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编排和协调大情节点与详细情节点的一键生成工作流。适用于长篇故事的结构化分析、需要模块化智能体协作的复杂任务
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Basé sur la classification professionnelle SOC
Create vertical short drama scripts covering macro construction, episode writing, precise optimization, and creative ideation using Emotional Spring Theory. Use when creating short dramas from scratch, optimizing existing script dialogue and pacing, designing story outlines with suspense hooks, or brainstorming slap-in-the-face scenes and character biographies.
Evaluate film and TV scripts across ideological, artistic, and entertainment value dimensions, producing structured scoring reports with follow-up recommendations. Use when assessing script development quality, determining modification direction, reviewing scripts before project approval, or benchmarking screenwriter capabilities.
Evaluate and score story text across market potential, innovation attributes, and content highlights dimensions, producing structured scoring reports with development recommendations. Use when conducting novel initial screening, assessing IP adaptation potential, scoring stories for multi-dimensional quality comparison, or guiding story creation and optimization direction.
Analyze five core story elements — genre type, story summary, character biographies, character relationships, and major plot points — producing a structured comprehensive report with optional mind map. Use when preparing for script adaptation, conducting deep story analysis, creating story development documentation, or evaluating overall story quality and market potential.
Orchestrate the full plot-point dramatic function analysis pipeline — text preprocessing, parallel segment analysis, result integration, and report generation — for long-form story content. Use when analyzing plot points and dramatic functions in long texts, producing structured dramatic analysis reports, or coordinating multi-step story analysis workflows.
基于故事文本分析人物特征，生成详细的人物小传。适用于深度理解故事人物设定、为演员塑造角色提供参考、为剧本创作建立人物档案
| name | plot-workflow |
| description | 编排和协调大情节点与详细情节点的一键生成工作流。适用于长篇故事的结构化分析、需要模块化智能体协作的复杂任务 |
| category | workflow |
| version | 2.1.0 |
| last_updated | "2026-01-11T00:00:00.000Z" |
| license | MIT |
| compatibility | Claude Code 1.0+ |
| maintainer | 宫凡 |
| allowed-tools | ["Read","Write"] |
| model | opus |
| changelog | [{"version":"2.0.0","date":"2026-01-11T00:00:00.000Z","changes":[{"type":"improved","content":"优化 description 字段，使其更精简并符合命令式语言规范"},{"type":"added","content":"添加 allowed-tools (Read, Write) 和 model (opus) 字段"}]},{"version":"2.0.0","date":"2026-01-11T00:00:00.000Z","changes":[{"type":"breaking","content":"按照 Agent Skills 官方规范重构"},{"type":"improved","content":"优化 description，使用命令式语言，精简主内容"},{"type":"added","content":"添加 license、compatibility 可选字段"}]},{"version":"1.0.0","date":"2026-01-10T00:00:00.000Z","changes":[{"type":"added","content":"初始版本"}]}] |
编排和协调大情节点与详细情点的一键生成工作流，实现智能体间的模块化外包和上下文隔离。
第一步：接收用户输入并进行参数验证
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第二步：执行文本预处理（截断、分割）
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第三步：协调批处理执行
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第四步：并行调用各个专业智能体
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第五步：整合所有智能体的输出结果
↓
第六步：格式化并返回最终结果
【工作流执行报告】
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
一、执行状态
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- 总步骤数：[数量]
- 已完成步骤：[数量]
- 当前状态：[状态]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
二、各智能体执行情况
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- [智能体1]：[状态] [结果]
- [智能体2]：[状态] [结果]
...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
三、最终结果
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[整合后的最终结果]
请参见 {baseDir}/references/examples.md 获取详细工作流示例。该文件包含了长篇故事或剧本的大情节点与详细情节点一键生成工作流的完整报告。
| 版本 | 日期 | 变更 |
|---|---|---|
| 2.1.0 | 2026-01-11 | 优化 description 字段，添加 allowed-tools 和 model 字段，调整主内容语言风格，并引导至 references/examples.md |
| 2.0.0 | 2026-01-11 | 按官方规范重构 |
| 1.0.0 | 2026-01-10 | 初始版本 |