axiom-metal-migration

charleswiltgen/axiom · updated Apr 8, 2026

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$npx skills add https://github.com/charleswiltgen/axiom --skill axiom-metal-migration
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summary

Porting OpenGL/OpenGL ES or DirectX code to Metal on Apple platforms.

skill.md

Metal Migration

Porting OpenGL/OpenGL ES or DirectX code to Metal on Apple platforms.

When to Use This Skill

Use this skill when:

  • Porting an OpenGL/OpenGL ES codebase to iOS/macOS
  • Porting a DirectX codebase to Apple platforms
  • Deciding between translation layer (MetalANGLE) vs native rewrite
  • Planning a phased migration strategy
  • Evaluating effort vs performance tradeoffs

Red Flags

❌ "Just use MetalANGLE and ship" — Translation layers add 10-30% overhead; fine for demos, not production

❌ "Convert shaders one-by-one without planning" — State management differs fundamentally; you'll rewrite twice

❌ "Keep the GL state machine mental model" — Metal is explicit; thinking GL causes subtle bugs

❌ "Port everything at once" — Phased migration catches issues early; big-bang migrations hide compounding bugs

❌ "Skip validation layer during development" — Metal validation catches 80% of porting bugs with clear messages

❌ "Worry about coordinate systems later" — Y-flip and NDC differences cause the most debugging time

❌ "Performance will be the same or better automatically" — Metal requires explicit optimization; naive ports can be slower

Migration Strategy Decision Tree

Starting a port to Metal?
├─ Need working demo in <1 week?
│   ├─ OpenGL ES source? → MetalANGLE (translation layer)
│   │   └─ Caveats: 10-30% overhead, ES 2/3 only, no compute
│   │
│   └─ Vulkan available? → MoltenVK
│       └─ Caveats: Vulkan complexity, indirect translation
├─ Production app with performance requirements?
│   └─ Native Metal rewrite (recommended)
│       ├─ Phased: Keep GL for reference, port module-by-module
│       └─ Full: Clean rewrite using Metal idioms from start
├─ DirectX/HLSL source?
│   └─ Metal Shader Converter (Apple tool)
│       └─ Converts DXIL bytecode → Metal library
│       └─ See metal-migration-ref for usage
└─ Hybrid approach?
    └─ MetalANGLE for demo → Native Metal incrementally
        └─ Best of both: fast validation, optimal end state

Pattern 1: Translation Layer (Quick Demo Path)

When to use: Validate feasibility, get stakeholder buy-in, prototype

MetalANGLE Setup (OpenGL ES → Metal)

// 1. Add MetalANGLE via SPM or CocoaPods
// GitHub: nicklockwood/MetalANGLE

// 2. Replace EAGLContext with MGLContext
import MetalANGLE

let context = MGLContext(api: kMGLRenderingAPIOpenGLES3)
MGLContext.setCurrent(context)

// 3. Replace GLKView with MGLKView
let glView = MGLKView(frame: bounds, context: context)
glView.delegate = self
glView.drawableDepthFormat = .format24

// 4. Existing GL code works unchanged
glClearColor(0, 0, 0, 1)
glClear(GL_COLOR_BUFFER_BIT)
// ... your existing GL rendering code

Tradeoffs Table

Aspect MetalANGLE Native Metal
Time to demo Hours Days-weeks
Runtime overhead 10-30% Baseline
Shader changes None Full rewrite
Compute shaders Not supported Full support
Future-proof Translation debt Apple-recommended
Debugging GL tools only GPU Frame Capture
Thermal/battery Higher Optimizable

When MetalANGLE Fails

MetalANGLE will NOT work if your code:

  • Uses OpenGL ES extensions not in core ES 2/3
  • Relies on compute shaders (GL_COMPUTE_SHADER)
  • Requires precise GL state machine semantics
  • Needs performance within 10% of native
  • Targets visionOS (no translation layer support)

Pattern 2: Native Metal Rewrite (Production Path)

When to use: Production apps, performance-critical rendering, long-term maintenance

Phased Migration Strategy

Phase 1: Abstraction Layer (1-2 weeks)
├─ Create renderer interface hiding GL/Metal specifics
├─ Keep GL implementation as reference
├─ Define clear boundaries: setup, resources, draw, present
└─ Validate abstraction with existing tests

Phase 2: Metal Backend (2-4 weeks)
├─ Implement Metal renderer behind same interface
├─ Convert shaders GLSL → MSL (use metal-migration-ref)
├─ Run GL and Metal side-by-side for visual diff
├─ GPU Frame Capture for debugging
└─ Milestone: Feature parity, visual match

Phase 3: Optimization (1-2 weeks)
├─ Remove abstraction overhead where it hurts
├─ Use Metal-specific features (argument buffers, indirect)
├─ Profile with Metal System Trace
├─ Tune for thermal envelope and battery
└─ Remove GL backend entirely

