generate-video
- Repo stars 2,457
- Author updated Live
- Author repo ArcReel
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ArcReel · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Shell exec
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: generate-video
description: MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor： | 剧本特征 | 路由 | 输出目录 | | generationmode == "referencevideo" 或…
category: ai
runtime: Python
---
# generate-video output preview
## PART A: Task fit
- Use case: MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor： | 剧本特征 | 路由 | 输出目录 | | generationmode == "referencevideo" 或存在 videounits[] | tasktype="referencevideo" → executereferencevideotask | referencevideos/{unitid}.mp4 | | segments[]（narration） | tasktype="video" → executevideotask | videos/scene{segment_id}.mp4 | runs entirely locally; runs on Python. Works with Claude Code….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “模式自动分派 / 工具调用 / 工作流程” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor： | 剧本特征 | 路由 | 输出目录 | | generationmode == "referencevideo" 或存在 videounits[] | tasktype="referencevideo" → executereferencevideotask | referencevideos/{unitid}.mp4 | | segments[]（narration） | tasktype="video" → executevideotask | videos/scene{segment_id}.mp4 | runs entirely locally; runs on Python. Works with Claude Code…”.
- **02** When the source has headings, the agent prioritizes “模式自动分派 / 工具调用 / 工作流程” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, run shell commands, write/modify files.
Start with a small task and check whether the result follows “模式自动分派 / 工具调用 / 工作流程”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: generate-video
description: MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor： | 剧本特征 | 路由 | 输出目录 | | generationmode == "referencevideo" 或…
category: ai
source: ArcReel/ArcReel
---
# generate-video
## When to use
- MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor： | 剧本特征 | 路由 | 输出目录 | | generationmode == "referencevideo" 或存在 videounits[] | task…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “模式自动分派 / 工具调用 / 工作流程” and keep inference separate from source facts.
- read files, run shell commands, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "generate-video" {
input -> user goal + target files + boundaries + acceptance criteria
context -> 模式自动分派 / 工具调用 / 工作流程
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, run shell commands, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}生成视频
模式自动分派
MCP 工具在读取剧本后检测顶层结构，自动路由到对应 executor：
| 剧本特征 | 路由 | 输出目录 |
|---|---|---|
generation_mode == "reference_video" 或存在 video_units[] |
task_type="reference_video" → execute_reference_video_task |
reference_videos/{unit_id}.mp4 |
segments[]（narration） |
task_type="video" → execute_video_task |
videos/scene_{segment_id}.mp4 |
scenes[]（drama） |
同上 | videos/scene_{scene_id}.mp4 |
参考模式跳过分镜图要求，直接把 {script_file} 丢给 executor；executor 自行读取 unit.references → 从 characters/scenes/props 三 bucket 解析 sheet 图 → 内存压缩 → 渲染 prompt → 调 VideoBackend。
为每个场景/片段/unit 创建视频。storyboard/grid 模式用分镜图作为起始帧；reference_video 模式用角色/场景/道具参考图作为 reference_images，跳过分镜环节。
画面比例、时长等规格由项目配置和视频模型能力决定，MCP 工具自动处理。
工具调用
重要：生成视频必须调用下列 MCP 工具入队。此 skill 不提供任何 Python/Shell 脚本，不得用 BASH 调 python .../scripts/*.py。
通过 MCP 工具入队：
| 操作 | 工具 |
|---|---|
| 整集生成（默认） | mcp__arcreel__generate_video_episode({"script": "episode_1.json"}) |
| 断点续传 | mcp__arcreel__generate_video_episode({"script": "episode_1.json", "resume": true}) |
| 单场景 | mcp__arcreel__generate_video_scene({"script": "episode_1.json", "scene_id": "E1S01"}) |
| 批量自选 | mcp__arcreel__generate_video_selected({"script": "episode_1.json", "scene_ids": ["E1S01", "E1S05", "E1S10"]}) |
| 自选 + 续传 | mcp__arcreel__generate_video_selected({"script": "episode_1.json", "scene_ids": [...], "resume": true}) |
| 全部待处理（独立模式） | mcp__arcreel__generate_video_all({"script": "episode_1.json"}) |
所有任务一次性提交到生成队列，由 Worker 按 per-provider 并发配置自动调度。 集号从 script 顶层
episode或文件名推导，无需手动传。reference_video模式下scene_id/scene_ids会被忽略，转整集生成。
工作流程
- 加载项目和剧本 — 确认所有场景都有
storyboard_image - 生成视频 — MCP 工具自动构建 Prompt、调用 API、保存 checkpoint
- 审核检查点 — 展示结果，用户可重新生成不满意的场景
- 更新剧本 — 自动更新
video_clip路径和场景状态
Prompt 构建
Prompt 由 MCP 工具内部自动构建，根据 content_mode 选择不同策略。从剧本 JSON 读取以下字段：
image_prompt（用于分镜图参考）：scene、composition（shot_type、lighting、ambiance）
video_prompt（用于视频生成）：action、camera_motion、ambiance_audio、dialogue、narration（仅 drama）
- 说书模式：
novel_text不参与视频生成（后期人工配音），dialogue仅包含原文中的角色对话 - 剧集动画模式：包含完整的对话、旁白、音效
- Negative prompt 自动排除 BGM
生成前检查
- 所有场景都有已批准的分镜图
- 对话文本长度适当
- 动作描述清晰简单
reference_video 模式
- 所有 unit 引用的角色 / 场景 / 道具在 project.json 三 bucket 中已注册且
*_sheet文件存在 - 每 unit shots 数 ≤ 4，总时长 ≤ 模型上限
- references 数 ≤ 模型
max_reference_images
参考生视频模式下，输出命名为
{unit_id}.mp4，位于reference_videos/目录。
Decide Fit First
Design Intent
How To Use It
Boundaries And Review