content-video-pipeline

content-video-pipeline is a skill for Claude Code, Codex from zj-unicom-ai/UniEmployee. It costs 49 tokens per session (1,357 once invoked), scanned A, original, MIT.

A production workflow for turning a topic, source material, or script into a finished video with narration, visuals, subtitles, motion, and quality checks.

In plain words
What is it for?
Use it to make educational, news, science, business, or personal videos from supplied material and verify the finished output.
Why use it?
It gives creators a repeatable way to organize research, fact checking, scripting, audio, scenes, and rendering without assuming one subject or toolchain.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to make educational, news, science, business, or personal videos from supplied material and verify the finished output.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zj-unicom-ai/uniemployee/content-video-pipeline
Install

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.

Any agent
npx skills add zj-unicom-ai/UniEmployee --skill content-video-pipeline
Clone the repo
git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for content-video-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/content-video-pipeline/github.svg)](https://agentmods.dev/skills/zj-unicom-ai/uniemployee/content-video-pipeline)
Your own site
<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.

agentmods 80×15 button for content-video-pipeline

Your own site · 80×15
<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>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,357 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 16d ago against content hash 591fded2f151, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

backend/skills/content-video-pipeline/SKILL.md · 64 lines

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。

Files

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.

Changes

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.

  1. 16d ago First seen · 64 lines · 49 tokens per session scan A 591fded2f151

Subscribe to this mod's changes

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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