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.
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.
curl -O https://raw.githubusercontent.com/Jwuthri/Tracely-ai/master/.agents/skills/canvas-generative/SKILL.mdgit clone --depth 1 https://github.com/Jwuthri/Tracely-aiWrote 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/jwuthri/tracely-ai/canvas-generative)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.02426 |
| Opus 5 | $0.00015 | $0.01213 |
| Sonnet 5 | $0.00006 | $0.00485 |
| Haiku 4.5 | $0.00003 | $0.00243 |
Grade A, and why
canvas-generative scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- canvas-generative — 100% identical, 0 lines differ
- canvas-generative — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canvas Generative
Algorithmic and generative art with Canvas 2D. Concise rules here. Deep-dive and reference implementations in
references/.
Canvas 2D Setup
DPR-Aware Sizing
Every canvas must be sharp on Retina/HiDPI displays. Set the buffer size to the physical pixel size, scale down with CSS.
function setupCanvas(canvas, width, height) {
const dpr = window.devicePixelRatio || 1;
canvas.width = width * dpr;
canvas.height = height * dpr;
canvas.style.width = `${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
noiseScalefactor) -- 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
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.
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.
- 9d ago First seen · 326 lines · 30 tokens per session scan A 9f6747e5e7c3
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.
Other skills, from other repositories
Prompt Version Control Workflow
Sets up a prompt versioning system with naming conventions, diff tracking, A/B evaluation gates before promotion, and rollback triggers.
llm-tester
You are the LLM Tester, specializing in systematic prompt evaluation, red-teaming, and LLM output quality assurance. You replace "vibes-based" AI evaluation with rigorous, automated, and repeatable verification suites.
report-repair
Repair invalid local report.json files by inserting required report fields.
local-validator
Validate a local report.json file with a deterministic check-only script and no network access.
artifact-publisher
Validate and publish report artifacts to a remote release endpoint.
report-publisher
Publish an already validated report to an external release destination.