docs: add GitHub Copilot SDK pipe masterclass cases and documentation updates

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

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