Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add binggandata/bggg-skills --skill tiktok-gemini-video-workflowgit clone --depth 1 https://github.com/binggandata/bggg-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/binggandata/bggg-skills/tiktok-gemini-video-workflow)<a href="https://agentmods.dev/skills/binggandata/bggg-skills/tiktok-gemini-video-workflow"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/tiktok-gemini-video-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/binggandata/bggg-skills/tiktok-gemini-video-workflow"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/tiktok-gemini-video-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00213 | $0.07522 |
| Opus 5 | $0.00106 | $0.03761 |
| Sonnet 5 | $0.00043 | $0.01504 |
| Haiku 4.5 | $0.00021 | $0.00752 |
Grade A, and why
tiktok-gemini-video-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikTok Gemini 视频工作流
目标
把“用户只提交产品图和爆款视频”转换为可恢复、可追踪的生产流水线。始终以飞书多维表格为任务状态源,以真实下载文件和回读结果作为完成证据。
必须组合使用的能力
- 涉及飞书 Base 时先使用
lark-base,通过lark-cli base +...读取真实表、字段和记录。 - 涉及生成九宫格时使用
imagegen;把每张输入图的角色写清楚。 - 涉及 Gemini 或 Flow 网页时使用
chrome:control-chrome,复用用户明确选择且已登录的 Chrome。 - 涉及单账号、多账号切换、额度轮询或账号日志时,读取 account-routing.md。
- 涉及定时轮询或额度恢复后续跑时,先查找产品提供的 automation 工具;不要用 shell cron 代替。
- 涉及可见星形水印时,优先使用已安装的
gemini-watermark-removerSkill;缺失时按 setup.md 取得许可后安装。
默认入口
- Gemini 专业视频入口:
https://gemini.google.com/videos - Google Flow:
https://labs.google/fx/tools/flow/ - Base 与 Flow 项目:以当前用户提供的链接为准;不要在 Skill 中内置任何项目、表、账号或记录 ID。
0. 环境预检
- 按 setup.md 检查 Node.js、官方
@larksuite/cli、飞书 user 授权、Chrome连接、ffmpeg/ffprobe和gemini-watermark-remover。 lark-cli未安装时,先说明安装内容并取得用户同意,再安装和配置;已经安装时不要重复操作。- 只有飞书只读访问、Chrome登录态、本地目录和后处理依赖都验证成功后,才开始处理任务。
开始前确认输入与输出
每次新会话或新批次启动时,先向用户一次性确认以下内容;这是启动硬门槛:
- 任务表:用户是否已经有用于本工作流的飞书多维表格。
- 已有:向用户索取 Base 链接、表/视图和处理范围。
- 没有:向用户确认新 Base 名称和数据表名称,然后按下方“初始化任务表”创建。
- 工作目录:下载素材、抽帧、转写、九宫格和尾帧等中间文件放在哪个目录。
- 成品目录:原始下载视频、消除水印版和最终确认版放在哪个目录。
- 浏览器与账号:使用哪个 Chrome 配置和哪个 Gemini/Flow 账号;为账号设置不含隐私凭据的别名。
- 单账号:确认账号别名、Chrome配置名和允许入口。
- 多账号:再确认启用账号、优先级、是否允许自动切换,以及是否要求同一任务保持同一账号;按 account-routing.md 建立账号登记。
- 交付方式:生成后立即上传飞书进入
待验收,还是先只保存本地、等用户统一确认后再上传。 - 通知方式:只更新 Base,还是在状态变化时发送飞书消息;发送消息需用户提供可解析的个人或群聊目标并授权使用
lark-im。 - 调度方式:本次手动运行,还是设置定时轮询;不设置时绝不创建后台任务。
- 提交表单版本:使用只收集产品图、爆款视频和可选产品名的基础版,还是增加目标国家/语言、产品卖点、禁止内容、旁白/音乐偏好等可选题。没有明确选择时先问,不替用户扩展表单。
用户已在当前会话明确提供时直接复用,不要重复询问;不要跨会话默认沿用旧目录或旧浏览器配置。未确认工作目录和成品目录前不得开始下载、生成或提交额度任务。
用户选择手动运行时,只处理本次确认范围内的任务;批次结束或遇到等待条件后,写回状态、额度日志和下一步并退出,不创建定时任务、后台常驻进程或自动续跑。
确认后为每条记录使用独立工作子目录,建议为<工作目录>/<record_id>/;成品统一进入成品目录。不要把成品散落在 Downloads 或临时目录。浏览器下载完成后立即移动并按产品名-视频序号-入口-处理状态-画幅-时长命名。
1. 初始化任务表
用户已有 Base
- 使用用户提供的 URL 执行
lark-cli base +url-resolve --as user,取得真实base_token。 - 读取目标表、字段、视图和表单;不要把 Wiki token 当作 Base token。
- 对照 base-contract.md 做结构检查。
- 字段或状态选项缺失时列出差异并取得用户授权后补齐;本 Skill 固定最多两段,不创建第3、4段字段。
- 结构检查完成后再询问工作目录、成品目录、浏览器和交付方式。
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 256 lines · 213 tokens per session scan A edcc158aadfa
tiktok-gemini-video-workflow is a skill published in the GitHub repository binggandata/bggg-skills (594 stars, last pushed 1mo ago), licensed MIT. It adds 213 tokens to every session and 7,522 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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