Testing Test-Pull-Request

assets/contributors.csv

@@ -97,7 +97,7 @@ Waheed Brown,Arm,https://github.com/armwaheed,https://www.linkedin.com/in/waheed
 Aryan Bhusari,Arm,,https://www.linkedin.com/in/aryanbhusari,,
 Ken Zhang,Insyde,,kai-di-zhang-b1642a266,,
 Ann Cheng,Arm,anncheng-arm,hello-ann,,
-Fidel Makatia Omusilibwa,,,,,
+Fidel Makatia Omusilibwa,,fidel-makatia,fidel-makatia-hsc-mieee,,
 Ker Liu,,,,,
 Rui Chang,,,,,
 Alejandro Martinez Vicente,Arm,,,,
@@ -113,5 +113,6 @@ Steve Suzuki,Arm,,,,
 Qixiang Xu,Arm,,,,
 Phalani Paladugu,Arm,phalani-paladugu,phalani-paladugu,,
 Richard Burton,Arm,Burton2000,,,
+Brendan Long,Arm,bccbrendan,https://www.linkedin.com/in/brendan-long-5817924/,,
 Asier Arranz,NVIDIA,,asierarranz,,asierarranz.com
-Prince Agyeman,Arm,,,,
+Prince Agyeman,Arm,,,,

content/install-guides/codex-cli.md

@@ -233,6 +233,18 @@ The Arm MCP server is listed in the output. If the arm-mcp server indicates it's
 
 You can also verify the tools are available by asking Codex to list the available Arm MCP tools.
 
+### Use Arm prompt files with the MCP Server
+
+The Arm MCP Server provides a rich set of tools and knowledge base, but to make the best use of it, you should pair it with Arm-specific prompt files. These prompt files supply task-oriented context, best practices, and structured workflows that guide the agent in using MCP tools more effectively across common Arm development tasks.
+
+#### Get the prompt files
+
+Browse the [agent integrations directory for Codex](https://github.com/arm/mcp/tree/main/agent-integrations/codex) to find prompt files for specific use cases:
+
+- **Arm migration** ([arm-migration.md](https://github.com/arm/mcp/blob/main/agent-integrations/codex/arm-migration.md)): Helps the agent systematically migrate applications from x86 to Arm, including dependency analysis, compatibility checks, and optimization recommendations.
+
+Each prompt file is a Markdown configuration that you can reference in your Codex CLI sessions to enable more targeted, task-specific assistance.
+
 If you're facing issues or have questions, reach out to mcpserver@arm.com.
 
-You're now ready to use Codex CLI with Arm-specific development assistance.
+You're now ready to use Codex CLI with the Arm MCP server for Arm-specific development assistance.

