Video Production
speechlab0210/video-production-skill
AI educational video production pipeline. An agent skill from speechlab0210/video-production-skill.
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。
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .claude/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cutType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/video-cut .agents/skills/video-cut && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .agents/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/video-cut .cursor/skills/video-cut && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .cursor/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zenstory-ai/video-recap-skills.git --path skills/video-cut--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/video-cut .gemini/skills/video-cut && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .gemini/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zenstory-ai/video-recap-skills video-cutInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/video-cut .github/skills/video-cut && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .github/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zenstory-ai/video-recap-skills --skill video-cut -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zenstory-ai/video-recap-skills video-cut --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zenstory-ai/video-recap-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/video-cut .opencode/skills/video-cut && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "video-cut" agent skill from https://github.com/zenstory-ai/video-recap-skills/tree/main/skills/video-cut into .opencode/skills/video-cut/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "video-cut", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
video-cut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. 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.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 5391686. It shows what the files ask for, not the result of running them.
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.
Ships 12 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3ffmpegFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from zenstory-ai/video-recap-skills at commit 5391686, republished under its MIT licence (© zenstory-ai). 270 words, ~1,627 tokens.
.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.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,帧数与 clip_plan_validated.json 记录的一致。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。
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(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:
{"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。
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。
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:
{
"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。
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。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。
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。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。--allow-overlap 开启后才允许。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。references/shot-review.md。需要检查短时间频繁切镜时,先用 ffmpeg scene filter 召回候选时间:
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。
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
SKILL.md and 13 other files (scripts, references) in skills/video-cut of zenstory-ai/video-recap-skills.
Open the folder on GitHubat commit 5391686
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.
Video Cut next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Video Cut this skillzenstory-ai/video-recap-skills | 561 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Video Productionspeechlab0210/video-production-skill | 105 | — | ~4.1k | Automated safety check: Notes | MIT | |
| Summarize Callreysu/ai-life-skills | 270 | — | ~3.8k | Automated safety check: Notes | MIT | |
| Podcastzarazhangrui/personalized-podcast | 438 | — | ~2.3k | Automated safety check: Notes | None | |
| Book Sales VideoKianzzz/book-sales-video | 218 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Dotty Av TestBrettKinny/dotty-stackchan | 113 | — | ~866 | Automated safety check: Notes | MIT |
speechlab0210/video-production-skill
AI educational video production pipeline. An agent skill from speechlab0210/video-production-skill.
reysu/ai-life-skills
Transcribe a call recording with speaker diarization, summarize it, and create Obsidian vault notes (call note, transcript, person notes for participants).
zarazhangrui/personalized-podcast
Generate a podcast episode from content you provide. An agent skill from zarazhangrui/personalized-podcast.
Kianzzz/book-sales-video
从书名或飞书多维表格中的成稿文案出发,结合微信读书资料与公开点评创作图书带货/书评短视频,并用豆包 TTS、Pexels、Codex 生图和本机 OpenChatCut 完成配音、配图、双语字幕、音效、动效、BGM、可编辑初稿与按需导出。用户提出“根据一本书做带货视频”“读取飞书文案制作图书视频”“写书评口播并自动剪成抖音视频”“仿参考样式做图书推荐短视频”时使用;仅查书、仅写普通书评或无关剪辑…
BrettKinny/dotty-stackchan
Run local black-box voice tests against the physical Dotty robot by playing a TTS prompt through the workstation speakers while the C920 records video and room audio.
TheCraigHewitt/skills
When the user wants to read, transcribe, summarize, or research a video — YouTube link, podcast clip, Loom, TikTok, X/Twitter video, local file, or any URL yt-dlp supports.
zenstory-ai/video-recap-skills
从输入视频生成中文解说成片或原声剧情短片。用户提供 .mp4 / .mov / .mkv / .webm,并要求剪辑、添加旁白、 配音、总结、短剧/电视剧/电影/纪录片/科普解说时使用。负责编排 video- 技能链:视频理解 → Agent 制定故事与视听方案 → 剪辑 → 配音 → 合成。触发词:视频解说、视频旁白、生成解说、 视频 recap、video…
zenstory-ai/video-recap-skills
合成视频解说最终成片:把旁白音频铺到源视频上,按旁白窗口压低原声,生成 SRT / ASS 字幕并可烧录, 最后做响度标准化。作为最终合成阶段使用。输入源视频、ttsmeta.json 与旁白位置; 输出 recap 成片和字幕。触发词:视频合成、混音、字幕、压字幕、assemble video、mux、ducking、subtitles、成片。
zenstory-ai/video-recap-skills
按需把一部成片拆成可复用的制作参考:测镜头节奏与响度,标注段落与音轨分工,把原片事实与可迁移方法分开, 导出不含原片人名台词的 productionreference.json 供下次制作参考。不在默认生产路径上。
zenstory-ai/video-recap-skills
对已完成分析的视频进行导演与剪辑策划,再写带时间戳的中文解说并校验;也处理已有短片的 宣发标题、花字修订和外部文案回填。普通策划输入 workdir 的 agentnarrationbrief.md 与 vlmanalysis.json;文案返修输入当前成片的工程与内容证据。策划输出 recapstoryplan.json、visualaudioboard.json、 可选…
zenstory-ai/video-recap-skills
把视频分析为结构化理解索引:场景检测、ASR 转写、逐场景 VLM 观察、静音窗口、融合时间线和写作 brief. An agent skill from zenstory-ai/video-recap-skills.
zenstory-ai/video-recap-skills
把带时间戳的 narration.json 合成为中文解说音频。使用 MiMo TTS(mimo-v2.5-tts)或 Fish Audio(s2.1-pro-free)或显式配置的通用 IndexTTS HTTP 服务逐段生成语音, 按时间窗动态适配语速并处理响度;输入输出时间线上的旁白,产出 ttssegments 与 ttsmeta.json。
Works with
Categories
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.
Video Cut fits situations like: tasks that involve Text to speech and voice; tasks that involve Speech recognition and synthesis.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.