by LeeHueeng · Claude Skill · ★ 95
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/LeeHueeng/store-screenshots.git
Taken from the project's README on GitHub.
None of the 43 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 43 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
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.
| Stars | 95 |
| Forks | 7 |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 48/100 |
| Last Updated | 2026-08-23 |
| Created | 2026-08-21 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~15k |
store-screenshots Raw app screenshots in → store-ready marketing images out. An AI agent skill for Claude Code & OpenAI Codex that adds device frames, branded backgrounds, and benefit-driven copy — at exact App Store & Google Play sizes. English · 한국어 Making App Store and Google Play screenshots by hand — opening Figma, hunting for device mockups, double-checking pixel sizes — gets old fast.
These tools work well together with store-screenshots for enhanced workflows:
Looking for a store-screenshots alternative? If you're comparing store-screenshots with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Claude Code plugin that checks iOS and Android apps against Apple's App Review Guidelines and Google Play poli
Open-source ASO tool and keyword rank tracker for the Apple App Store and Google Play. Self-host with Docker o
Generate App Store & Google Play preview videos and screenshots from a simple TypeScript config. Built on Remo
Automate Multi-Locale asset creation for your iOS app.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 43 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: