> 把长视频按 Agent 选择的原片区间剪成短片。作为两阶段创作流程中的剪辑环节,读取 clip_plan.json 与源视频, 输出 edited_source.mp4;随后 Agent 按输出时间线写 narration.json。单独调用且未传 --no-narration-map 时, 仍支持旧版单阶段路径,把原片时间的 narration.json 映射为 narration_mapped.json。 触发词:视频剪辑、剪辑式解说、video cut、clip plan、拼剪。
npx skills add https://github.com/worldwonderer/video-recap-skills --skill video-cut
本技能只执行 Agent 已经做出的剪辑决定:
clip_plan.json,写出带 clip_id、原片/输出时间与时长的 clip_plan_validated.json。edited_source.mp4。narration.json。narration_mapped.json。相同输入会得到相同输出。缓存仅表示:当 edited_source.mp4 新于 clip_plan.json 时复用成片。
work_dir/clip_plan.json 可以是数组,也可以是 {"clips": [...]}:
{"start": 12.0, "end": 28.5, "reason": "b02 | turn | power: A→B | POV=女主 | 保留反应 | 入点=问题落下 | 出点=沉默结束"}
start / end 是原片秒数;也接受 source_start / source_end 或 in / out。target_duration,例如 "10m"。source_id。speech_boundary_anchors.json 与 ASR 时间段由理解阶段提供;Agent 先写大致区间,工具会尝试吸附并把仍在讲话区间内的入/出点作为 blocker 返回。work_dir/narration.json 只在旧版单阶段路径中可选读取;该路径要求旁白使用原片时间。若允许重复或重叠片段,旁白可带 source_clip_id 消歧。
工具不会替 Agent 做创作选择。写片段前先完成本节的剪辑意图检查,并让每个区间映射到 recap_story_plan.json 的一个 beat。
使用现有自由文本 reason 保存简洁决定:
beat_id | function | change | POV | preferred moment | 入点 reason | 出点 reason
不要因为“事件重要”就保留整段;要保留最能让 change 成立的具体表演、反应、动作或揭示。理解与情绪允许时晚进早出,同时保证台词、动作和技术边界完整。
下面的 scripts/... 均相对于本技能目录。若执行器从仓库根目录启动,请给脚本路径加上本技能的绝对目录。脚本不从其他技能目录读取文件;外部输入仅限命令显式传入的视频、参数与 work_dir 产物。
python3 scripts/cut.py <video> --work-dir <work_dir> \
[--target-duration 10m] [--clip-padding 0] [--allow-overlap]
clip_plan_validated.json:标准化片段,包含 clip_id、source_start/end、output_start/end 与 duration。edited_source.mp4:按计划拼接后的短视频。narration_mapped.json:仅旧版单阶段路径生成;编排流程使用 --no-narration-map,不会生成该文件。编排流程下游把 edited_source.mp4 当作视频,把 Agent 按输出时间写的 narration.json 当作旁白。
clip_plan.json 与 narration.json 都使用原片时间;本工具负责原片 → 输出映射。narration.json 直接使用剪后输出时间,不再映射。--allow-overlap 开启后,旁白应填写 source_clip_id。unsafe_clip_sentence_boundary 并阻断。SCENE_CUT_SNAP 默认开启:先按画面把 source start 向后、source end 向前吸附到附近硬切,随后句末吸附再做最终修正,避免视觉修正重新制造半句原声。默认范围为 SCENE_CUT_SNAP_MARGIN=0.5 秒,检测阈值为 SCENE_CUT_DETECT_THRESHOLD=0.4。clamped_beats 永久阻断,--allow-sparse-cut 也不能绕过旁白句子完整性。clip_plan.json。Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take worldwonderer/video-cut from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.