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 binggandata · Codex Skill · ★ 564
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bggg-skills safe to install? View the security audit →
bggg-skills 中文 | English 是 BGGG 开源的 Codex Skills 集合仓库。 每个 skill 都是一个独立目录，可以复制或软链接到 使用。后续更多 BGGG 的可复用工作流、工具封装和创作能力都会逐步放到这个仓库里。 Created by @binggandata · 小红书 · X / Twitter · 微信：binggandata2 当前 Skills ：把一张或多张图片转成可编辑的分层 PSD，支持 Codex/imagegen 辅助拆图、全画布 PNG 图层导出、颜色拆层、白底转透明，以及纯 Python PSD 写入。 ：把图片、截图、HTML 或 SVG 设计稿转成可编辑 PPTX，支持 Codex/imagegen 辅助组件重建、文本框还原、原生形状重建，以及 HTML/SVG 解析到 PPTX。 ：Skill 进化器，通过对比、分析和吸收其他 skill 的优势，帮助目标 skill 渐进式升级。 ：复用本地真实 Chrome 登录态做 TikTok 只读调研，输出 JSON/CSV/Markdown 和截图证据。 ：用 下载 TikTok 单视频或博主可见作品，单视频失败时用 tikwm 兜底。 ：把 TikTok/UGC/本地视频拆成 Codex 可读的 metadata、transcript、scene、keyframe、contact sheet 和 timeline。 ：用 JSON edit plan 和 FFmpeg 把 AI 视频、本地素材或口播素材剪成 9:16 TikTok 成片。 ：基于已有 CapCut 模板草稿生成新草稿，提取模板样式，验证草稿结构，并检查 AI 视频痕迹。 ：亚马逊 Sif 关键词三表处理、分层评级、竞品弱点/广告缺口分析，并输出 PD 主攻词单与 Word 报告。 ：同一 ASIN 有历史记录时，对比 1-7 天窗口内两期 PD 词单，输出词库更新与投放建议报告。 [we
| Stars | 564 |
| Forks | 90 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 59.9250110873841/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-13 |
| Created | 2026-05-02 |
| Platforms | codex, python |
| Est. Tokens | ~14k |
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bggg-skills is Open-source Codex skills from BGGG. It is categorized as a Codex Skill with 564 GitHub stars.
bggg-skills is primarily written in Python.
You can find installation instructions and usage details in the bggg-skills GitHub repository at github.com/binggandata/bggg-skills. The project has 564 stars and 90 forks, indicating an active community.
bggg-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to bggg-skills on Agent Skills Hub include pi-skills, agent-skill-creator, learning-opportunities. 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: