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 beatra-ai · Claude Skill · ★ 1
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is talking-avatar-video-skill safe to install? View the security audit →
Talking avatar video agent skill for Claude Code, Codex & OpenClaw: turn one portrait and a script or voice track into an AI presenter video for explainers, lessons, and product messages. 数字人口播视频 · 照片说话
| Stars | 1 |
| Forks | 0 |
| Language | Python |
| Category | Claude Skill |
| License | MIT-0 |
| Quality Score | 52.8591681145957/100 |
| Last Updated | 2026-09-30 |
| Created | 2026-09-16 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~13k |
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talking-avatar-video-skill is Talking avatar video agent skill for Claude Code, Codex & OpenClaw: turn one portrait and a script or voice track into an AI presenter video for explainers, lessons, and product messages. 数字人口播视频 · 照片. It is categorized as a Claude Skill with 1 GitHub stars.
talking-avatar-video-skill is primarily written in Python. It covers topics such as agent-skills, ai-avatar, ai-presenter.
You can find installation instructions and usage details in the talking-avatar-video-skill GitHub repository at github.com/beatra-ai/talking-avatar-video-skill. The project has 1 stars and 0 forks, indicating an active community.
talking-avatar-video-skill is released under the MIT-0 license, making it free to use and modify according to the license terms.
The top alternatives to talking-avatar-video-skill on Agent Skills Hub include semanticscholar-skill, agent-skills, skillnote. 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: