webgpu▌
cazala/webgpu-skill · updated Apr 8, 2026
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Design, implement, and debug WebGPU applications, GPU compute pipelines, and WGSL shaders.
- ›Covers WebGPU initialization, device setup, compute and render pipelines, shader authoring, and GPU/CPU synchronization strategies
- ›Includes architecture patterns for modular passes, phase-based simulation, spatial indexing, and capability fallback handling
- ›Provides guidance on performance optimization, safe readback strategies, and debugging practices for rendering, compute, ML workloads, and p
WebGPU Skill
Use this skill to design, implement, and debug WebGPU applications and GPU compute pipelines. Keep it framework-agnostic and focus on reusable WebGPU/WGSL patterns.
What this skill covers
- Cover WebGPU initialization, device setup, and surface configuration.
- Cover compute pipelines, workgroup sizing, and storage buffer layout.
- Cover render pipelines, render passes, and post-processing patterns.
- Cover GPU/CPU synchronization and safe readback strategies.
- Cover performance and debugging practices.
- Cover architecture patterns: modular passes, phase-based simulation, and capability handling.
- Cover use cases: rendering, compute, ML training/inference, grid simulations, and systems modeling.
Core principles
- Choose a capability strategy: fallback runtime, reduced mode, or fail fast.
- Avoid full GPU readbacks in hot paths; use localized queries or small readback buffers.
- Structure simulation with phases (state, apply, integrate, constrain, correct) to keep WGSL cohesive.
- Use spatial grids or other spatial indexing for neighbor queries and high particle counts.
- Build modular passes so render and compute stages stay composable and testable.
Workflow
When asked to build a WebGPU feature:
- Confirm the target platform and WebGPU support expectations.
- Propose a resource layout (buffers, textures, bind groups) with a simple data model.
- Sketch the pipeline graph (compute vs render passes) and dependencies.
- Provide minimal working code and scale up with performance constraints.
- Choose a capability strategy when WebGPU is unavailable.
Deliverable checklist
- Provide clean WebGPU init and error handling.
- Include a buffer layout with alignment notes (16-byte struct alignment for WGSL).
- Include a pass graph with clear read/write ownership (ping-pong textures if needed).
- Call out readback and when it is safe.
- Provide an optional fallback or reduced mode for critical functionality.
References and assets
- Use REFERENCE.md for a compact WebGPU cheat sheet.
- Use references/ for deeper patterns and concepts.
- Use examples/ for runnable snippets.
- Use templates/ for project scaffolds or starter code.
Quick reference
See REFERENCE.md for a compact WebGPU cheat sheet and references/ for deeper patterns, including references/use-cases.md and references/simulation-patterns.md.
How to use webgpu on Cursor
AI-first code editor with Composer
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 webgpu
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches webgpu from GitHub repository cazala/webgpu-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate webgpu. Access the skill through slash commands (e.g., /webgpu) 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.
List & Monetize Your Skill
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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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 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
Discussion
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Ratings
4.7★★★★★68 reviews- ★★★★★Olivia Srinivasan· Dec 28, 2024
webgpu fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Evelyn White· Dec 28, 2024
I recommend webgpu for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chinedu Okafor· Dec 24, 2024
webgpu is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Emma Flores· Dec 20, 2024
webgpu reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Liam Rahman· Dec 20, 2024
We added webgpu from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kofi Srinivasan· Dec 16, 2024
Registry listing for webgpu matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 12, 2024
Keeps context tight: webgpu is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aisha Haddad· Dec 12, 2024
Useful defaults in webgpu — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ira Torres· Nov 19, 2024
Solid pick for teams standardizing on skills: webgpu is focused, and the summary matches what you get after install.
- ★★★★★Chinedu Desai· Nov 15, 2024
webgpu reduced setup friction for our internal harness; good balance of opinion and flexibility.
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