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/gsap/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/gsap)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/gsap"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/gsap.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.01489 |
| Opus 5 | $0.00010 | $0.00745 |
| Sonnet 5 | $0.00004 | $0.00298 |
| Haiku 4.5 | $0.00002 | $0.00149 |
Grade A, and why
gsap 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GSAP — Animation Engine
When to use GSAP
| Criteria | CSS Transitions | Framer Motion | GSAP |
|---|---|---|---|
| Hover / simple toggle | Yes | Yes | Overkill |
| Sequenced timeline | No | Limited | Yes |
| Scroll-driven | scroll-timeline | Limited | ScrollTrigger |
| Complex stagger | No | Basic | Distribution |
| Mobile perf (60fps) | Good | Average | Excellent |
| Text splitting | No | No | SplitText |
| SVG morph / draw | No | No | MorphSVG |
| Bundle size concern | 0kb | ~30kb | ~25kb + plugins |
Rule: if the animation needs timeline, scroll-link, or distributed stagger, use GSAP. Otherwise CSS first.
Setup
// Always register plugins at the top level
import gsap from "gsap";
import { ScrollTrigger } from "gsap/ScrollTrigger";
import { SplitText } from "gsap/SplitText";
gsap.registerPlugin(ScrollTrigger, SplitText);
React: use useGSAP() from the @gsap/react package instead of useEffect + manual cleanup.
import { useGSAP } from "@gsap/react";
useGSAP(() => {
gsap.to(".box", { x: 200 });
}, { scope: containerRef }); // auto-cleanup, auto-revert
Core Patterns
defaults{} to avoid repetition
const tl = gsap.timeline({
defaults: { duration: 0.8, ease: "power2.out" },
});
tl.to(".a", { y: -20 })
.to(".b", { y: -20 }, "<0.1")
.to(".c", { y: -20 }, "<0.1");
fromTo for full control
gsap.fromTo(".card", { y: 40, opacity: 0 }, { y: 0, opacity: 1, stagger: 0.15 });
Stagger with distribution
gsap.to(".grid-item", {
scale: 0,
stagger: {
each: 0.05,
from: "center", // "start" | "end" | "center" | "edges" | "random" | index
grid: "auto", // auto-detects the grid
axis: "x", // "x" | "y" | null (both)
},
});
What ships with it
4 files 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.
- 8d ago First seen · 189 lines · 20 tokens per session scan A 08df3ec0c89b
gsap is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,216 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 1,489 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 2026-08-30.
Other skills, from other repositories
image-exporter
Export local SVG and PNG image assets into a release bundle.
native-transparent-imagegen
Generate new raster assets that must contain native pixel transparency, then verify the untouched PNG or WebP before delivery. Use for transparent stickers, sprites, character cutouts, product assets, and furry or fine-edged subjects where background removal would be unacceptable. Do not use to remove a background…
recurring-character-diary-comic
Create, audit, and repair short page-native diary comics around an existing authorized recurring character, with story-directed page rhythm, exact dialogue, directional-surface proof, and original-resolution visual QA. Use when extending an established recurring-character series from an anecdote, conversation, dream…
single-path-process-diorama
A method for turning a three-to-five-step linear process into one finished miniature theatrical scene rather than a grid of separate panels. It uses a continuous path and visible actions to show the order of the process.
logo-semantic-fusion
Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work on a logo, symbol, app icon, monogram…
Prompt Version Control Workflow
Sets up a prompt versioning system with naming conventions, diff tracking, A/B evaluation gates before promotion, and rollback triggers.