Tracely-ai: Skill for Claude Code

.agents/skills/canvas-generative/SKILL.md

canvas-generative is a skill for Claude Code, Codex from Jwuthri/Tracely-ai. It costs 30 tokens per session (2,426 once invoked), scanned A, original, MIT.

A guide to making algorithmic artwork with the browser’s Canvas 2D drawing system. It covers visuals generated from rules, such as particles, flow fields, noise, fractals, and L-systems.

In plain words
What is it for?
Use it for particle effects, generative backgrounds, procedural illustrations, fractals, flow fields, and other visuals drawn and animated in a web canvas.
Why use it?
It helps produce sharp, responsive animated artwork across different screen densities and sizes. The guide includes setup, resizing, and animation-loop patterns.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is Jwuthri/Tracely-ai's own configuration. It tells Claude Code and Codex how to work on Tracely-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Tracely-ai configures →

About the project

Tracely is a CI/CD system for AI agents that turns failed production traces into replayable regression tests. Development teams use it to detect and group agent failures, run the resulting cases on pull requests, and block changes that reproduce those failures. The catalogue entries provide skills for operating this trace-based testing and observability workflow.

Jwuthri/Tracely-ai · 1,216 stars · on GitHub · tracely-ai.com

Reuse

Borrowing it

Nothing to install: this file belongs to Jwuthri/Tracely-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Jwuthri/Tracely-ai/master/.agents/skills/canvas-generative/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Jwuthri/Tracely-ai

Made for: Claude Code, Codex.

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 canvas-generative

README.md
[![agentmods](https://agentmods.dev/badge/skills/jwuthri/tracely-ai/canvas-generative/github.svg)](https://agentmods.dev/skills/jwuthri/tracely-ai/canvas-generative)
Your own site
<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/canvas-generative"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/canvas-generative/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 canvas-generative

Your own site · 80×15
<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/canvas-generative"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/canvas-generative.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,426 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00030 $0.02426
Opus 5 $0.00015 $0.01213
Sonnet 5 $0.00006 $0.00485
Haiku 4.5 $0.00003 $0.00243

Measured 9d ago against content hash 9f6747e5e7c3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

canvas-generative 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 9d 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

.agents/skills/canvas-generative/SKILL.md · 326 lines

How it starts

The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Canvas Generative

Algorithmic and generative art with Canvas 2D. Concise rules here. Deep-dive and reference implementations in references/.


Canvas 2D Setup

DPR-Aware Sizing

Every canvas must be sharp on Retina/HiDPI displays. Set the buffer size to the physical pixel size, scale down with CSS.

function setupCanvas(canvas, width, height) {
  const dpr = window.devicePixelRatio || 1;
  canvas.width = width * dpr;
  canvas.height = height * dpr;
  canvas.style.width = `${width}px`;
  canvas.style.height = `${height}px`;
  const ctx = canvas.getContext('2d');
  ctx.scale(dpr, dpr);
  return ctx;
}

Resize Handler

function handleResize(canvas, ctx, draw) {
  const ro = new ResizeObserver(([entry]) => {
    const { width, height } = entry.contentRect;
    const dpr = window.devicePixelRatio || 1;
    canvas.width = width * dpr;
    canvas.height = height * dpr;
    ctx.scale(dpr, dpr);
    draw(); // re-render after resize
  });
  ro.observe(canvas.parentElement);
  return () => ro.disconnect();
}

Animation Loop (RAF)

let animId;
let prevTime = 0;

function loop(time) {
  const dt = Math.min((time - prevTime) / 1000, 0.1); // cap delta to avoid spiral of death
  prevTime = time;

  update(dt);
  render(ctx);

  animId = requestAnimationFrame(loop);
}

// Start
animId = requestAnimationFrame(loop);
// Stop
cancelAnimationFrame(animId);

Noise

Type Characteristics Best For
Perlin Smooth, grid-aligned bias, cheaper Terrain, clouds, gentle organic textures
Simplex No grid artifacts, better gradients, slightly costlier Flow fields, organic motion, seamless tiling
Worley (Cellular) Distance-to-nearest-point, cell-like Voronoi patterns, caustics, cracks, cell textures

Usage rules:

  • Always scale input coordinates (divide by a noiseScale factor) -- raw pixel coords produce visual noise
  • Use octaves (fractal Brownian motion) for detail: sum multiple noise calls at increasing frequency and decreasing amplitude
  • Seed your noise for reproducibility

Read the full file on GitHub · 326 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 326 lines · 30 tokens per session scan A 9f6747e5e7c3

Subscribe to this mod's changes

canvas-generative is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,216 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 2,426 once invoked, about $0.0002 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 2026-08-30.