Fetches axiom-metal-migration from charleswiltgen/axiom and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/axiom-metal-migration
Restart Cursor to activate axiom-metal-migration. Access via /axiom-metal-migration in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
❌ "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 MGLContextimportMetalANGLElet context =MGLContext(api: kMGLRenderingAPIOpenGLES3)MGLContext.setCurrent(context)// 3. Replace GLKView with MGLKViewlet glView =MGLKView(frame: bounds, context: context)glView.delegate =selfglView.drawableDepthFormat =.format24
// 4. Existing GL code works unchangedglClearColor(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
›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
