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 clipplan.json 与源视频, 输出 editedsource.mp4;随后 Agent 按输出时间线写 narration.json。支持单视频与多视频(sources manifest)拼剪, 本工具不读取、不映射旁白。

MITAuto-check passedMedia & Creative

Install Video Cut

skills CLI
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/video-cut .claude/skills/video-cut && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
video-cut
GitHub stars
561
Token cost
~1.6k tokens
SKILL.md length
270 words
Files
14 (incl. scripts, references)
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

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 clipplan.json 与源视频, 输出 editedsource.mp4;随后 Agent 按输出时间线写 narration.json。支持单视频与多视频(sources manifest)拼剪, 本工具不读取、不映射旁白。

  • Works in 7 steps: 定位 → 输入契约 → 剪辑意图契约 → …
  • Tasks that involve Text to speech and voice
  • SKILL.md covers 1. 定位, 2. 输入契约, 3. 剪辑意图契约 and 4. 运行命令, plus 3 more sections
  • Runs Python scripts from its folder; calls python3 and ffmpeg

What it does

Video Cut is an agent skill from zenstory-ai/video-recap-skills. 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 clipplan.json 与源视频, 输出 editedsource.mp4;随后 Agent 按输出时间线写 narration.json。支持单视频与多视频(sources manifest)拼剪, 本工具不读取、不映射旁白。 触发词:视频剪辑、剪辑式解说、video cut、clip plan、拼剪。

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts and reference files (for example `references/shot-review.md`, `scripts/cut.py` and `scripts/cut_cli.py`).

It sits in Media & Creative, covering Text to speech and voice and Speech recognition and synthesis. It works with FFmpeg. The repository describes itself as: Claude Code / Codex skills that turn a video into a Chinese narration recap (视频解说): scene detection, ASR, VLM, script, TTS, ffmpeg assembly, optional editable JianYing / CapCut… The licence is MIT.

When your agent uses it

  • Tasks that involve Text to speech and voice
  • Tasks that involve Speech recognition and synthesis

Example prompts

  • “/video-cut”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. 定位
  2. 输入契约
  3. 剪辑意图契约
  4. 运行命令
  5. 输出契约
  6. 边界与时间线规则
  7. 能力边界

What it can do on your machine

Read from SKILL.md and the folder at commit 5391686. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 12 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • ffmpeg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Video Cut loads about 1.6k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 270 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from zenstory-ai/video-recap-skills at commit 5391686, republished under its MIT licence (© zenstory-ai). 270 words, ~1,627 tokens.

Download SKILL.mdSave it as .claude/skills/video-cut/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
video-cut
description
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。支持单视频与多视频(sources manifest)拼剪, 本工具不读取、不映射旁白。 触发词:视频剪辑、剪辑式解说、video cut、clip plan、拼剪。
user-invocable
false

1. 定位

本技能只执行 Agent 已经做出的剪辑决定:

  1. 校验并补全 clip_plan.json,写出带 clip_id、原片/输出时间与时长的 clip_plan_validated.json。
  2. 先避开原片硬切附近的闪帧风险,再把边界吸附到可靠句末/自然停顿;声音完整性拥有最终优先级。
  3. 把每个入点对齐到源视频帧网格、每段时长对齐到整数个输出帧(不足一帧的移动,优先选句界门禁仍判为安全、仍在停顿内的一侧;两侧同样安全时选不跨过原片硬切的一侧,避免闪一帧),句界门禁检查的是对齐后的边界。
  4. 拼接选定区间,输出恒定帧率的 edited_source.mp4,帧数与 clip_plan_validated.json 记录的一致。
  5. 到此停止,由 Agent 按真实输出时间线写 narration.json;本工具不读取旁白,也不做原片→输出映射。

相同输入会得到相同输出。edited_source.mp4.meta.json 记录标准化 clips、渲染设置和每个源文件的 size/mtime_ns;三者与当前一致且 edited_source.mp4 存在非空才复用,任一不同即重渲染。只有 sidecar 而没有媒体文件不复用。