content/install-guides/gemini.md

@@ -407,6 +407,18 @@ Configured MCP servers:
   - sysreport_instructions
 ```
 
+### Use Arm prompt files with the MCP Server
+
+The Arm MCP Server provides a rich set of tools and knowledge base, but to make the best use of it, you should pair it with Arm-specific prompt files. These prompt files supply task-oriented context, best practices, and structured workflows that guide the agent in using MCP tools more effectively across common Arm development tasks.
+
+#### Get the prompt files
+
+Browse the [agent integrations directory](https://github.com/arm/mcp/tree/main/agent-integrations/gemini) to find prompt files for specific use cases:
+
+- **Arm migration** ([arm-migration.toml](https://github.com/arm/mcp/blob/main/agent-integrations/gemini/arm-migration.toml)): Helps the agent systematically migrate applications from x86 to Arm, including dependency analysis, compatibility checks, and optimization recommendations.
+
+Each prompt file is a TOML configuration that you can reference in your Gemini CLI sessions to enable more targeted, task-specific assistance.
+
 If you're facing issues or have questions, reach out to mcpserver@arm.com.
 
-You're now ready to use Gemini CLI with the Arm MCP server for Arm-specific development assistance.
+You're now ready to use Gemini CLI with the Arm MCP server for Arm-specific development assistance.

content/install-guides/github-copilot.md

@@ -335,6 +335,20 @@ Example prompts that use the Arm MCP Server:
 - `Search the Arm knowledge base for Neon intrinsics examples`
 - `Find learning resources about migrating from x86 to Arm`
 
+## Use Arm prompt files with the MCP Server
+
+The Arm MCP Server provides a rich set of tools and knowledge base, but to make the best use of it, you should pair it with Arm-specific prompt files. These prompt files supply task-oriented context, best practices, and structured workflows that guide the agent in using MCP tools more effectively across common Arm development tasks.
+
+### Get the prompt files
+
+Browse the [agent integrations directory for Visual Studio Code](https://github.com/arm/mcp/tree/main/agent-integrations/vs-code) to find prompt files for specific use cases:
+
+- **Arm migration** ([arm-migration.prompt.md](https://github.com/arm/mcp/blob/main/agent-integrations/vs-code/arm-migration.prompt.md)): Helps the agent systematically migrate applications from x86 to Arm, including dependency analysis, compatibility checks, and optimization recommendations.
+
+Each prompt file is a Markdown configuration that you can reference in your GitHub Copilot sessions to enable more targeted, task-specific assistance.
+
+If you're facing issues or have questions, reach out to mcpserver@arm.com.
+
 ## Troubleshooting MCP Server connections
 
 This section helps you resolve common issues when installing and using GitHub Copilot with the Arm MCP Server on Arm systems. If you encounter problems not covered here, contact [mcpserver@arm.com](mailto:mcpserver@arm.com) for support.
@@ -349,4 +363,4 @@ If the Arm MCP Server doesn't connect:
 
 
 
-You're now ready to use GitHub Copilot with the Arm MCP Server to enhance your Arm development workflow!
+You're now ready to use GitHub Copilot with the Arm MCP server for Arm-specific development assistance.

content/install-guides/kiro-cli.md

@@ -262,6 +262,18 @@ Use the `/tools` command to list the available tools:
 
 You should see the Arm MCP server tools listed in the output. If the arm-mcp server says it's still loading, wait a moment and run `/tools` again.
 
-If you are facing issues or have questions, reach out to mcpserver@arm.com.
+### Use Arm prompt files with the MCP Server
 
-You're ready to use Kiro CLI.
+The Arm MCP Server provides a rich set of tools and knowledge base, but to make the best use of it, you should pair it with Arm-specific prompt files. These prompt files supply task-oriented context, best practices, and structured workflows that guide the agent in using MCP tools more effectively across common Arm development tasks.
+
+#### Get the prompt files
+
+Browse the [agent integrations directory for Kiro](https://github.com/arm/mcp/tree/main/agent-integrations/kiro) to find prompt files for specific use cases:
+
+- **Arm migration** ([arm-migration.md](https://github.com/arm/mcp/blob/main/agent-integrations/kiro/arm-migration.md)): Helps the agent systematically migrate applications from x86 to Arm, including dependency analysis, compatibility checks, and optimization recommendations.
+
+Each prompt file is a Markdown configuration that you can reference in your Kiro CLI sessions to enable more targeted, task-specific assistance.
+
+If you're facing issues or have questions, reach out to mcpserver@arm.com.
+
+You're now ready to use Kiro CLI with the Arm MCP server for Arm-specific development assistance.

content/install-guides/multipass.md

@@ -24,7 +24,7 @@ ecosystem_dashboard: https://developer.arm.com/ecosystem-dashboard/linux?package
 
 test_images:
 - ubuntu:latest
-test_maintenance: true
+test_maintenance: false
 
 ### PAGE SETUP
 weight: 1                       # Defines page ordering. Must be 1 for first (or only) page.

content/install-guides/perf.md

@@ -22,7 +22,7 @@ ecosystem_dashboard: https://developer.arm.com/ecosystem-dashboard/linux?package
 
 test_images:
 - ubuntu:latest
-test_maintenance: true
+test_maintenance: false
 
 ### PAGE SETUP
 weight: 1                       # Defines page ordering. Must be 1 for first (or only) page.

content/install-guides/pytorch.md

@@ -14,7 +14,7 @@ ecosystem_dashboard: https://developer.arm.com/ecosystem-dashboard/linux?package
 test_images:
 - ubuntu:latest
 test_link: null
-test_maintenance: true
+test_maintenance: false
 title: PyTorch
 tool_install: true
 weight: 1

content/learning-paths/embedded-and-microcontrollers/alif-image-classification/_index.md

@@ -1,31 +1,31 @@
 ---
-title: Run image classification on an Alif Ensemble E8 DevKit with ExecuTorch and Ethos-U85
+title: Run image classification on an Alif Ensemble E8 DevKit using ExecuTorch and Ethos-U85
+
+description: Deploy a MobileNetV2 image classification model to an Alif Ensemble E8 DevKit and run inference on the Ethos-U85 NPU.
 
 draft: true
 cascade:
     draft: true
 
 minutes_to_complete: 120
 
-who_is_this_for: This Learning Path is for embedded developers who want to deploy a neural network on an Arm Cortex-M55 microcontroller with an Ethos-U85 NPU. You will compile a MobileNetV2 model using ExecuTorch, embed it into bare-metal firmware, and run image classification on the Alif Ensemble E8 DevKit.
+who_is_this_for: This is an advanced topic for embedded developers who want to deploy a neural network model to an Arm Cortex-M55 microcontroller using ExecuTorch and an Ethos-U85 NPU.
 
 learning_objectives:
     - Compile a MobileNetV2 model for the Ethos-U85 NPU using ExecuTorch's ahead-of-time (AOT) compiler on an Arm-based cloud instance.
     - Build ExecuTorch static libraries for bare-metal Cortex-M55 targets.
-    - Configure CMSIS project files, memory layout, and linker scripts for a large ML workload on the Alif Ensemble E8.
-    - Run real-time image classification inference on the Ethos-U85 NPU and verify results through SEGGER RTT.
+    - Configure CMSIS project files, memory layout, and linker scripts for an ML workload on the Alif Ensemble E8.
+    - Run real-time image classification inference on the Ethos-U85 NPU and verify results using SEGGER Real-Time Transfer (RTT).
 
 prerequisites:
-    - An Alif Ensemble E8 DevKit with a USB-C cable.
-    - A SEGGER J-Link debug probe (the DevKit has one built in).
-    - A development machine running macOS (Apple Silicon) or Linux.
-    - (Optional) An AWS account or access to an Arm-based cloud instance (Graviton c7g.4xlarge recommended). You can also build ExecuTorch locally on an Arm-based machine, though the steps will differ.
-    - Basic familiarity with C/C++ and embedded development concepts.
-    - VS Code installed on your development machine.
+    - Experience with C/C++ and embedded development concepts.
+    - An [Alif Ensemble E8 DevKit](https://alifsemi.com/support/kits/ensemble-e8devkit/) with a USB-C cable.
+    - A SEGGER J-Link debug probe (included in the DevKit).
+    - A development machine running macOS on Apple Silicon with Visual Studio Code installed.
+    - An AWS account or access to an Arm-based cloud instance for native Arm compilation.
 
 author: Gabriel Peterson
 
-### Tags
 skilllevels: Advanced
 subjects: ML
 armips:

content/learning-paths/embedded-and-microcontrollers/alif-image-classification/application-code.md

@@ -5,7 +5,7 @@ weight: 5
 layout: "learningpathall"
 ---
 
-## Overview
+## What the application code does
 
 The application code initializes the Ethos-U85 NPU, loads the MobileNetV2 model through ExecuTorch, runs inference on an embedded test image, and prints the classification result over SEGGER RTT.
 
@@ -16,7 +16,7 @@ Rather than building this code line by line, you download the complete `main.cpp
 Download the working `main.cpp` from the workshop repository and place it in your project:
 
