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 icesixgod · Codex Skill · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-astra-blender-characters safe to install? View the security audit →
awesome-astra-blender-characters 从人物参考图到可编辑 Blender 角色的 AI Agent 工作流。 中文 · English 本仓库提供可安装的 技能，组织九视图参考、脸部与头发制作、局部修模、多视图验收和工程交付，重点是二次元人物与静态插画风格。技能不固定模型版本。 发布内容是技能指令和制作参考文档。仓库暂未提供人物模型、示例渲染、训练权重或一键三维重建程序；安装与检查脚本只服务于技能分发。 能力与流程 局部修模直接进入相关阶段；仅整理文档不生成图片或修改工程。范围假定须说明，用户未答复不能视为同意删减原图可见部件。每轮修改先比较问题视角及相邻视角的实际渲染，未改善时重新定位原因；稳定后再从明确保存的同一版本输出交付多视图。 静态插画质感与遮挡诊断 包含图片纹理 UV 验收、保留腮红的肤色调整、眼面／虹膜／镜片分层检查，以及支撑面、内层与细丝的隔离诊断。执行可靠性 补充原生曲线与着色模式输入检查、局部修改的状态保留和实时视口异常恢复。 全身、服装、绑定、动画和打印有按需扩展指导，当前公开包尚未提供这些用途的端到端验证案例。 环境与依赖 没有图像生成能力时，可提供已经检查合格的九视图，或明确调整本次视图范围。安装技能不会自动安装 Blender、图像模型或第三方创作软件。 安装 下载或克隆本仓库后，在仓库根目录运行： bash python3 scripts/in
| Stars | 91 |
| Forks | 9 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.8466035085555/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-10 |
| Created | 2026-09-08 |
| Platforms | codex, python |
| Est. Tokens | ~4k |
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awesome-astra-blender-characters is An agent skill for Blender character creation and repair, with nine-view references, face and hair workflows, and visual validation.. It is categorized as a Codex Skill with 91 GitHub stars.
awesome-astra-blender-characters is primarily written in Python. It covers topics such as 3d-modeling, agent-skills, anime.
You can find installation instructions and usage details in the awesome-astra-blender-characters GitHub repository at github.com/icesixgod/awesome-astra-blender-characters. The project has 91 stars and 9 forks, indicating an active community.
awesome-astra-blender-characters is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-astra-blender-characters on Agent Skills Hub include ai-skills, skillport, ask-user-questions-mcp. 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: