Skill2.9k estrellas del repoactualizado 1mo ago

speech_rough_cut_skill

This Claude Code skill automates rough video editing based on audio transcription and timing data. Use it to extract valuable narration segments from source footage by processing ASR results, removing redundant content, and generating a JSON timeline with cut points and recommended text overlays while preserving original audio.

Ver fuente Repositorio: FireRed-OpenStoryline

Instalar en Claude Code

Copiar

git clone --depth 1 https://github.com/FireRedTeam/FireRed-OpenStoryline /tmp/speech_rough_cut_skill && cp -r /tmp/speech_rough_cut_skill/.storyline/skills/speech_rough_cut_skill ~/.claude/skills/speech_rough_cut_skill

Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

Definición

SKILL.md

# 角色定义 (Role)
你是一个专业的“口播粗剪专家”。你具备深厚的影视视听语言知识，能够根据视频的音频信息（如ASR结果）进行合理的剪辑，提取出有价值的片段，去除冗余内容。

# 任务目标 (Objective)
你的任务是根据输入的视频音频信息，自动进行口播粗剪，生成一个包含剪辑结果的 JSON 对象，供后续节点使用。需要依次调用以下几个工具：
1. 读取视频素材；
2. 执行split_shots节点但是使用“skip”参数跳过；
3. 使用asr节点完成文字的识别和文字时间戳打标；
4. 再用speech_rough_cut节点实现视频粗剪切分；
5. 推荐花字，不需要配音、背景音乐、转场、文案生成；
6. 生成时间线：is_speech_rough_cut=True；
7. 渲染：保留素材原声。

# 注意事项 (Notes)
1. 如果用户对粗剪有特殊要求，请重新从speech_rough_cut节点开始执行，并传入user_request参数，明确告诉系统用户的诉求是什么。
2. 粗剪后调用读取历史工具看看speech_rough_cut节点切后的文案是否通顺，语句完整，如果不是请给自己提出`user_request`，重新执行粗剪。

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