Token导航 LogoToken导航

Agent Skill · 开发

glass-cut-video(玻璃切割视频)

用于辅助视频生成、动画合成、脚本化剪辑或 Remotion 等视频项目开发。它适合让 Agent 组织镜头、生成素材说明、维护合成代码或排查渲染问题。使用时需要确认分辨率、时长、素材路径和导出格式;涉及外部素材、人物肖像或商业发布时,应先核对版权授权和内容审核要求。

glass-cut-video 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 6,752。

总安装 4,396周安装 185GitHub Stars 公开资料未说明下载 1,539核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.3

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:glass-cut-video(玻璃切割视频)
来源仓库:https://github.com/zoucdr/glass-cut-video
安装命令:
openclaw skills install glass-cut-video
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install glass-cut-video

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

生成玻璃切割与破碎效果的短视频动画。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。支持文本转视频或将静态图片转化为动态裂纹过程。可用于广告、演示或创意视觉内容制作。需注意素材版权合规性与输出分辨率设置。glass-cut-video 属于开发类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install glass-cut-video;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

glass-cut-video 怎么安装?

在终端执行 openclaw skills install glass-cut-video。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

glass-cut-video 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

glass-cut-video 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

glass-cut-video 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

glass-cut-video 用的人多吗?

公开数据是累计安装 4,396 次,近一周安装 185 次,下载 1,539 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

glass-cut-video 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
glass-cut-video

玻璃切割视频 · 本页

4,396—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
glass-cut-video
version
1.0.0
description
Generate satisfying vertical short videos of glass cutting and shattering (WeryAI): text-to-video or animate a glass photo into cuts, crack spread, and break motion for Douyin/Kuaishou-style ASMR. Use when you need glass cutting video, glass shatter clip, scratch-and-crack ASMR, or users ask for clean slices, crack propagation, or glass ASMR sound.
tags
[asmr, satisfying, short-video, glass, vertical-video]
metadata
{ "openclaw": { "emoji": "🪟", "primaryEnv": "WERYAI_API_KEY", "paid": true, "network_required": true, "requires": { "env": ["WERYAI_API_KEY"], "bins": ["node"], "node": ">=18" } } }
user-invocable
true

Glass cutting & shatter videos

Built for satisfying-content creators. Whether it’s the perfection of a straight cut, the rush of tempered glass exploding, or stained glass glowing in the light—you can go from one line of text or one image to a ready-to-post 9:16 clip.

Dependencies: scripts/video_gen.js in this folder + WERYAI_API_KEY in the environment + Node.js 18+. No other Cursor skills required.

Prerequisites

  • WERYAI_API_KEY must be set in the environment before running video_gen.js.
  • Node.js 18+ is required. Image inputs must be public https URLs (no local file paths).
  • Each successful wait run consumes WeryAI credits; re-running creates new paid tasks.

Security, secrets, and API hosts

  • WERYAI_API_KEY: Treat as a secret. Only configure it if you trust this skill's source; it is listed in OpenClaw metadata as requires.env / primaryEnv so installers know it is mandatory at runtime (never commit it inside the skill package).
  • API hosts (fixed in video_gen.js): Video tasks use https://api.weryai.com; the models list uses https://api-growth-agent.weryai.com. Only WERYAI_API_KEY is read from the environment—do not rely on URL-related environment variables.
  • Higher assurance: Run generation in a short-lived or isolated environment (separate account or container), and review scripts/video_gen.js (HTTPS submit + poll loop) before production use.

Prompt expansion (mandatory)

video_gen.js does not expand prompts. Before every wait --json, turn the user's short or vague brief into a full English production prompt.

When: The user gives only keywords, one line, or loose intent—or asks for richer video language. Exception: They paste a finished long prompt within the model's prompt_length_limit and ask you not to rewrite; still show the full text in the confirmation table.

Always add (video language): shot scale and angle; camera move or lock-off; light quality and motivation; subject action paced to duration; one clear payoff for this niche; state 9:16 vertical when this skill defaults to vertical.

Length: Obey prompt_length_limit for the chosen model_key when this doc lists it; trim filler adjectives before removing core action, lens, or light clauses.

Confirmation: The pre-submit table must include the full expanded prompt (never a one-line summary). Wait for confirm or edits.

Niche checklist

  • Glass grammar: score line, crack propagation, snap, shards, tempered burst, or stained-glass glow—pick one clear beat.
  • Macro & light: caustics, speculars, transmitted color; ASMR glass tone if audio on.
  • Safety tone: stylized satisfying break, not real injury context.

### Example prompts at the top of this file are short triggers only—always expand from the user's actual request.

Workflow

  1. Confirm the request matches this skill (text-to-video and/or image-to-video as documented).
  2. Collect the user's brief, optional image URL(s), tier (best / good / fast) or an explicit model key.
  3. Expand prompt (mandatory): Unless the user supplied a finished long prompt and explicitly asked not to rewrite it, expand the brief into a full English production prompt using ## Prompt expansion (mandatory) below. Do not call the API with only the user's minimal words.
  4. Check the expanded prompt against the selected model's prompt_length_limit in the frozen tables in this document (when present); shorten if needed.
  5. Verify duration, aspect_ratio, resolution, generate_audio, negative_prompt, and other fields against the frozen tables and API notes in this SKILL.md.
  6. Show the pre-submit parameter table including the full expanded prompt; wait for confirm or edits.
  7. After confirmation, run node {baseDir}/scripts/video_gen.js wait --json '...' with the expanded prompt.
  8. Parse stdout JSON and return video URLs; on failure, surface errorCode / errorMessage and suggest parameter fixes.