2. 输入契约

work_dir/clip_plan.json 可以是数组,也可以是 {"clips": [...]}:

json
{"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。
  • 顶层可选 target_duration,例如 "10m"。
  • 多视频项目的每个片段还必须填写 source_id(不接受 id 代替),并用 --sources-manifest 传入来源清单(形状见下)。
  • 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。只含语气词或 ASR 杂音的窗口("啊!"、"Hi.")不算讲话区间,只在紧挨真实对白的一侧保留 1 秒保护。只有标点的窗口("……")仍算讲话。

多视频来源清单只接受一种形状,其他形状直接报错并写明期望形状:

json
{"sources": [{"source_id": "ep1", "source_path": "/media/ep1.mp4", "duration": 1520.0, "source_work_dir": "sources/ep1"}]}

duration 可省略(省略时用 ffprobe 读取);source_work_dir 可省略,填写时相对 --work-dir,用于读取该来源的静音、句末锚点与 ASR。其他键忽略。

3. 剪辑意图契约

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:

text
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。

对不能删去的问答、反应或动作兑现,先核源证据,再在同一 clip_plan.json 登记精确区间:

json
{
  "clips": [{"start": 12, "end": 18}],
  "required_evidence": {
    "nodes": [
      {"id": "refusal", "source": "/media/episode.mp4", "start": 12.25, "end": 14.5, "track": "audio", "content": "对方拒绝请求"},
      {"id": "response", "source": "/media/episode.mp4", "start": 15, "end": 17.5, "track": "video", "content": "听到拒绝后的反应与决定"}
    ],
    "before": [["refusal", "response"]]
  }
}

source 使用实际源文件绝对路径,start/end 是原片秒;多源可另填 source_id 消歧。只登记确实需要保留的具体时刻,不将整个 beat 默认锁死。before 只登记本片必需的先后关系;无需约束顺序时写 before: []。

工具在全部画面/句界吸附后检查每个必保时刻至少有一处完整连续保留、来源和先后;音频节点还检查源音轨是否存在。每次结果出现(包括局部片段)都需满足其声明的前提,不能用后面的完整段替开头缺前提的片段过关。结果写入 clip_plan_validated.json.qc.required_evidence;缺段、错序或无效声明会在预检、缓存复用和渲染前阻断,时长放宽选项不会跳过。该结果验证选段保留,实际语义与最终混音仍按审片步骤核对。

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。

4. 运行命令

bash
python3 scripts/cut.py <video> --work-dir <work_dir> [--clip-plan <clip_plan.json>] \
  [--sources-manifest <sources.json>] [--target-duration 10m] [--allow-overlap] \
  [--allow-duration-drift] [--normalize-only] \
  [--review-shots [--shot-scene-threshold 0.35] [--shot-roi X Y W H]]
  • --clip-plan:剪辑计划路径,默认 <work_dir>/clip_plan.json。

  • --sources-manifest:多源剪辑的来源清单 {"sources": [{"source_id", "source_path"[, "duration", "source_work_dir"]}]};片段用 source_id 指明来源,并按自己的来源吸附句界与画面切点。

  • --target-duration:目标时长。实际时长与目标之比在 0.85–1.15 之外记 warning,在 0.60–1.40 之外阻断。

  • --allow-duration-drift:只放行时长偏差阻断(记为 allowed: true 的 warning),不放行句界或必保证据阻断。

  • --normalize-only:只标准化、吸附并检查计划,写出 clip_plan_validated.json 后退出,不渲染。

  • --review-shots:在渲染或复用的 edited_source.mp4 上召回短镜与密集切点候选,只报告、不修复;--shot-scene-threshold 是召回阈值(默认 0.35,不是验收标准),--shot-roi 只扫描该像素矩形(有黑边或包装时用),不裁画面。

cut 阻断时以非零状态退出,并把原因写入 clip_plan_validated.json 的 qc.blocking,每项带 code:

  • unsafe_clip_sentence_boundary:片段边界仍在原声讲话内;逐边界判定见 qc.boundary_status.sentence_checks。被阻断的边界带 nearest_safe: {"before", "after"}:前后 5 秒内最近的安全边界 {time, reason, delta}(原片秒;delta 为相对当前边界的秒数,没有则为 null;已是原片帧网格上的落点并复核过;重跑时切镜头避让若把它拉回讲话内,这次避让会被撤回,qc.boundary_status.shot_snaps 记 reverted_unsafe,所以原样写回不会再因句界被阻断),按它改 clip_plan.json 的 start/end 后重跑。入点往前(before)是多保留、往后(after)是裁掉,出点相反;先确认改动不会切掉必保内容或与相邻片段重叠。两侧都是 null 说明附近没有停顿,要换区间而不是微调。
  • target_duration_drift:时长偏差超出阻断阈值;明细见 qc.target_duration。
  • REQUIRED_EVIDENCE_INVALID / REQUIRED_EVIDENCE_MISSING / REQUIRED_EVIDENCE_ORDER / REQUIRED_EVIDENCE_AUDIO_UNAVAILABLE:必保证据声明无效、缺段、错序或源无音轨;明细见 qc.required_evidence。

5. 输出契约

  • clip_plan_validated.json:标准化片段,包含 clip_id、source_start/end、output_start/end、duration 与 frame_count(该片段渲染的帧数)。qc.frame_grid 记录输出帧率 output_frame_rate(单源沿用源帧率,多源用画布帧率,NTSC 写成 30000/1001)、总帧数与各源帧率;每次帧对齐的前后时间写在 qc.boundary_status.frame_snaps。
  • edited_source.mp4:按计划拼接后的恒定帧率短视频,帧数等于 qc.frame_grid.frame_count。
  • shot_review.json:仅 --review-shots 开启后生成的实际视频短镜/密集切镜候选;不会更改计划。

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。

6. 边界与时间线规则

  • clip_plan.json 使用原片时间;narration.json 直接使用剪后输出时间,不存在原片 → 输出的旁白映射。输出时间以 clip_plan_validated.json 为准:帧对齐会把边界移动不到一帧(25fps 下不超过 40 ms),写旁白前读 validated 计划,不要用自己写的原始区间推算。
  • 边界不在帧网格上时,concat 会在每个接点丢掉一个帧位(25fps 下画面停顿 80 ms,成片变成可变帧率),所以帧对齐无法关闭。源帧率未知(r_frame_rate 为 0/0 或大于 120)时入点不动,时长仍对齐到整数输出帧。
  • 默认禁止重叠或重复原片区间;--allow-overlap 开启后才允许。
  • 片段起点只能位于源头、可靠句末/静音窗,或与上一片段构成无损同源连续连接;片段终点同理。ASR 判定仍在讲话且无法吸附时写入 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;附近没有停顿、句末吸附修不回来时,把边界移进讲话的那次避让会被撤回(原位置能过门禁时)。默认范围为 SCENE_CUT_SNAP_MARGIN=0.5 秒,检测阈值为 SCENE_CUT_DETECT_THRESHOLD=0.4。
  • scene-change score 只提供接点候选,不证明接点自然。先检查短时间窗内是否出现密集候选,再区分来源:原片自带的无关短镜头整段删除;相关但短到像闪帧的镜头通过扩展 IN/OUT 保留完整动作、反应或台词,不用定格/慢放伪造时长;由本次拼接制造的切点则优先移动边界、恢复同源连续运动、合并相邻片段或改用更自然的连接,尽量消除。成片后仍要逐个播放接点前后约 0.5–1 秒;白闪或曝光叠化再结合逐帧亮度定位,不能为了通过视觉检测切断完整台词,也不能用转场遮掩坏接点。
  • 修短残镜时不得仅为压低 scene 分数而对接点附近施加与所属镜头不连续的极端放大或位移;取景复核与修复验证流程见 references/shot-review.md。
  • 连续同源片段的无损连接不做句中双侧音频淡出;非连续片段仍在安全停顿内做防爆音淡入淡出。

