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 AgenticAIPlan/AgenticAISkills --skill ip-drama-video-sopgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/ip-drama-video-sop)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/ip-drama-video-sop"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ip-drama-video-sop/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/agenticaiplan/agenticaiskills/ip-drama-video-sop"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ip-drama-video-sop.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 | $0.00079 | $0.01718 |
| Opus 5 | $0.00039 | $0.00859 |
| Sonnet 5 | $0.00016 | $0.00344 |
| Haiku 4.5 | $0.00008 | $0.00172 |
Grade A, and why
ip-drama-video-sop 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- founder-requirements-clarification — 88% identical, 425 lines differ
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IP小说改编短剧全流程 SOP
Skill 定位
一站式覆盖"小说→剧本→分镜→视频"全链路，用户可从任意阶段进入：
| 用户已有 | 从哪步开始 |
|---|---|
| 小说/IP原文 | Step 1 拆书分析 |
| 剧本策划案 | Step 3 剧本正文 |
| 剧本正文 | Step 4 分镜头脚本 |
| 分镜头脚本 | Step 5 角色形象设定 |
全流程概览（逐步确认）
Step 1：拆书分析 ──────── ✋ 确认
Step 2：剧本策划 ──────── ✋ 确认
Step 3：剧本正文 ──────── ✋ 逐集确认
Step 4：分镜头脚本 ────── ✋ 确认
Step 5：角色形象设定 ──── ✋ 选择确认
Step 6：分镜图片生成 ──── ✋ 逐张确认
Step 7：AI短视频生成 ──── ✋ 预览确认
⚠️ 每一步 AI 产出都是初稿，须用户确认后才推进。用户可修改、调整或推翻重来。若只需某一步的产出，不应自动拉向后续步骤。
各步骤简要说明
Step 1：拆书分析（有小说时触发）
通读小说 → 整体分析（梗概/卖点/情感引擎/爽点/人物标记）→ 框架分析（人物关系/关键情节/弧光/主题）→ 确定集数 → 人物设定
Step 2：剧本策划
剧本名称 → 集数 → 类型 → 梗概 → 人物小传 → 集纲
Step 3：剧本正文（需用户要求）
逐集创作，格式规范（△/括号/OS/VO/闪回等）详见 references/ip-drama-video-sop.md
Step 4：分镜头脚本（有剧本时触发）
将剧本拆解为分镜表格：镜号/场景/景别/镜头画面/镜头运动/时长/台词/配音
Step 5：角色形象设定（需用户要求）
生成3张角色概念图供选择风格，确定角色形象
工具依赖：文生图模型/API（如千帆 irag、DALL-E 等），由用户在所属平台自行调用或通过 Agent 集成的生图能力执行
Step 6：分镜图片生成（需确认角色形象后）
依据角色形象和分镜脚本逐镜生成画面，保持角色一致性
工具依赖：同 Step 5 文生图能力，需支持参考图（reference image）功能以保持角色一致性
Step 7：AI短视频生成（需确认分镜图片后）
TTS配音 → 分镜视频合成 → 配乐 → 最终串联导出
工具依赖：
- TTS 配音：文本转语音服务（如千帆 TTS、Azure TTS 等）
- 视频合成：图片+语音合成视频片段（如千帆 musesteamer、FFmpeg 等）
- 配乐：免版权音乐库或 AI 音乐生成
- 最终串联：视频剪辑工具（如 FFmpeg）
⚠️ Step 5-7 依赖外部 AI 工具和 API。Agent 可生成 Prompt、构建调用参数并指导操作流程，但实际生图/配音/视频合成需用户在对应平台执行或通过已集成的 API 调用完成。若用户环境不具备相关工具，Agent 应提示用户确认工具可用性后再进入这些步骤。
关键规则
- 逐步确认：每步产出须用户确认才推进，不可跳步
- 人物弧光是改编基石：若原著弧光扁平，剧本需重构
- 爽点与节奏：密集铺排爽点，每集结尾设钩子
- 次要人物精简：短剧人物关系极简
- 台词时长匹配：1秒镜头≈4-6字台词
- 角色一致性：AI生图时同一角色在不同分镜中形象统一
- 集数参考：传统平台短剧60-80集；轻量级AI视频10-20集
何时使用
前提条件：用户已有小说/IP原文，或已有剧本正文，需要进行改编/分镜/视频生成。
适用场景：
- "我有一部小说，想改编成短剧"
- "帮我把这本IP小说做改编拆书分析"
- "帮我把这段剧本转成分镜头脚本"
- "我已有短剧剧本，想AI生成短视频"
- "帮我把小说改编的剧本做成视频"
不适用场景（不应触发）：
- 无原著/剧本的纯创作请求
- 通用策划/视频制作咨询
- 纯拍摄/剪辑技术问题
使用示例
示例 1：从小说到视频（全流程）
用户：我有一个小说，想改编成短视频。
助手：
- Step 1 → 输出拆书分析 → 询问："是否满意？"
- 确认后 Step 2 → 输出策划案 → 询问："策划案是否满意？"
- 确认后 Step 3 → 逐集创作剧本 → 每集确认
- 确认后 Step 4 → 输出分镜头脚本 → 询问："分镜表是否满意？"
- 确认后 Step 5 → 角色概念图 → 用户选择
- 确认后 Step 6 → 逐镜生成分镜图片 → 逐张确认
- 确认后 Step 7 → 生成视频 → 预览确认
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.
- 12d ago First seen · 136 lines · 79 tokens per session scan A 121dee95e30e
ip-drama-video-sop is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 79 tokens to every session and 1,718 once invoked, about $0.0004 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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