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 sergebulaev · Claude Skill · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tiktok-skills safe to install? View the security audit →
TikTok Marketing Skills for Claude Code and Codex Part of the linkedin-skills family (400+ stars). Same voice engine and approve-before-publish flow, now for TikTok. Also available for Instagram · X · YouTube · Threads · Facebook. 8 skills that turn Claude Code and Codex into your TikTok content team. They
| Stars | 61 |
| Forks | 10 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 70.3992978371733/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-03 |
| Created | 2026-07-02 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~18k |
Looking for a tiktok-skills alternative? If you're comparing tiktok-skills 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.
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tiktok-skills is TikTok marketing skills for Claude Code and Codex: 3-second video hooks, captions, trend mapping, and a weekly plan. Draft, humanize, and publish via Publora. MIT. Content engineering by Creative Cont. It is categorized as a Claude Skill with 61 GitHub stars.
tiktok-skills is primarily written in Python. It covers topics such as agent-skill, ai-agents, ai-marketing.
You can find installation instructions and usage details in the tiktok-skills GitHub repository at github.com/sergebulaev/tiktok-skills. The project has 61 stars and 10 forks, indicating an active community.
tiktok-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tiktok-skills on Agent Skills Hub include social-media-caption-generator-claude, founder-marketing-skills, wonda. 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: