ecs-performance

ecs-performance is a skill for Claude Code from bullish0x/GameStudio. It costs 23 tokens per session (4,571 once invoked), scanned A, original, MIT.

A guide to making an entity-component-system (ECS) game engine run faster. An ECS stores game objects as entities and their data as components.

In plain words
What is it for?
Use it to optimize archetype storage, reuse memory, improve cache-friendly data access, batch entity processing, and profile bottlenecks.
Why use it?
It helps reduce slow frame times, excess memory use, and performance problems when a game has many entities or targets mobile devices.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to optimize archetype storage, reuse memory, improve cache-friendly data access, batch entity processing, and profile bottlenecks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bullish0x/gamestudio/ecs-performance
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add bullish0x/GameStudio --skill ecs-performance
Clone the repo
git clone --depth 1 https://github.com/bullish0x/GameStudio

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ecs-performance

README.md
[![agentmods](https://agentmods.dev/badge/skills/bullish0x/gamestudio/ecs-performance/github.svg)](https://agentmods.dev/skills/bullish0x/gamestudio/ecs-performance)
Your own site
<a href="https://agentmods.dev/skills/bullish0x/gamestudio/ecs-performance"><img src="https://agentmods.dev/badge/skills/bullish0x/gamestudio/ecs-performance/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ecs-performance

Your own site · 80×15
<a href="https://agentmods.dev/skills/bullish0x/gamestudio/ecs-performance"><img src="https://agentmods.dev/badge/skills/bullish0x/gamestudio/ecs-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,571 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.04571
Opus 5 $0.00012 $0.02286
Sonnet 5 $0.00005 $0.00914
Haiku 4.5 $0.00002 $0.00457

Measured 7d ago against content hash 78c3de2bbab9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ecs-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/ecs-performance/SKILL.md · 782 lines

How it starts

The opening of the file, as written. The whole thing — 782 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ECS Performance Optimization

When to Use

Use this skill when:

  • Scaling to thousands of entities
  • Optimizing frame time
  • Reducing memory allocations
  • Improving cache coherency
  • Debugging performance issues
  • Targeting mobile devices

Core Principles

  1. Data-Oriented: Organize data for cache efficiency
  2. Minimize Allocations: Pool and reuse objects
  3. Batch Operations: Process similar entities together
  4. Profile First: Measure before optimizing
  5. Lazy Evaluation: Compute only when needed
  6. Archetype-Based: Group entities by components

Performance Optimization Techniques

1. Archetype Optimization

// core/Archetype.ts
export interface Archetype {
  signature: string;
  entities: Entity[];
  componentData: Map<ComponentConstructor<any>, any[]>;
}

export class ArchetypeStorage {
  private archetypes = new Map<string, Archetype>();

  getArchetype(componentTypes: ComponentConstructor<any>[]): Archetype {
    const signature = this.getSignature(componentTypes);
    let archetype = this.archetypes.get(signature);

    if (!archetype) {
      archetype = {
        signature,
        entities: [],
        componentData: new Map(),
      };

      // Pre-allocate component arrays
      for (const type of componentTypes) {
        archetype.componentData.set(type, []);
      }

      this.archetypes.set(signature, archetype);
    }

    return archetype;
  }

  addEntity(entity: Entity, componentTypes: ComponentConstructor<any>[]): void {
    const archetype = this.getArchetype(componentTypes);
    archetype.entities.push(entity);

    // Add component data to arrays
    for (const type of componentTypes) {
      const component = entity.getComponent(type);
      const array = archetype.componentData.get(type)!;
      array.push(component);
    }
  }

  removeEntity(entity: Entity, componentTypes: ComponentConstructor<any>[]): void {
    const archetype = this.getArchetype(componentTypes);
    const index = archetype.entities.indexOf(entity);

    if (index !== -1) {
      // Remove from entity array
      archetype.entities.splice(index, 1);

      // Remove from component arrays
      for (const [type, array] of archetype.componentData) {
        array.splice(index, 1);
      }
    }
  }

  // Iterate cache-friendly
  iterateArchetype<T extends any[]>(
    componentTypes: ComponentConstructor<any>[],
    callback: (components: T, entity: Entity) => void
  ): void {
    const archetype = this.getArchetype(componentTypes);
    const componentArrays = componentTypes.map((type) =>
      archetype.componentData.get(type)
    );

    const length = archetype.entities.length;

    for (let i = 0; i < length; i++) {
      const components = componentArrays.map((arr) => arr![i]) as T;
      callback(components, archetype.entities[i]);
    }
  }

  private getSignature(componentTypes: ComponentConstructor<any>[]): string {
    return componentTypes
      .map((t) => t.name)
      .sort()
      .join(':');
  }
}

Read the full file on GitHub · 782 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 782 lines · 23 tokens per session scan A 78c3de2bbab9

Subscribe to this mod's changes

ecs-performance is a skill published in the GitHub repository bullish0x/GameStudio (12 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 4,571 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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