CLI reference

node {baseDir}/scripts/video_gen.js wait --json '{"model":"…","prompt":"…","duration":5,"aspect_ratio":"9:16"}'
node {baseDir}/scripts/video_gen.js wait --json '…' --dry-run
node {baseDir}/scripts/video_gen.js status --task-id <id>

Definition of done

Done when the user receives at least one playable video URL from the API response, or a clear failure explanation with next steps. All parameters used must fall within the selected model’s allowed sets in this document. The submitted prompt must be the expanded production prompt unless the user explicitly supplied a finished long prompt and asked not to rewrite it.

Boundaries (out of scope)

  • We do not review platform compliance, copyright, or likeness; we do not warrant commercial usability of outputs.
  • We do not provide offline rendering, traditional NLE projects, or API field combinations not documented here.
  • Do not hard-code absolute paths in this doc; {baseDir} is this skill root (next to SKILL.md).

Example prompts

  • Vertical clip: cutting thick glass, satisfying sound, crack spreading from one point
  • This image is a windowpane—animate a blade scratch and cracks growing in the reflection
  • Satisfying glass ASMR 9:16, clean slice, translucent look, not gory
  • Glass cutting ASMR 9:16, slow crack propagation, clean slice reveal

Default parameters

FieldValue
ModelKLING_V3_0_PRO
Aspect9:16 (fixed, vertical short video)
Duration5 s
Resolution— (KLING_V3_0_PRO has no resolution field—do not send)
AudioOn (auto glass cut / shatter sounds)
LookExtreme macro, cold white light, slow motion, minimal background, translucency first
API validity (default KLING_V3_0_PRO): Text-to-video: duration only 5 / 10 / 15, aspect_ratio only 9:16, 1:1, 16:9; image-to-video: aspect_ratio only 9:16, 16:9, 1:1; no resolution field—do not send. Fast VEO tier: text VEO_3_1_FAST, image CHATBOT_VEO_3_1_FAST, duration fixed 8, aspect_ratio only 9:16 or 16:9. For other model_key values, follow the allowed sets in this document; do not send unsupported fields.

Text-to-video: cutting glass

The user describes glass type and how it breaks; you generate the clip. Good for batch ideation and hook testing.

User should provide:

  • Glass type (clear / stained / tempered / frosted / laminated)
  • Cut or break style (straight score-and-snap / irregular cut / slow shatter / full burst / crack spread)
  • Optional: angle or detail (“shards fly”, “side light on refraction”)

Flow:

  1. Collect glass type and break style; ask if missing.
  2. Build an English prompt emphasizing cut marks, refraction, and break motion.
  3. After confirmation, run ({baseDir} = this skill root):
   node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"(full English prompt)","aspect_ratio":"9:16","duration":5,"generate_audio":true}'

Match JSON to the confirmed table; add resolution only if the model supports it. Parse videos from stdout.

  1. Return URLs and what to tweak next.

Parameters:

FieldValue
modelKLING_V3_0_PRO
aspect_ratio9:16
duration5
generate_audiotrue

Sample prompt (stained glass straight cut):

Extreme close-up macro shot of vibrant stained glass being scored and snapped cleanly along a straight line, cold white backlighting makes colors saturate brilliantly, the crack propagates with satisfying precision, glass edge perfectly smooth, slow motion 240fps, dust motes caught in light beam, ASMR cutting sound, minimal white background, shallow depth of field

Sample prompt (tempered shatter):

Ultra close-up of tempered glass shattering into thousands of tiny cubed fragments in extreme slow motion, cold studio lighting catches every spinning shard, crystalline transparency, satisfying explosive burst contained in frame, glass pebbles cascade like water, cinematic depth of field, ASMR crunch and tinkle

Expected result: 5 s vertical clip with coherent cut/shatter motion, readable glass texture and light, synced break sounds, high completion rate.


Image-to-video: glass motion

User supplies a glass image URL; you add crack spread or cut motion on top of that look.

User should provide:

  • Image URL (public https only)
  • Desired motion (crack from center / blade scratch clean line / slow full break / edge chip)

Flow:

  1. Validate https:// URL (not a local path).
  2. Build motion prompt anchored to the image’s glass material.
  3. After confirmation:
   node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"(full English prompt)","image":"(user HTTPS URL)","aspect_ratio":"9:16","duration":5,"generate_audio":true}'
  1. Return URLs.

Parameters:

FieldValue
modelKLING_V3_0_PRO
aspect_ratio9:16
duration5
generate_audiotrue
imageUser’s image URL

Sample prompt (crack spread):

The glass surface in the image begins to crack, a single hairline fracture propagates outward in extreme slow motion, branching into a web of perfect cracks, cold white light refracts through each fracture line creating rainbow caustics, satisfying tension-release moment, ASMR glass stress sound

Expected result: Motion matches the uploaded glass; strong refraction; consistent with the source image.


Prompt building blocks

Brittle snap: satisfying snap, perfect clean break, zero resistance, ultra-brittle

Clean edge: laser-precise cut line, mirror-smooth edge, factory perfect, atomic-level clean slice

Clarity: crystal clear transparency, light refracts into spectrum, backlit caustics, liquid-like clarity

Safe tension: controlled shatter, contained explosion, safe distance macro, slow motion danger

Slow-mo: 240fps extreme slow motion, time dilation effect, every shard frozen mid-air

Note: Image URLs must be publicly reachable over HTTPS or the API will fail. Upload to a host first if needed.

平台分布

OpenClaw

74.32%
按下载量换算1,144

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills