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 juicesharp/rpiv-mono --skill frontend-designgit clone --depth 1 https://github.com/juicesharp/rpiv-monoWrote 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/juicesharp/rpiv-mono/frontend-design)<a href="https://agentmods.dev/skills/juicesharp/rpiv-mono/frontend-design"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/frontend-design/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/juicesharp/rpiv-mono/frontend-design"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/frontend-design.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 | $0.00122 | $0.04323 |
| Opus 5 | $0.00061 | $0.02161 |
| Sonnet 5 | $0.00024 | $0.00865 |
| Haiku 4.5 | $0.00012 | $0.00432 |
Grade A, and why
frontend-design 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 — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Design
Frontend code without aesthetic intent reads as AI slop — Inter, SaaS blue, three centered cards. This skill forces a deliberate aesthetic before a line of code. Scan what exists. Ask only what isn't settled. Synthesize a brief that primes every subsequent turn.
The brief is the product. Boldness is the standard. Half-commitments produce the slop the skill exists to prevent.
Two invocation modes:
- Full checkpoint (default): scan → 7-dimension interview → inject guidelines
- Headless (
--headless): scan → inject findings as guidelines → stop (no interview)
Input
$ARGUMENTS — optional --headless flag for scan-only mode; otherwise full aesthetic checkpoint. Inline design-intent phrasing (e.g., "editorial dark with copper accents") and referenced design files (DESIGN.md, style guides, brand decks) are also read.
Flow
- Input → 2. Style discovery → 3. Aesthetic checkpoint → 4. Guideline synthesis
Steps
Step 1: Input Handling
-
No argument provided — full checkpoint mode:
I'll guide your frontend design direction. Provide one of: `/skill:frontend-design` — full aesthetic checkpoint (scan + interview + guidelines) `/skill:frontend-design --headless` — scan-only: inject style findings without interviewThen wait for input.
-
The input contains
--headless— headless mode:- Set mode to
headless. Proceed to Step 2. After Step 2, skip Step 3 and go directly to Step 4.
- Set mode to
-
Otherwise — full checkpoint mode:
- Set mode to
full. Proceed to Step 2.
- Set mode to
-
Extract design intent from the input itself — both files and inline phrasing.
- Read referenced files fully (DESIGN.md, style guides, brand decks, tickets, named paths). Each dimension the file commits to (tone, color, type, motion, spatial, backgrounds, differentiation) counts as user-settled.
- Parse inline aesthetic commitments: phrases like "editorial dark with copper accents", "brutalist serif on cream", "1985 terminal aesthetic". Record each named dimension as user-settled.
- Do not count vague adjectives. "Modern", "clean", "fresh", "professional", "polished", "minimal-ish" are non-commitments — they do not settle any dimension. The user must name a specific direction for it to count.
- External references (URLs, Figma links, screenshot paths) cannot be fetched from inside this skill. If the user supplies one without an inline excerpt, ask them to paste the relevant tokens/text — do not proceed with a guess.
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 · 280 lines · 122 tokens per session scan A 64797895a267
frontend-design is a skill published in the GitHub repository juicesharp/rpiv-mono (779 stars, last pushed today), licensed MIT. It adds 122 tokens to every session and 4,323 once invoked, about $0.0006 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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