 ```bash
-cd ~/repo/alif/alif_vscode-template/mv2_runner
+cd ~/alif/alif_vscode-template/mv2_runner
 curl -L -o main.cpp \
   https://raw.githubusercontent.com/ArmDeveloperEcosystem/workshop-ethos-u/main/main.cpp
 ```
@@ -25,7 +25,7 @@ curl -L -o main.cpp \
 If you prefer, you can clone the full repository with `git clone https://github.com/ArmDeveloperEcosystem/workshop-ethos-u.git` and copy `main.cpp` from there.
 {{% /notice %}}
 
-The following sections explain what the code does. You don't need to modify anything; the downloaded file is ready to build.
+The following sections explain what the code does. The downloaded file is ready to build as-is.
 
 ## Fault handlers
 
@@ -124,19 +124,12 @@ The method allocator holds the loaded model graph. The temp allocator provides s
 
 ## The inference pipeline
 
-The `run_inference()` function follows a 10-step pipeline:
+The `run_inference()` function handles the full pipeline from model loading to output. It starts by initializing the ExecuTorch runtime and creating a zero-copy data loader that reads the compiled `.pte` model directly from flash memory. The program is then parsed and method metadata queried to determine how much planned memory the model needs.
 
-1. **Initialize** the ExecuTorch runtime.
-2. **Create a data loader** that reads the model directly from flash memory (zero-copy).
-3. **Load the program** (parse the `.pte` flatbuffer).
-4. **Query method metadata** to find out how many planned buffers the model needs and how large they are.
-5. **Set up planned memory** by carving sub-allocations from the SRAM1 pool.
-6. **Create the memory manager** that ties together the method, temp, and planned allocators.
-7. **Load the method** (the `forward` function of the model).
-8. **Prepare the input tensor**: convert the embedded int8 image data to float32 (the model's first operator is `quantize_per_tensor`, which expects float input).
-9. **Execute inference**: the quantize op runs on the CPU, the entire MobileNetV2 backbone runs as a single NPU command stream on the Ethos-U85, and the dequantize op runs back on the CPU.
-10. **Read the output**: find the argmax of the 1000-class output vector to get the predicted ImageNet class.
+Memory is set up next: sub-allocations are carved from the SRAM1 pool for planned buffers, and a memory manager ties together the method, temp, and planned allocators. Once memory is in place, the `forward` method is loaded.
 
-The NPU handles the bulk of the computation. The CPU-side overhead (ExecuTorch loading, input conversion, quantize/dequantize) is small compared to the NPU workload.
+Before inference runs, the input tensor is prepared by converting the embedded int8 image data to float32. This is needed because the model's first operator is `quantize_per_tensor`, which expects float input. Inference then runs in three stages: the quantize operator executes on the CPU, the entire MobileNetV2 backbone runs as a single NPU command stream on the Ethos-U85, and the dequantize operator runs back on the CPU. Finally, the argmax of the 1000-class output vector gives the predicted ImageNet class.
 
-You now have the application code in place. The next section configures the memory layout to accommodate the model and ExecuTorch runtime.
+The NPU handles the bulk of the computation. The CPU-side overhead of ExecuTorch loading, input conversion, and quantize/dequantize is small compared to the NPU workload.
+
+The application code is in place. The next section configures the memory layout to accommodate the model and ExecuTorch runtime.