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 surfmind-space/awesome-surfmind --skill landing-page-critiquegit clone --depth 1 https://github.com/surfmind-space/awesome-surfmindWrote 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/surfmind-space/awesome-surfmind/landing-page-critique)<a href="https://agentmods.dev/skills/surfmind-space/awesome-surfmind/landing-page-critique"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/landing-page-critique/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/surfmind-space/awesome-surfmind/landing-page-critique"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/landing-page-critique.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 | $0.00067 | $0.00685 |
| Opus 5 | $0.00034 | $0.00342 |
| Sonnet 5 | $0.00013 | $0.00137 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
landing-page-critique 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 12d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page Critique
Review the landing, pricing, signup, campaign, or product page the way a conversion-minded editor would — message clarity, audience fit, proof, objections, calls to action, and the friction that costs signups — with concrete fixes. Work only from what's visible on the page, in the user's selected text, or in tool results; don't critique invisible analytics like conversion rate unless the user provides them.
- Assess the headline, offer, audience clarity, proof, CTA, objections, visual hierarchy, friction, trust signals, and mobile/readability clues.
- Tie each critique to a visible page element or a missing one; separate quick wins from deeper positioning issues.
- Suggest concrete copy or structure rewrites, not generic marketing clichés (e.g. Before: "Get Started" → After: "Start your free 14-day trial — no card needed").
- Separate supported facts, tool-derived context, and your own assumptions or recommendations rather than blending them.
Return concise headings and include these sections when they fit: Overall diagnosis, What works, Friction points, Quick wins, Deeper tests, Suggested copy/structure. Preserve important names, numbers, links, dates, and currencies. Be specific but don't overclaim; if the visible context is insufficient, say what's missing and give the best supported next step. Respond in the same language as the user's request.
Example
Page (a freelancer-invoicing SaaS hero):
"The smarter way to manage your business." · Subhead: "Invoicing, payments, and reporting in one place." · CTA: "Sign up" · Below: a paragraph of feature copy, no pricing, no logos.
Critique:
Overall diagnosis: The page sells features but never names who it's for or what makes it "smarter" — a visitor can't tell in five seconds why to pick this over their current tool.
What works: The one-line scope ("invoicing, payments, reporting") is clear and concrete.
Friction points: Generic headline; no pricing or proof, so the visitor must guess at cost and trust; the CTA asks for commitment ("Sign up") before showing value.
Quick wins:
- Headline — Before: "The smarter way to manage your business." → After: "Get paid faster — invoicing built for freelancers."
- CTA — Before: "Sign up" → After: "Create your first invoice free."
Deeper tests: Add a proof strip (customer count or logos) above the fold and surface a starting price; test a 3-step "how it works" against the current feature paragraph.
No pricing, traffic, or audience data is on the page — confirm the target user before committing to the headline rewrite.
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.
- 12d ago First seen · 44 lines · 67 tokens per session scan A f518c6081dd9
landing-page-critique is a skill published in the GitHub repository surfmind-space/awesome-surfmind (5 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 685 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.
Other skills, from other repositories
cs-master
Expert computer scientist for algorithms, complexity analysis, and distributed systems.
uiux-master
UI/UX expert for design systems, accessibility, and avoiding generic AI-generated design patterns.
c-master
C programming expert for memory management, systems programming, and performance optimization.
data-science
ML engineering expert for feature engineering, model debugging, and production pipelines.
go-master
Go expert for goroutines, channels, microservices, and concurrent patterns.
network-devops
Network performance expert for latency optimization, packet analysis, and infrastructure tuning.