需要检查短时间频繁切镜时,先用 ffmpeg scene filter 召回候选时间:

bash
ffmpeg -i input.mp4 -vf "select='gt(scene,0.35)',showinfo" -an -f null -

0.35 是起始阈值,不是质量判据;大幅运动、闪白和叠化都可能误报。把候选映射回原片 shot 与本次拼接边界后,按上面的来源分类处理,并以正常速度播放决定是否保留。

需要精确到实际帧、检查长区间内部残镜并记录所用计划路径时,使用 scripts/shot_review.py 或 cut.py --review-shots(有黑边或包装时加 --roi / --shot-roi); 详见 references/shot-review.md。

7. 能力边界

  • 不做语义理解,不写旁白,不替 Agent 选择片段;scene filter 只承担技术边界候选检测。
  • 只做生成 edited_source.mp4 所需的剪切、拼接与一次中间编码,不承担字幕包装或最终交付压缩。

© zenstory-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 13 other files (scripts, references) in skills/video-cut of zenstory-ai/video-recap-skills.

  • SKILL.md
  • references/shot-review.md
  • scripts/cut.py
  • scripts/cut_cli.py
  • scripts/cut_contract.py
  • scripts/cut_qc.py
  • scripts/cut_render.py
  • scripts/frame_grid.py
  • scripts/lib.py
  • scripts/media_geometry.py
  • scripts/narrative_selection.py
  • scripts/sentence_boundaries.py
  • scripts/sentence_gate.py
  • scripts/shot_review.py

Open the folder on GitHubat commit 5391686

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in zenstory-ai/video-recap-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Works with

Questions about Video Cut

What does Video Cut do?

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 clipplan.json 与源视频, 输出 editedsource.mp4;随后 Agent 按输出时间线写 narration.json。支持单视频与多视频(sources manifest)拼剪, 本工具不读取、不映射旁白。. Video Cut is an agent skill from zenstory-ai/video-recap-skills.

When should I use Video Cut?

Video Cut fits situations like: tasks that involve Text to speech and voice; tasks that involve Speech recognition and synthesis.

How do I install Video Cut in Claude Code?

Run `npx skills add zenstory-ai/video-recap-skills --skill video-cut -a claude-code`. Or copy the skill folder (skills/video-cut in zenstory-ai/video-recap-skills) into .claude/skills/video-cut in your project. Claude Code loads it when a task matches its description.

How do I install Video Cut in Codex?

Run `npx skills add zenstory-ai/video-recap-skills --skill video-cut -a codex`. Or copy the skill folder (skills/video-cut in zenstory-ai/video-recap-skills) into .agents/skills/video-cut in your project. Codex loads it when a task matches its description.

Can I use Video Cut in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zenstory-ai/video-recap-skills --skill video-cut -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/video-cut, .gemini/skills/video-cut, .github/skills/video-cut and .opencode/skills/video-cut in your project.

What does Video Cut need to run?

Going by SKILL.md and its folder, Video Cut needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and ffmpeg). Our summary lists: Python 3.

Does Video Cut access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Video Cut safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Video Cut use?

Video Cut is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Video Cut use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Video Cut?

Skills that share tags, products or a category with Video Cut: Video Production (speechlab0210/video-production-skill, 105 stars), Summarize Call (reysu/ai-life-skills, 270 stars), Podcast (zarazhangrui/personalized-podcast, 438 stars) and Book Sales Video (Kianzzz/book-sales-video, 218 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Video Cut?

zenstory-ai (a GitHub organization) maintains it in zenstory-ai/video-recap-skills, which has 561 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 4, 2026.

Source: zenstory-ai/video-recap-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.