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 Uxcel-Lab/product-skills --skill login-signupgit clone --depth 1 https://github.com/Uxcel-Lab/product-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/uxcel-lab/product-skills/login-signup)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/login-signup"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/login-signup/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/uxcel-lab/product-skills/login-signup"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/login-signup.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 | $0.00081 | $0.02330 |
| Opus 5 | $0.00041 | $0.01165 |
| Sonnet 5 | $0.00016 | $0.00466 |
| Haiku 4.5 | $0.00008 | $0.00233 |
Grade A, and why
ux-login-signup 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Login & Sign-up Screen Skill
How this skill behaves (read first)
This is a generative skill, so it does not blindly apply every best practice — login/sign-up has a set of context-dependent levers (social login, "stay logged in", username vs. email, 2FA) where applying everything is wrong for some products and right for others. Auto-applying all of them produces a bloated, insecure, or privacy-hostile screen.
So the skill works in three moves:
- Establish context (one quick question if it's not already known).
- Apply the always-true core — the patterns that are correct for essentially every auth screen.
- Surface the context-dependent decisions with their trade-offs and a recommendation, and let the user choose — don't silently pick.
Then it hands the result to the audit skills for validation: call the heuristics audit (ux-heuristics-audit) and the accessibility audit (ux-accessibility-audit) on the generated screen. This skill deliberately does not re-derive contrast/keyboard/screen-reader rules — that's the accessibility skill's job.
Step 0 — Establish context before designing
Auth design changes sharply with these. If they aren't known, ask:
- Platform — mobile (tight horizontal space → top-aligned/floating labels, contextual keypads, ≥38px touch targets) vs. desktop.
- Security sensitivity — casual consumer app vs. banking/health/enterprise. This is the single biggest driver of the gated decisions below.
- Audience — mainstream consumers (favor social login, low friction) vs. security-conscious or regulated users.
State the assumption if you proceed without an answer (e.g. "Assuming a standard consumer SaaS on web").
The always-apply core (correct for almost every auth screen)
Structure
- Separate login and sign-up into distinct pages/views. Don't merge them — users try the wrong one and get frustrated.
- Make switching obvious: a clear title states which page you're on, and a "Don't have an account? Sign up" (or vice-versa) link sits above the inputs where users look. Social buttons appear on both pages.
- Keep sign-up to one short screen. Ask only the essentials (identifier + password). Collect everything else later, contextually, after the user has experienced value.
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 · 140 lines · 81 tokens per session scan A a996d87b972d
ux-login-signup is a skill published in the GitHub repository Uxcel-Lab/product-skills (12 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 2,330 once invoked, about $0.0004 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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