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 ChenShuo2004/cs-skills --skill cs-chatcut-video-blueprintgit clone --depth 1 https://github.com/ChenShuo2004/cs-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/chenshuo2004/cs-skills/cs-chatcut-video-blueprint)<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-chatcut-video-blueprint"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-chatcut-video-blueprint/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/chenshuo2004/cs-skills/cs-chatcut-video-blueprint"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-chatcut-video-blueprint.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 | $0.00074 | $0.01323 |
| Opus 5.5 | $0.00030 | $0.00529 |
| Sonnet 5.5 | $0.00015 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
cs-chatcut-video-blueprint 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 18d 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
CS 自媒体剧本与制作蓝图
把真实素材变成可录制、可拍摄、可逐图准备的视频文本包,适用于观点口播、项目复盘、教程演示和情景短剧。可交接到 ChatCut 或其他匹配工具;名称保留原调用方式,文本交付不依赖 ChatCut。默认简体中文。
按请求决定交付深度
- 只选题:去重、比较受众相关性、观点张力、事实支撑和制作可行性,推荐一个主题。只有比较确有帮助时才打分。
- 只写口播:直接给可复制的完整稿件,不展开分镜。
- 要自媒体剧本包、剧本附分镜分图提示词或完整蓝图:连续完成正文、视觉约定、逐镜分镜、逐图图片提示词、按需的动作提示词与素材缺口;不要求用户额外说“端到端”。
- 已有稿件只补分镜或提示词:保留原稿、已定风格和台词,只交付点名范围;不重开选题、不重写正文。
- 用户要求“先给我选题”“稿子确认后再继续”时,按其明确阶段停下。已有确认继续有效;需要改变已锁定事实、立场或稿件时才说明变更。
从素材推断平台、时长、受众与语气,并简短标注会影响结果的假设。事实来源、说话人真实经历不能猜;缺少关键依据时给出能完成的稿件部分和最小缺口。不要把完整 brief 当作问卷。
自媒体故事开发
需要从素材写剧本、修正中段推进或去模板感时,读取 自媒体剧本方法。先确定内容承诺和可成立的结尾,再按内容形式检查因果或论证;不为填表硬加对手、倒计时、反转或金句。
真实项目的个人 IP 口播可先用 $cs-personal-ip-script 成稿,再在同一请求内接着完成这里的分镜与提示词。已有正文直接复用;只要口播时不增加文本制作包。
口播质量
- 每条只针对一个具体人群、场景和问题。开头用可识别的事实、损失、动作或矛盾,让观众立刻知道与自己的关系。
- 每段推进新事实、例子、解释或行动;不重复换说法。结构服从内容,不强塞固定的 Hook、冲突、结论模板。
- 产品能力、数字与经历回链到素材;用户观点与外部事实分开。未证实的卖点不能写成肯定断言。
- 纯口播与停顿、重音等表达标注分开,避免被配音读进台词。只在用户需要时提供多个 Hook 或标注稿。
- 以可朗读字数和明确语速假设估算时长,标为“预计”。最终音频才决定真实时码;不能声称仅凭字数通过了实听或精确时长验收。
- CTA 最多一个动作;内容不需要引导时可以没有 CTA。明确无 BGM 时,不增加音乐设计。
制作蓝图
需要分镜、分图或生成提示词时读取 分镜与逐图提示词,按镜号关联台词、图片与动作;每条生成提示词完整可复制,不以“同上”省略。需要保存完整剧本包时使用 文本包模板。
完整制作蓝图另读 蓝图格式。每个素材和 Motion Graphics 都要对应具体口播与观看任务;透明叠加、全屏不透明画面要区分,保护人物、产品 UI 与字幕。实拍、录屏和证据截图写获取指令,不能用生成图伪造真实项目证据。
计划时码不能冒充最终音频对齐。已有最终音频且用户需要字幕或动画真实时间时,可交接给 $cs-narration-phrase-timeline。
数字人产品视频的交接读取 产品包摘要,保留事实、禁用断言、证据编号、规格、声音来源和 sample-then-batch 规则。只有满足产品包范围的制作才交给 $cs-digital-human-product-video-pipeline;普通 ChatCut 剪辑不误送到这个专用管线。
执行边界与完成
- 不创建、选择或修改 ChatCut 项目。
- 不上传、导入、导出、渲染或把素材放入时间线。
- 不调用 ChatCut MCP、不编写 Motion Graphics JSX、不虚构 asset ID。
- 可以在用户指定的本地目录保存脚本、蓝图和产品包草稿。用户要求制作视频时先交付本阶段产物,再使用实际可用且匹配的制作工具。
- 交付前核对事实来源、口播推进、镜号与图号引用、关键内容的画面/声音承载、跨镜连续性和计划时长覆盖。提示词条目不代表已生成媒体;静态检查不代表画面质量或传播效果。
- 先给用户所需成品,仅附影响继续制作的待确认项。
What ships with it
6 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.
- 18d ago First seen · 55 lines · 74 tokens per session scan A dad48e9fefc8
cs-chatcut-video-blueprint is a skill published in the GitHub repository ChenShuo2004/cs-skills (204 stars, last pushed 18d ago), licensed MIT. It adds 74 tokens to every session and 1,323 once invoked, about $0.0003 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 2026-10-07.
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