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v1.0.0
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About this Skill

Ideal for AI Agents like Cursor and AutoGPT that require evidence-driven performance optimization for issues like FPS drops and slow load times. Performance-optimization is a technique to improve application performance by measuring and optimizing runtime, load time, and other key metrics.

Features

Defines symptoms and targets for performance optimization, such as FPS drops and slow first paint
Measures performance before changing code using browser performance tools
Captures reproduction of issues, including GPU/CPU pegged and memory growth
Supports measurable targets, like steady 60 FPS and smaller JS bundles
Optimizes load time by reducing large bundles and long shader compile times

# Core Topics

deadronos deadronos
[0]
[0]
Updated: 3/7/2026

Quality Score

Top 5%
20
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add deadronos/deadronostask/performance-optimization

Agent Capability Analysis

The performance-optimization MCP Server by deadronos is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use performance-optimization, performance-optimization setup guide, what is performance-optimization.

Ideal Agent Persona

Ideal for AI Agents like Cursor and AutoGPT that require evidence-driven performance optimization for issues like FPS drops and slow load times.

Core Value

Empowers agents to identify and resolve performance bottlenecks using measurable targets, such as achieving steady 60 FPS, reducing draw calls, and minimizing JS bundle sizes, leveraging browser performance metrics and optimization techniques.

Capabilities Granted for performance-optimization MCP Server

Debugging FPS drops and stuttering in real-time applications
Optimizing load times by reducing bundle sizes and improving shader compile times
Analyzing and improving GPU/CPU utilization to prevent pegging and memory growth

! Prerequisites & Limits

  • Requires reproduction of the performance issue to capture relevant metrics
  • Limited to optimizing performance for specific, measurable targets
Project
SKILL.md
2.4 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
Readonly

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.

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