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.
npx skills add 0xheycat/isometric-game-skills --skill canvas-performance-optimizationgit clone --depth 1 https://github.com/0xheycat/isometric-game-skillsWrote 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.
[](https://agentmods.dev/skills/0xheycat/isometric-game-skills/canvas-performance-optimization)<a href="https://agentmods.dev/skills/0xheycat/isometric-game-skills/canvas-performance-optimization"><img src="https://agentmods.dev/badge/skills/0xheycat/isometric-game-skills/canvas-performance-optimization/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.
<a href="https://agentmods.dev/skills/0xheycat/isometric-game-skills/canvas-performance-optimization"><img src="https://agentmods.dev/badge/skills/0xheycat/isometric-game-skills/canvas-performance-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00396 |
| Opus 5 | $0.00015 | $0.00198 |
| Sonnet 5 | $0.00006 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
canvas-performance-optimization 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 10d 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.
What it actually says
Canvas Performance Optimization
Overview
Isometric games die by a thousand draw calls. This skill profiles the frame, culls off-screen tiles, batches from the atlas, and removes per-frame allocations to hit a stable 60fps.
When to Use
- Frame rate drops or stutters.
- Large maps lag when panning.
- You suspect GC pauses or overdraw.
Process
- Profile first - measure where the frame time actually goes.
- Cull: only draw tiles/objects inside the viewport.
- Batch draws from one atlas to minimize texture binds.
- Cache static layers (ground) to an offscreen canvas; redraw only when it changes.
- Eliminate per-frame allocations (reuse arrays/objects).
- Avoid sub-pixel blits; snap to integer pixels.
- Re-profile to confirm the fix, do not assume.
Profile -> Cull -> Batch -> Cache static layer -> Kill allocations -> Re-profile
Rationalizations (Stop Lying to Yourself)
| Excuse | Reality |
|---|---|
| "I know what is slow without profiling" | Guessing wastes hours on the wrong thing. Profile first. |
| "Redrawing the ground every frame is fine" | Static ground should be cached. Redraw only on change. |
Red Flags - STOP if you catch yourself:
- Optimizing before profiling.
- Redrawing static layers every frame.
- Allocating inside the render loop.
Verification
You are NOT done until every box is checked:
- Frame time was profiled before and after.
- Off-screen content is culled and static layers cached.
- No allocations occur inside the render loop; 60fps holds.
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.
- 10d ago First seen · 53 lines · 29 tokens per session scan A c0aa64fa383d
canvas-performance-optimization is a skill published in the GitHub repository 0xheycat/isometric-game-skills (15 stars, last pushed 28d ago), licensed MIT. It adds 29 tokens to every session and 396 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 2026-08-31.
Other skills, from other repositories
threejs-fundamentals
Three.js scene setup, cameras, renderer, Object3D hierarchy, coordinate systems. Use when setting up 3D scenes, creating cameras, configuring renderers, managing object hierarchies, or working with transforms.
threejs-geometry
Three.js geometry creation - built-in shapes, BufferGeometry, custom geometry, instancing. Use when creating 3D shapes, working with vertices, building custom meshes, or optimizing with instanced rendering.
threejs-world-generation
Build deterministic, editable, free-viewpoint Three.js worlds from text or structured briefs. Use for cinematic 3D terrain, semantic regions, procedural biomes, explicit landmarks, environmental scattering, camera fly-throughs, world diagnostics, or requests for a real 3D environment rather than generated 2D footage.…
sound-effects
Generate sound effects from text descriptions using ElevenLabs. Use when creating sound effects, generating audio textures, producing ambient sounds, cinematic impacts, UI sounds, or any audio that isn't speech. Supports looping, duration control, and prompt influence tuning.
character-animation-qa
Review local character animation with schema checks, Playwright browser previews, frame sampling, and FFmpeg/ffprobe final output checks.
pose-library-design
Design reusable 2D character pose libraries, action cycles, and expression states for data-driven animation.