GLSL to Metal Shading Language (MSL) Conversion

GLSL MSL Notes
attribute / varying [[stage_in]] struct Vertex attributes via struct
uniform [[buffer(N)]] parameter Explicit binding index
gl_Position Return float4 from vertex Vertex function return value
gl_FragColor Return float4 from fragment Fragment function return value
texture2D(tex, uv) tex.sample(sampler, uv) Separate sampler object
vec2/3/4 float2/3/4 Type names differ
mat4 float4x4 Matrix types differ
mix() mix() Same name
precision mediump float (not needed) Metal infers precision
#version 300 es #include <metal_stdlib> Different preamble

Example conversion:

// GLSL vertex shader
#version 300 es
uniform mat4 u_mvp;
in vec3 a_position;
in vec2 a_texCoord;
out vec2 v_texCoord;

void main() {
    v_texCoord = a_texCoord;
    gl_Position = u_mvp * vec4(a_position, 1.0);
}
// Equivalent MSL vertex shader
#include <metal_stdlib>
using namespace metal;

struct VertexIn {
    float3 position [[attribute(0)]];
    float2 texCoord [[attribute(1)]];
};

struct VertexOut {
    float4 position [[position]];
    float2 texCoord;
};

struct Uniforms {
    float4x4 mvp;
};

vertex VertexOut vertexShader(VertexIn in [[stage_in]],
                              constant Uniforms &uniforms [[buffer(1)]]) {
    VertexOut out;
    out.texCoord = in.texCoord;
    out.position = uniforms.mvp * float4(in.position, 1.0);
    return out;
}

Key differences to watch:

  • GLSL globals → MSL function parameters with [[attribute]] qualifiers
  • Implicit uniform binding → explicit [[buffer(N)]] indices
  • sampler2D combines texture+sampler → Metal separates texture2d and sampler
  • GLSL preprocessor → Metal uses C++ #include and using namespace metal

Core Architecture Differences

Concept OpenGL Metal
State model Implicit, mutable Explicit, immutable PSO
Validation At draw time At PSO creation
Shader compilation Runtime (JIT) Build time (AOT)
Command submission Implicit Explicit command buffers
Resource binding Global state Per-encoder binding
Synchronization Driver-managed App-managed

MTKView Setup (Native Metal)

import MetalKit

class MetalRenderer: NSObject, MTKViewDelegate {
    let device: MTLDevice
    let commandQueue: MTLCommandQueue
    var pipelineState: MTLRenderPipelineState!

    init?(metalView: MTKView) {
        guard let device = MTLCreateSystemDefaultDevice(),
              let queue = device.makeCommandQueue() else {
            return nil
        }
        self.device = device
        self.commandQueue = queue

        metalView.device = device
        metalView.clearColor = MTLClearColor(red: 0, green: 0, blue: 0, alpha: 1)
        metalView.depthStencilPixelFormat = .depth32Float

        super.init()
        metalView.delegate = self

        buildPipeline(metalView: metalView)
    }

    private func buildPipeline(metalView: MTKView) {
        let library = device.makeDefaultLibrary()!
        let vertexFunction = library.makeFunction(name: "vertexShader")
        let fragmentFunction = library.makeFunction(name: "fragmentShader")

        let descriptor = MTLRenderPipelineDescriptor()
        descriptor.vertexFunction = vertexFunction
        descriptor.fragmentFunction = fragmentFunction
        descriptor.colorAttachments[0].pixelFormat = metalView.colorPixelFormat
        descriptor.depthAttachmentPixelFormat = metalView.depthStencilPixelFormat

        // Pre-validated at creation, not at draw time
        pipelineState = try! device.makeRenderPipelineState(descriptor: descriptor)
    }

    func draw(in view: MTKView) {
        guard let drawable = view.currentDrawable,
              let descriptor = view.currentRenderPassDescriptor,
              let commandBuffer = commandQueue.makeCommandBuffer()
how to use axiom-metal-migration

How to use axiom-metal-migration on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add axiom-metal-migration
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/charleswiltgen/axiom --skill axiom-metal-migration

The skills CLI fetches axiom-metal-migration from GitHub repository charleswiltgen/axiom and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/axiom-metal-migration

Reload or restart Cursor to activate axiom-metal-migration. Access the skill through slash commands (e.g., /axiom-metal-migration) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.847 reviews
  • Shikha Mishra· Dec 28, 2024

    axiom-metal-migration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Alexander Gonzalez· Dec 20, 2024

    axiom-metal-migration reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Lucas Gill· Dec 16, 2024

    Keeps context tight: axiom-metal-migration is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Jin Martinez· Dec 16, 2024

    axiom-metal-migration has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Soo Abbas· Dec 4, 2024

    We added axiom-metal-migration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nia Diallo· Nov 27, 2024

    We added axiom-metal-migration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Rahul Santra· Nov 19, 2024

    axiom-metal-migration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Charlotte Diallo· Nov 7, 2024

    Registry listing for axiom-metal-migration matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Mateo Garcia· Nov 7, 2024

    Solid pick for teams standardizing on skills: axiom-metal-migration is focused, and the summary matches what you get after install.

  • Charlotte Ndlovu· Nov 7, 2024

    Keeps context tight: axiom-metal-migration is the kind of skill you can hand to a new teammate without a long onboarding doc.

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