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 zj-unicom-ai/UniEmployee --skill content-video-pipelinegit clone --depth 1 https://github.com/zj-unicom-ai/UniEmployeeWrote 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/zj-unicom-ai/uniemployee/content-video-pipeline)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/content-video-pipeline"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/content-video-pipeline/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/zj-unicom-ai/uniemployee/content-video-pipeline"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/content-video-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00049 | $0.01357 |
| Opus 5.5 | $0.00020 | $0.00543 |
| Sonnet 5.5 | $0.00010 | $0.00271 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
content-video-pipeline 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 16d 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.
What it actually says
跨领域内容视频流水线
帮助创作者把一个主题或一组素材,转化为有明确受众、叙事、声音和画面的完整视频。适用于资讯、教育、科普、历史、亲子、财经、文化、企业培训及个人表达等领域。它提供可移植的制作方法,不要求使用特定操作系统、数据源、声线、品牌或渲染框架。
核心分工:AI 和自动化工具降低执行门槛;创作者负责目标、选题、证据、解释、审美取舍与最终审核。不要把某个领域的内容结构直接套用到所有视频。
首次适配
开始前查看目标项目说明、现有工程和已确认模板。从已有信息中识别以下设置,不重复询问:
- 作品主题、受众、目标平台、画幅、语言和预期时长。
- 输入来源:用户提供的资料、MCP、RSS、API、数据库、采访、课程大纲或人工整理等。
- 该领域适用的来源标准、事实核验方式、归因要求和内容边界。
- 旁白或录音方式、已选声线、语言和输出格式。
- 视频框架、项目锁定的 CLI 版本、预览、检查和渲染方式。
- 品牌模板、授权素材、字幕安全区、转场、音效、片尾及固定归档位置。
沿用已经确认的环境与偏好。缺少关键配置时,先完成不依赖它的选题、结构或脚本草案,再询问最少的必要问题;不得虚构工具、凭据或素材授权。
制作流程
1. 确定创作目标和内容单位
明确观众是谁、作品要解决什么问题、希望观众理解或采取什么行动。根据领域选择适合的叙事单位,例如资讯事件、课程知识点、实验、人物、步骤或用户问题。内容数量、章节数和视频时长由主题与目标决定,不使用固定条数。
2. 整理素材并核实依据
从已配置入口收集内容,记录来源和时间信息。按事件、主题或其他合适单位合并重复素材,同时保留彼此相关但实质不同的内容。按照领域风险与规范核实来源:可优先检查原始材料、官方信息、研究或专家资料,并用可靠的二手来源补充背景。
区分已确认信息、来源方主张、推论和未知内容。高风险领域应采用相称的核验标准,清楚说明适用范围与不确定性。不要让 AI 生成内容取代必要的专业审阅。
3. 建立叙事与结构化脚本
先决定作品的主线、信息顺序和表达重点,再让 AI 协助提纲、改写或生成画面建议。对于事实型内容,将证据与解释区分开;对于教学或故事类内容,则按学习目标、情节或体验设计结构。
使用机器和编辑都可读的中间脚本驱动画面、字幕和音频。每个场景可包含:唯一 ID、叙事目的、标题、旁白、屏幕文字、画面描述或素材引用、预计时长、来源或创作说明。字段按领域增减。页面/场景数量由实际脚本生成,而非写死在模板中。
4. 制作声音并安排时间
根据作品需要选择配音、本人录音、环境声、音乐和静默。复用用户已选择的声线及本地默认参数,不将个人声线标识或密钥写入可分享文件。拆分旁白片段以便独立修改,核对专名、数字和专业词汇,并抽听成品。
使用实际音频时长安排场景切换,并为阅读、思考或情绪停顿留出空间。不同类型的内容节奏不同,不以统一的字数或语速作为标准。音效应支持场景和内容,不妨碍理解。
5. 匹配视觉语言和模板
让画面形式服务于内容气质与观众预期。优先延续用户认可的品牌模板;若是新项目,先确定画幅、视觉层级、字体、配色、图像来源、转场和片尾,再细化动效。避免把其他领域或既有系列的视觉设定机械套用过来。
标题、重点画面、解释文字应有清楚的阅读顺序。动效可以突出信息和引导注意力,但不应遮挡关键内容或无故延长节奏。对时间轴渲染,确保动画可由固定时间和本地素材确定性地呈现。
6. 检查、渲染和归档
依据作品目标检查内容准确性、来源归属、画面可读性、字幕安全区、声音、场景节奏、开头结尾、画幅、时长和音轨。检查关键画面和最终帧;若使用网页播放器预览,同时提供正确的封面海报,避免暂停状态出现黑色占位而被误认为视频首帧。
在项目固定目录中按日期、集数或主题归档简报、脚本、来源、素材、配音、工程、检查记录和最终渲染。保留可复用模板与确认版本,迭代时不要覆盖仍需比较的版本。只有在用户要求并授权时才向外部平台发布或上传。
交付
按用户要求交付视频、预览页、封面或工程文件,并说明视频规格和完成的关键检查。未渲染时清楚说明当前交付状态。保留该项目既有约定,不将此 Skill 中的示例当作固定品牌规则。
What ships with it
1 file 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.
- 16d ago First seen · 64 lines · 49 tokens per session scan A 591fded2f151
content-video-pipeline is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (362 stars, last pushed 8d ago), licensed MIT. It adds 49 tokens to every session and 1,357 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-30.
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