skill-based fixes

Author: anupras-mohapatra-arm

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/1-overview.md

@@ -11,9 +11,11 @@ layout: learningpathall
 
 The Arm MCP Server exposes Arm Performix as a first-class tool that AI coding assistants can invoke directly. Rather than switching between your IDE and the Performix GUI to analyze results and then back again to apply code changes, an AI agent can orchestrate the entire profiling pipeline. Configuring the recipe, launching the collection run, retrieving hotspot data, and proposing optimizations can all be part of a single agentic workflow.
 
-## What the Arm Performix MCP server tool is
+## What the Arm Performix tool is
 
-Arm Performix is a performance profiling tool that simplifies the workflow of collecting CPU samples, building flame graphs, and identifying the functions that dominate application runtime. When integrated into the MCP server, it lets an AI agent orchestrate the entire profiling pipeline — configuring the recipe, launching the collection run, and retrieving the resulting hotspot data — without manual interaction with the Performix engine.
+Arm Performix is a performance profiling tool that simplifies the workflow of collecting CPU samples, building flame graphs, and identifying the functions that dominate application runtime. 
+
+When you integrate Arm Performix into the MCP server, the tool lets an AI agent orchestrate the entire profiling pipeline — configuring the recipe, launching the collection run, and retrieving the resulting hotspot data — without manual interaction with the Performix engine.
 
 You don't need to context switch between your IDE and the Performix GUI to analyze results and then back again to apply code changes. An AI agent can do all of this for you in a single agentic workflow.
 
@@ -53,7 +55,7 @@ Every decision in the loop is grounded in the hotspot data returned by the tool;
 
 To use the Arm MCP Server with an AI coding assistant, configure the assistant to connect to the MCP server. Connecting your assistant allows it to query Arm-specific tools, documentation, and capabilities exposed through the Model Context Protocol (MCP).
 
-The required configuration steps vary by AI coding assistant. Refer to the following install guides for step-by-step instructions on connecting AI coding assistants to the Arm MCP server:
+The required configuration steps vary by AI coding assistant. For step-by-step instructions on connecting AI coding assistants to the Arm MCP server, see the following install guides:
 
 - [GitHub Copilot](/install-guides/github-copilot/)
 - [Antigravity CLI](/install-guides/antigravity/)

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/2-setup.md

@@ -19,7 +19,7 @@ You don't need to understand the Mandelbrot algorithm to follow the Learning Pat
 
 ## Connect to your Arm target
 
-For profiling, you'll target an AWS Graviton3-based metal instance (`m7g.metal`) with 64 Neoverse V1 cores. Any Arm Linux server with multiple cores works for the parallelization step, but a metal instance gives you direct access to all hardware threads without the overhead of virtualization.
+For profiling, you'll target an AWS Graviton3-based metal instance (`m7g.metal`) with 64 Neoverse V1 cores. Any Arm Linux server with multiple cores works, but a metal instance gives you direct access to all hardware threads without the overhead of virtualization.
 
 Connect to the remote target via SSH through the Arm MCP Server. The `apx_recipe_run` tool accepts the target host IP address and SSH username directly as parameters, so there's no separate target configuration step required.
 
@@ -58,6 +58,7 @@ Note the absolute path to the binary on the remote server. You'll need this when
 ```text
 /home/ec2-user/Mandelbrot-Example/build/mandelbrot_single_thread_debug
 ```
+
 ## What you've accomplished and what's next
 
 You now have everything in place for profiling: a compiled, debug-enabled binary on an Arm Neoverse target that Performix can reach.

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/4-optimize.md

@@ -81,7 +81,7 @@ The only remaining hotspot is `Mandelbrot::draw` itself at ~98.6% of samples, wh
 
 ## What you've accomplished
 
-You've now applied AI-suggested optimizations — such as replacing `std::complex<double>` with plain `double` arithmetic, enabling `-03` for compiler optimizations, and eliminating sqrt in the escape check — to the Mandelbrot application.
+You've now applied AI-suggested optimizations — such as replacing `std::complex<double>` with plain `double` arithmetic, enabling `-O3` for compiler optimizations, and eliminating sqrt in the escape check — to the Mandelbrot application.
 
 The cumulative result, measured by profile sample counts, was a reduction from approximately 48,750 baseline samples to approximately 3,997 — a ~12x speedup — through three rounds of code changes, each validated by a re-profile before moving to the next.
 

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md

@@ -1,5 +1,5 @@
 ---
-title: Identify code hotspots using Arm Performix through the Arm MCP Server
+title: Identify and optimize code hotspots using Arm Performix through the Arm MCP Server
 
 description: Learn how to use an AI agent and the Performix tool through the Arm MCP Server to run the Code Hotspots recipe on a C++ application, interpret flame graph results, and apply targeted optimizations on Arm Neoverse.
 
@@ -34,7 +34,7 @@ generated_summary_faq:
   faq_generated_at: '2026-07-07T16:22:08Z'
   faq_source_hash: a81d31a804debf71196a478bdf388c9e58b6dab67a9e881b916f3d0169d9555d
   summary: >-
-    You'll combine the Arm MCP Server's `apx_recipe_run` tool with an AI agent to drive the complete Code Hotspots workflow to profile on Arm Neoverse. First, you'll build an intentionally unoptimized Mandelbrot
+    You'll combine the Arm MCP Server's `apx_recipe_run` tool with an AI agent to run the complete Code Hotspots workflow on an Arm Neoverse target. First, you'll build an intentionally unoptimized Mandelbrot
     C++ application on a remote Arm Linux target, then use a GitHub Copilot prompt file to run
     the Performix Code Hotspots recipe. The agent confirms target details, executes collection,
     and returns a flame graph with structured hotspot data to pinpoint the hottest functions.
@@ -55,7 +55,7 @@ generated_summary_faq:
   - question: Should I compile the Mandelbrot example with optimizations before profiling?
     answer: >-
       No. The single-threaded, unoptimized build is intentional so the hotspot analysis produces
-      a clear signal. The agent later proposes enabling -O3 as part of the optimization pass.
+      a clear signal. The agent later proposes enabling `-O3` as part of the optimization pass.
   - question: How do I know the agent is targeting the correct machine?
     answer: >-
       The agent explicitly confirms your remote target details before running the Code Hotspots