Cinematic-Landing-Kit: Memory-driven design system for AI coding agents using context-and-memory package that instructs any AI coding

by alwkala · Agent Tool · ★ 8

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aiai-codingaiskillsantigravityapple-style-uicinematicclaude-codedesign-systemdesign-systemsgenerator-template

Quick Facts

Stars8
Forks1
LanguageHTML
CategoryAgent Tool
LicenseMIT
Quality Score62/100
Open Issues1
Last Updated2026-07-28
Created2026-07-01
Platformsbrowser, claude-code
Est. Tokens~18k

About Cinematic-Landing-Kit

Memory-driven design system for AI coding agents using context-and-memory package that instructs **any AI coding agent** to produce scroll-driven, cinematic product landing pages — the kind that feel like a luxury TV ad, not a website. One HTML file, CDN libraries, zero build step.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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