处理
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**Minimax 2.1** 自动编写并执行了以下核心逻辑：
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```bash
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# 生成优化调色板
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ffmpeg -i input.mov -vf "fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4,palettegen" palette.png
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# 生成最终高质量 GIF
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ffmpeg -i input.mov -i palette.png -lavfi "[0:v]fps=20,scale=1280:-1:flags=lanczos,setpts=PTS/1.4[v];[v][1:v]paletteuse" output.gif
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```
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### 3. 处理结果摘要
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| 指标 | 原始视频 | 处理后 GIF | 状态 |
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| :--- | :--- | :--- | :--- |
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| **文件大小** | 38 MB | **14 MB** | ✅ 达标 |
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| **分辨率** | 3024x1898 | 1280x803 | ✅ 缩放平滑 |
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| **播放速度** | 1.0x | 1.4x | ✅ 节奏紧凑 |
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| **色彩质量** | N/A | 256色最优量化 | ✨ 极其清晰 |
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---
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## 💡 为什么这个案例很有意义？
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传统的 LLM 只能“告诉你”怎么做，而基于 **GitHub Copilot SDK** 的 Pipe 能够：
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1. **理解** 复杂的多媒体处理参数。
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2. **感知** 文件系统中的原始素材。
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3. **执行** 耗时、耗能的二进制工具任务。
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4. **验证** 产出物（体积、分辨率）是否符合用户的最终约束。
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---
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> [查看 GitHub Copilot SDK Pipe 源码](../../../plugins/pipes/github-copilot-sdk/README.md)

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