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 kensaurus/cursor-kenji --skill enhance-web-landinggit clone --depth 1 https://github.com/kensaurus/cursor-kenjiWrote 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/kensaurus/cursor-kenji/enhance-web-landing)<a href="https://agentmods.dev/skills/kensaurus/cursor-kenji/enhance-web-landing"><img src="https://agentmods.dev/badge/skills/kensaurus/cursor-kenji/enhance-web-landing/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/kensaurus/cursor-kenji/enhance-web-landing"><img src="https://agentmods.dev/badge/skills/kensaurus/cursor-kenji/enhance-web-landing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 298 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Excessive Agency · line 77 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 125 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00061 | $0.10679 |
| Opus 5 | $0.00030 | $0.05339 |
| Sonnet 5 | $0.00012 | $0.02136 |
| Haiku 4.5 | $0.00006 | $0.01068 |
Grade A, and why
enhance-web-landing 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 6d 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
1 near-identical copy found in the catalogue:
- taste-skill — 88% identical, 275 lines differ
How it starts
The opening of the file, as written. The whole thing — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Surface router:
/uiux. You are here:enhance-web-landing. Native iOS/Android (SwiftUI / Compose, no web layer) is out of scope — use Apple HIG / Material directly.
enhance-web-landing — Anti-Slop Landing Pages
Degree of freedom: MIXED. Design-read and dials [HIGH freedom]; dependency check, contrast, and preflight hard rules [LOW freedom — run exactly].
Landing pages, portfolios, and redesigns. Not dashboards, not data tables, not multi-step product UI. Every rule below is contextual. None of it fires automatically. First read the brief, then pull only what fits.
How to reason
- Read — page kind, vibe words, audience, existing brand, quiet constraints
- Dial — VARIANCE / MOTION / DENSITY from the read (not the LLM default)
- System — official package if the brief is a system; one system per project
- Preflight — hero-in-viewport, contrast, no slop defaults, real images
Worked example
Read: B2B SaaS landing for technical buyers; user said "Linear-style"; no brand serif. Dial: 5 / 3 / 3 — clean, low motion, airy. System: Tailwind v4 + Geist; not Inter + purple mesh; shadcn only if already owned. Preflight: split hero (not centered), one accent, real product shot, CTA unwrapped.
Self-critique before reporting
- Design read stated — one line before code; no silent default aesthetic
- Anti-default — no Inter + purple gradient + three equal cards unless the read asks
- Hard rules — hero fits first viewport; button contrast AA; one CTA intent
- Right owner — dashboards/product UI →
enhance-web-ui; existing-app polish →enhance-web-redesign
0. BRIEF INFERENCE (Read the Room Before Anything Else) [HIGH freedom]
Before touching code or tweaking dials, infer what the user actually wants. Most LLM design output is bad because the model jumps to a default aesthetic instead of reading the room.
0.A Read these signals first
- Page kind - landing (SaaS / consumer / agency / event), portfolio (dev / designer / creative studio), redesign (preserve vs overhaul), editorial / blog.
- Vibe words the user used - "minimalist", "calm", "Linear-style", "Awwwards", "brutalist", "premium consumer", "Apple-y", "playful", "serious B2B", "editorial", "agency-y", "glassy", "dark tech".
- Reference signals - URLs they linked, screenshots they pasted, products they named, brands they're competing with.
- Audience - B2B procurement panel vs. design-conscious consumer vs. recruiter scanning a portfolio. The audience picks the aesthetic, not your taste.
- Brand assets that already exist - logo, color, type, photography. For redesigns, these are starting material, not optional input (see Section 11).
- Quiet constraints - accessibility-first audiences, public-sector, regulated industries, trust-first commerce, kids' products. These constraints OVERRIDE aesthetic preference.
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.
- 6d ago First seen · 456 lines · 61 tokens per session scan A 7b04e504ff1a
enhance-web-landing is a skill published in the GitHub repository kensaurus/cursor-kenji (9 stars, last pushed 11d ago), licensed MIT. It adds 61 tokens to every session and 10,679 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 2026-09-03.
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