No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ZestfulPulse · Claude Skill · ★ 127
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ios-app-store-submit safe to install? View the security audit →
ios-app-store-submit 🇺🇸 English | 🇰🇷 한국어 From automating App Store submission to deciding whether an iOS app is actually ready to submit, and helping recover when review fails. preserves the existing Archive → Upload → Submit workflow while adding evidence-driven Readiness, Apple Review Guidelines, Privacy, and HIG/Design checks before submission. This is the evolution of ios-app-store-submit, not a new repository. The public release target is v2.0.0. v2 Architecture v2 preserves the original build, signing, upload, and submission workflow while adding evidence-driven intelligence before and after App Review. Current Status ✅
| Stars | 127 |
| Forks | 16 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 75.0446214048829/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-25 |
| Created | 2026-08-05 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
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ios-app-store-submit is Claude Code skill for automating iOS App Store submission.. It is categorized as a Claude Skill with 127 GitHub stars.
ios-app-store-submit is primarily written in Python. It covers topics such as ai-agent-skill, automation-devops, developer-tools.
You can find installation instructions and usage details in the ios-app-store-submit GitHub repository at github.com/ZestfulPulse/ios-app-store-submit. The project has 127 stars and 16 forks, indicating an active community.
ios-app-store-submit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ios-app-store-submit on Agent Skills Hub include Gito, claude-code-skills, CodeWhisper. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: