Porting OpenGL/OpenGL ES or DirectX code to Metal on Apple platforms.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaxiom-metal-migrationExecute the skills CLI command in your project's root directory to begin installation:
Fetches axiom-metal-migration from charleswiltgen/axiom and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate axiom-metal-migration. Access via /axiom-metal-migration in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Porting OpenGL/OpenGL ES or DirectX code to Metal on Apple platforms.
Use this skill when:
❌ "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
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
When to use: Validate feasibility, get stakeholder buy-in, prototype
// 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
| 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 |
MetalANGLE will NOT work if your code:
When to use: Production apps, performance-critical rendering, long-term maintenance
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 | 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:
[[attribute]] qualifiers[[buffer(N)]] indicessampler2D combines texture+sampler → Metal separates texture2d and sampler#include and using namespace metal| 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 |
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()✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.8★★★★★47 reviews- SShikha Mishra★★★★★Dec 28, 2024
axiom-metal-migration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAlexander Gonzalez★★★★★Dec 20, 2024
axiom-metal-migration reduced setup friction for our internal harness; good balance of opinion and flexibility.
- LLucas 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.
- JJin Martinez★★★★★Dec 16, 2024
axiom-metal-migration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SSoo Abbas★★★★★Dec 4, 2024
We added axiom-metal-migration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNia Diallo★★★★★Nov 27, 2024
We added axiom-metal-migration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- RRahul Santra★★★★★Nov 19, 2024
axiom-metal-migration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- CCharlotte Diallo★★★★★Nov 7, 2024
Registry listing for axiom-metal-migration matched our evaluation — installs cleanly and behaves as described in the markdown.
- MMateo 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.
- CCharlotte 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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