performance-optimization — community performance-optimization, deadronostask, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for AI Agents like Cursor, Windsurf, and Claude Code needing advanced performance optimization and debugging capabilities for runtime and load time issues. Task Manager (Learning Project)

deadronos deadronos
[0]
[0]
Updated: 3/5/2026

Agent Capability Analysis

The performance-optimization skill by deadronos is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for AI Agents like Cursor, Windsurf, and Claude Code needing advanced performance optimization and debugging capabilities for runtime and load time issues.

Core Value

Empowers agents to identify and resolve performance bottlenecks using measurable targets, such as achieving steady 60 FPS, reducing draw calls, and optimizing GPU/CPU usage, all while leveraging browser performance metrics and tools.

Capabilities Granted for performance-optimization

Debugging FPS drops and stutters in real-time applications
Optimizing load times by minimizing bundle sizes and shader compile times
Analyzing and reducing memory growth in long-running processes

! Prerequisites & Limits

  • Requires browser performance metrics and debugging tools
  • Limited to optimizing runtime and load time issues
  • Needs measurable targets for effective performance optimization
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

performance-optimization

Install performance-optimization, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command...

SKILL.md
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Performance Optimization (Opt-in)

Use this skill to make performance work repeatable and evidence-driven.

Workflow (always follow)

  1. Define the symptom + target

    • Runtime: FPS drops, stutters, GPU/CPU pegged, memory growth.
    • Load time: slow first paint, large bundle, long shader compile.
    • Pick a measurable target (e.g., steady 60 FPS, lower draw calls, smaller JS).
  2. Measure before changing code

    • Reproduce the issue and capture one of:
      • Browser Performance trace (CPU main thread)
      • R3F/Three stats (FPS, draw calls, triangles)
      • Memory timeline (detached nodes, heap growth)
  3. Classify the bottleneck

    • CPU-bound: heavy JS, too many React renders, expensive per-frame work.
    • GPU-bound: too many draw calls, heavy shaders/postprocessing, high DPR.
    • IO/Load-bound: big bundles/assets, blocking work on startup.
    • Memory-bound: leaks in textures/geometries/materials, retained arrays.
  4. Apply the smallest fix that moves the metric

    • Prefer fixes that reduce work per frame and draw calls.
    • Keep changes surgical and verify the metric improved.
  5. Verify and guard against regressions

    • Run npm test, npm run lint, npm run typecheck.
    • If you introduced a new helper/heuristic, add a small unit test.

R3F / Three.js tactics (common wins)

  • Draw calls: prefer instancing/merging; reduce unique materials.
  • DPR: cap DPR or apply device-specific caps. Dynamic DPR techniques are intended for R3F projects and are not applicable in this repo.
  • Per-frame work: keep useFrame callbacks tiny; avoid allocations inside useFrame.
  • Memoization: cache geometries/materials/textures; reuse vectors/quaternions.
  • Postprocessing: disable/scale effects on low perf; prefer fewer passes.
  • Shadows: reduce shadow map size; limit shadow-casting lights.

React tactics (common wins)

  • Avoid state updates every frame; use refs for frame-local values.
  • Stabilize props to prevent re-render cascades.
  • Split heavy components; lazy-load non-critical UI where appropriate.

When to load deeper reference material

  • For generic perf checklists across frontend/backend/db: read references/performance-optimization.md.
  • Dynamic DPR techniques are not applicable for this repository (no R3F usage).
  • For offloading CPU-heavy loops: consider the js-worker-multithreading skill.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is performance-optimization?

Perfect for AI Agents like Cursor, Windsurf, and Claude Code needing advanced performance optimization and debugging capabilities for runtime and load time issues. Task Manager (Learning Project)

How do I install performance-optimization?

Run the command: npx killer-skills add deadronos/deadronostask/performance-optimization. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for performance-optimization?

Key use cases include: Debugging FPS drops and stutters in real-time applications, Optimizing load times by minimizing bundle sizes and shader compile times, Analyzing and reducing memory growth in long-running processes.

Which IDEs are compatible with performance-optimization?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for performance-optimization?

Requires browser performance metrics and debugging tools. Limited to optimizing runtime and load time issues. Needs measurable targets for effective performance optimization.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add deadronos/deadronostask/performance-optimization. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use performance-optimization immediately in the current project.

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