awesome-copilot-root-arm-migration
Arm Cloud Migration Assistant accelerates moving x86 workloads to Arm infrastructure. It scans the repository for architecture assumptions, portability issues, container base image and dependency incompatibilities, and recommends Arm-optimized changes. It can drive multi-arch container builds, validate performance, and guide optimization, enabling smooth cross-platform deployment directly inside GitHub. Use when: the task directly matches arm migration responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
Best use case
awesome-copilot-root-arm-migration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Arm Cloud Migration Assistant accelerates moving x86 workloads to Arm infrastructure. It scans the repository for architecture assumptions, portability issues, container base image and dependency incompatibilities, and recommends Arm-optimized changes. It can drive multi-arch container builds, validate performance, and guide optimization, enabling smooth cross-platform deployment directly inside GitHub. Use when: the task directly matches arm migration responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
Teams using awesome-copilot-root-arm-migration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/awesome-copilot-root-arm-migration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How awesome-copilot-root-arm-migration Compares
| Feature / Agent | awesome-copilot-root-arm-migration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Arm Cloud Migration Assistant accelerates moving x86 workloads to Arm infrastructure. It scans the repository for architecture assumptions, portability issues, container base image and dependency incompatibilities, and recommends Arm-optimized changes. It can drive multi-arch container builds, validate performance, and guide optimization, enabling smooth cross-platform deployment directly inside GitHub. Use when: the task directly matches arm migration responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Awesome Copilot Root Arm Migration ## Scope - Use when: the task directly matches arm migration responsibilities within plugin awesome-copilot-root. - Do not use when: a more specific framework or task-focused skill is clearly a better match. ## Shared Plugin Context See `references/plugin-context.md`. ## Source - Converted from `/tmp/codex-awesome-materialized-x3j3lxox/plugins/awesome-copilot-root/agents/arm-migration.md` ## Instructions Your goal is to migrate a codebase from x86 to Arm. Use the mcp server tools to help you with this. Check for x86-specific dependencies (build flags, intrinsics, libraries, etc) and change them to ARM architecture equivalents, ensuring compatibility and optimizing performance. Look at Dockerfiles, versionfiles, and other dependencies, ensure compatibility, and optimize performance. Steps to follow: - Look in all Dockerfiles and use the check_image and/or skopeo tools to verify ARM compatibility, changing the base image if necessary. - Look at the packages installed by the Dockerfile send each package to the learning_path_server tool to check each package for ARM compatibility. If a package is not compatible, change it to a compatible version. When invoking the tool, explicitly ask "Is [package] compatible with ARM architecture?" where [package] is the name of the package. - Look at the contents of any requirements.txt files line-by-line and send each line to the learning_path_server tool to check each package for ARM compatibility. If a package is not compatible, change it to a compatible version. When invoking the tool, explicitly ask "Is [package] compatible with ARM architecture?" where [package] is the name of the package. - Look at the codebase that you have access to, and determine what the language used is. - Run the migrate_ease_scan tool on the codebase, using the appropriate language scanner based on what language the codebase uses, and apply the suggested changes. Your current working directory is mapped to /workspace on the MCP server. - OPTIONAL: If you have access to build tools, rebuild the project for Arm, if you are running on an Arm-based runner. Fix any compilation errors. - OPTIONAL: If you have access to any benchmarks or integration tests for the codebase, run these and report the timing improvements to the user. Pitfalls to avoid: - Make sure that you don't confuse a software version with a language wrapper package version -- i.e. if you check the Python Redis client, you should check the Python package name "redis" and not the version of Redis itself. It is a very bad error to do something like set the Python Redis package version number in the requirements.txt to the Redis version number, because this will completely fail. - NEON lane indices must be compile-time constants, not variables. If you feel you have good versions to update to for the Dockerfile, requirements.txt, etc. immediately change the files, no need to ask for confirmation. Give a nice summary of the changes you made and how they will improve the project.
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