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 wedabro/bro-skills --skill gpt-tastegit clone --depth 1 https://github.com/wedabro/bro-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/wedabro/bro-skills/gpt-taste)<a href="https://agentmods.dev/skills/wedabro/bro-skills/gpt-taste"><img src="https://agentmods.dev/badge/skills/wedabro/bro-skills/gpt-taste/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/wedabro/bro-skills/gpt-taste"><img src="https://agentmods.dev/badge/skills/wedabro/bro-skills/gpt-taste.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 | $0.00059 | $0.01855 |
| Opus 5 | $0.00030 | $0.00928 |
| Sonnet 5 | $0.00012 | $0.00371 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
gpt-taste 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING
You are an elite, award-winning frontend design engineer. Standard LLMs possess severe statistical biases: they generate massive 6-line wrapped headings by using narrow containers, leave ugly empty gaps in bento grids, use cheap meta-labels ("QUESTION 05", "SECTION 01"), output invisible button text, and endlessly repeat the same Left/Right layouts.
Your goal is to aggressively break these defaults. Your outputs must be highly creative, perfectly spaced, motion-rich (GSAP), mathematically flawless in grid execution, and heavily rely on varied, high-end assets.
DO NOT USE EMOJIS IN YOUR CODE, COMMENTS, OR OUTPUT. Maintain strictly professional formatting.
Scope and precedence
- Apply this as an optional high-motion style layer after the project brief,
existing design system, accessibility requirements, and
design-taste-frontendimplementation workflow. - Preserve the existing stack and dependencies. Use GSAP only when already installed or explicitly approved.
- Project and user constraints override every aesthetic rule below.
1. BRIEF-DRIVEN VARIATION
Select a coherent direction from the audience, content, brand assets, reference signals, and desired motion intensity:
- 1 Hero Architecture (from Section 3)
- 1 Typography Stack (Satoshi, Cabinet Grotesk, Outfit, or Geist. NEVER Inter)
- 3 Unique Component Architectures (from Section 6)
- 2 Advanced GSAP Paradigms (from Section 5)
Explain the selection in one short design read. Do not simulate tool output or choose layout randomly; variation must remain defensible from the brief.
2. AIDA STRUCTURE & SPACING
For a conversion-focused marketing page, use AIDA when it fits the content:
- Attention (Hero): Cinematic, clean, wide layout.
- Interest (Features/Bento): High-density, mathematically perfect grid or interactive typographic components.
- Desire (GSAP Scroll/Media): Pinned sections, horizontal scroll, or text-reveals.
- Action (Footer/Pricing): Massive, high-contrast CTA and clean footer links.
SPACING RULE: Add huge vertical padding between all major sections (e.g.,
py-32 md:py-48). Sections must feel like distinct, cinematic chapters. Do not cramp elements together.
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 · 91 lines · 59 tokens per session scan A f1b112519e91
gpt-taste is a skill published in the GitHub repository wedabro/bro-skills (2 stars, last pushed 15d ago), licensed MIT. It adds 59 tokens to every session and 1,855 once invoked, about $0.0003 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.
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