glsl

martinholovsky/claude-skills-generator · updated Jun 2, 2026

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$npx skills add https://github.com/martinholovsky/claude-skills-generator --skill glsl
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summary

File Organization: This skill uses split structure. See references/ for advanced shader patterns.

skill.md

GLSL Shader Programming Skill

File Organization: This skill uses split structure. See references/ for advanced shader patterns.

1. Overview

This skill provides GLSL shader expertise for creating holographic visual effects in the JARVIS AI Assistant HUD. It focuses on efficient GPU programming for real-time rendering.

Risk Level: LOW - GPU-side code with limited attack surface, but can cause performance issues

Primary Use Cases:

  • Holographic panel effects with scanlines
  • Animated energy fields and particle systems
  • Data visualization with custom rendering
  • Post-processing effects (bloom, glitch, chromatic aberration)

2. Core Responsibilities

2.1 Fundamental Principles

  1. TDD First: Write visual regression tests and shader unit tests before implementation
  2. Performance Aware: Profile GPU performance, optimize for 60 FPS target
  3. Precision Matters: Use appropriate precision qualifiers for performance
  4. Avoid Branching: Minimize conditionals in shaders for GPU efficiency
  5. Optimize Math: Use built-in functions, avoid expensive operations
  6. Uniform Safety: Validate uniform inputs before sending to GPU
  7. Loop Bounds: Always use constant loop bounds to prevent GPU hangs
  8. Memory Access: Optimize texture lookups and varying interpolation

3. Implementation Workflow (TDD)

3.1 Step 1: Write Failing Test First

// tests/shaders/holographic-panel.test.ts
import { describe, it, expect, beforeEach } from 'vitest'
import { WebGLTestContext, captureFramebuffer, compareImages } from '../utils/webgl-test'

describe('HolographicPanelShader', () => {
  let ctx: WebGLTestContext

  beforeEach(() => {
    ctx = new WebGLTestContext(256, 256)
  })

  // Unit test: Shader compiles
  it('should compile without errors', () => {
    const shader = ctx.compileShader(holoFragSource, ctx.gl.FRAGMENT_SHADER)
    expect(shader).not.toBeNull()
    expect(ctx.getShaderErrors()).toEqual([])
  })

  // Unit test: Uniforms are accessible
  it('should have required uniforms', () => {
    const program = ctx.createProgram(vertSource, holoFragSource)
    expect(ctx.getUniformLocation(program, 'uTime')).not.toBeNull()
    expect(ctx.getUniformLocation(program, 'uColor')).not.toBeNull()
    expect(ctx.getUniformLocation(program, 'uOpacity')).not.toBeNull()
  })

  // Visual regression test
  it('should render scanlines correctly', async () => {
    ctx.renderShader(holoFragSource, { uTime: 0, uColor: [0, 0.5, 1], uOpacity: 1 })
    const result = captureFramebuffer(ctx)
    const baseline = await loadBaseline('holographic-scanlines.png')
    expect(compareImages(result, baseline, { threshold: 0.01 })).toBeLessThan(0.01)
  })

  // Edge case test
  it('should handle extreme UV values', () => {
    const testCases = [
      { uv: [0, 0], expected: 'no crash' },
      { uv: [1, 1], expected: 'no crash' },
      { uv: [0.5, 0.5], expected: 'no crash' }
    ]
    testCases.forEach(({ uv }) => {
      expect(() => ctx.renderAtUV(holoFragSource, uv)).not.toThrow()
    })
  })
})

3.2 Step 2: Implement Minimum to Pass

// Start with minimal shader that passes tests
#version 300 es
precision highp float;

uniform float uTime;
uniform vec3 uColor;
uniform float uOpacity;

in vec2 vUv;
out vec4 fragColor;

void main() {
  // Minimal implementation to pass compilation test
  fragColor = vec4(uColor, uOpacity);
}

3.3 Step 3: Refactor with Full Implementation

// Expand to full implementation after tests pass
void main() {
  vec2 uv = vUv;
  float scanline = sin(uv.y * 100.0) * 0.1 + 0.9;
  float pulse = sin(uTime * 2.0) * 0.1 + 0.9;
  vec3 color = uColor * scanline * pulse;
  fragColor = vec4(color, uOpacity);
}

3.4 Step 4: Run Full Verification

# Run all shader tests
npm run test:shaders

# Visual regression tests
npm run test:visual -- --update-snapshots  # First time only
npm run test:visual

# Performance benchmark
npm run bench:shaders

# Cross-browser compilation check
npm run test:webgl-compat

4. Technology Stack & Versions

4.1 GLSL Versions

Version Context Features
GLSL ES 3.00 WebGL 2.0 Modern features, better precision
GLSL ES 1.00 WebGL 1.0 Legacy support

4.2 Shader Setup

#version 300 es
precision highp float;
precision highp int;

// WebGL 2.0 shader header

5. Performance Patterns

5.1 Avoid Branching - Use Mix/Step

// ❌ BAD - GPU branch divergence
vec3 getColor(float value) {
  if (value < 0.3) {
    return vec3(1.0, 0.0, 0.0);  // Red
  } 
how to use glsl

How to use glsl 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 glsl
2

Execute installation command

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

$npx skills add https://github.com/martinholovsky/claude-skills-generator --skill glsl

The skills CLI fetches glsl from GitHub repository martinholovsky/claude-skills-generator 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/glsl

Reload or restart Cursor to activate glsl. Access the skill through slash commands (e.g., /glsl) 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. 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

4.736 reviews
  • Aanya Gupta· Dec 24, 2024

    Registry listing for glsl matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Dec 20, 2024

    Useful defaults in glsl — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chinedu Rahman· Dec 20, 2024

    glsl reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • James Bhatia· Dec 12, 2024

    glsl fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakshi Patil· Nov 11, 2024

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

  • Omar Lopez· Nov 11, 2024

    glsl fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Layla Martinez· Nov 3, 2024

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

  • Chaitanya Patil· Oct 26, 2024

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

  • Yusuf Bhatia· Oct 22, 2024

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

  • Soo Robinson· Oct 2, 2024

    We added glsl from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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