Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 gooseworks-ai/goose-skills --skill render-model-comparison-gridgit clone --depth 1 https://github.com/gooseworks-ai/goose-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/gooseworks-ai/goose-skills/render-model-comparison-grid)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-model-comparison-grid"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-model-comparison-grid/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/gooseworks-ai/goose-skills/render-model-comparison-grid"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-model-comparison-grid.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 | $0.00158 | $0.00891 |
| Opus 5 | $0.00079 | $0.00445 |
| Sonnet 5 | $0.00032 | $0.00178 |
| Haiku 4.5 | $0.00016 | $0.00089 |
Grade A, and why
render-model-comparison-grid 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 11d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-model-comparison-grid
Render the 'model comparison grid' format from a config. The signature of this format is a
"Same prompt. N models." gauntlet: a dark stage where, per beat, a PROMPT eyebrow +
the (condensed) prompt fades in centered in monospace and holds readable ~0.8s, then
docks to a small top strip while a grid of 2-4 labeled panels staggers in (0.15s apart)
and holds for side-by-side comparison. A persistent model/variant label sits under each
panel; column order is identical on every beat. Ends on a minimal end card (headline +
column names only — no meta-stats line).
The grid is media-agnostic per cell: any cell is a static image or a muted video clip (i2v outputs, screen recordings), mixable within one beat. Video cells loop during the hold and are frame-seeked deterministically (the renderer awaits each seek), so the render never depends on wall-clock playback timing.
The renderer itself is FREE/deterministic (Playwright frame-step + FFmpeg). The paid inputs
are separate capabilities: the cell images come from create-image-fal, the cell
clips from create-video-fal, and the music bed from create-music-elevenlabs.
Prompt text and labels are real DOM — never AI-rendered.
Default shape: 5 beats × 4.5s + 2.5s end card = 25.0s @ 1280×720/30fps, all configurable
from one config.json.
Run
build_composition.py --config config.json --output hyperframe.html ; render_seekable_hyperframe.py hyperframe.html master-silent.mp4 --fps 30 --width 1280 --height 720 — dark stage, staggered grid, deterministic, $0. The config schema is documented at the top of scripts/build_composition.py; scripts/config.example.json IS the shipped worked example (re-point the cell paths at your own media).
build_composition.py validates every cell path and the column count (2-4), infers each
cell's media type from its extension (.png/.jpg/.jpeg/.webp → image; .mp4/.mov/.webm/.m4v
→ muted video), and emits a self-contained HTML that exposes window.mediaReady() +
window.renderAt(t). render_seekable_hyperframe.py awaits both, so <video> cells seek
to the right frame before each screenshot — never a frozen first frame.
What ships with it
5 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.
- 11d ago First seen · 48 lines · 158 tokens per session scan A e04d6e86c3c7
render-model-comparison-grid is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 158 tokens to every session and 891 once invoked, about $0.0008 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.
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