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Agent Skill · 效率

carpet-wash-video(地毯清洗视频)

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。

carpet-wash-video 是效率类 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,按安装量在效率类的 21,003 个 Skill 里排第 14,945。

总安装 3,246周安装 138GitHub Stars 公开资料未说明下载 1,137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
0.1.0

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:carpet-wash-video(地毯清洗视频)
来源仓库:https://github.com/zoucdr/carpet-wash-video
安装命令:
openclaw skills install carpet-wash-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 carpet-wash-video

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

carpet-wash-video 生成地毯清洁过程短视频,支持文本转视频或照片驱动动画。适用于家政培训、产品演示或短视频内容创作。通过 clawhub 安装,集成于 OpenClaw,基于 WeryAI 技术生成垂直短片。使用前需确认素材版权与人物肖像授权情况。建议输出前预览效果,调整镜头节奏与字幕准确性。

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 carpet-wash-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

carpet-wash-video 怎么安装?

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

carpet-wash-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。

carpet-wash-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。

carpet-wash-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。

carpet-wash-video 用的人多吗?

公开数据是累计安装 3,246 次,近一周安装 138 次,下载 1,137 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。

carpet-wash-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。
  • 需要处理效率类任务、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。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
carpet-wash-video

地毯清洗视频 · 本页

3,246—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
carpet-wash-video
version
1.0.0
description
Generate satisfying vertical carpet deep-clean shorts (WeryAI): text-to-video or dirty rug photo to rinse, grime runoff, and fiber revival. Use when you need carpet cleaning satisfying video, stain removal rinse, dirty-to-clean reveal, or users ask for grime line, fluffy pile comeback, before/after contrast.
tags
[cleaning, satisfying, asmr, short-video, vertical]
metadata
{ "openclaw": { "emoji": "🧹", "primaryEnv": "WERYAI_API_KEY", "paid": true, "network_required": true, "requires": { "env": ["WERYAI_API_KEY"], "bins": ["node"], "node": ">=18" } } }
user-invocable
true

Carpet wash & restore videos

For cleaning / satisfying creators: pressure rinse, brush agitation, dirty water running out, fibers standing up again—strong before/after and ASMR wash sounds. One prompt or one dirty carpet photo.

Dependencies: scripts/video_gen.js + WERYAI_API_KEY + Node.js 18+.

Prerequisites

  • WERYAI_API_KEY must be set.
  • Node.js 18+; images must be https URLs.

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).
  • Optional URL overrides (WERYAI_BASE_URL, WERYAI_MODELS_BASE_URL): video_gen.js defaults to https://api.weryai.com and https://api-growth-agent.weryai.com. Overrides are intended for testing or approved alternate endpoints. If these variables are set in your environment, confirm they point to hosts you trust—otherwise prompts, images, and your bearer token could be sent elsewhere.
  • 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

  • Wash physics: water/foam migration, dirty runoff, fiber lift, before/after color stripe or patch.
  • Camera: top-down or low side angle; slow motion on squeeze or extractor pass; ASMR-forward if audio on.
  • Stains: tie language to user rug type (mud, pet, grey traffic) and method (pressure, brush, steam).

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

Workflow

  1. Confirm scenario (text-to-video and/or image-to-video as documented).
  2. Collect the user's brief, optional https image URL, tier (best / good / fast) or explicit model.
  3. Expand prompt (mandatory): Unless the user supplied a finished long prompt and asked not to rewrite, expand the brief with full shot, light, wash physics, and audio cues per ## Prompt expansion (mandatory). Do not submit a one-liner.
  4. Check the expanded prompt against prompt_length_limit if listed for the model; trim if needed.
  5. Verify duration, aspect_ratio, generate_audio, and other fields against this doc.
  6. Show the confirmation table with 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. Return playable URL(s) or clear error guidance.

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

Playable URL(s) or clear failure; parameters within model limits. 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)

  • No legal/commercial guarantees; no offline editing stacks; {baseDir} only—no absolute paths.

Example prompts

  • Filthy living-room rug, pressure washer leaves a clean stripe, vertical satisfying
  • From this moldy carpet photo—rinse and dark water pouring off
  • OCD clean macro: pile goes grey to bright, foam and squeeze moment
  • Satisfying carpet deep clean 9:16, dirty-to-clean reveal line

Default parameters

FieldValue
ModelKLING_V3_0_PRO
Aspect9:16
Duration5 (short hook)
AudioOn (brush, water, squeeze ASMR)
LookTop-down close, soft light, extreme before/after color, slow grime flow, minimal background
API validity (KLING_V3_0_PRO): Text: duration 5 / 10 / 15, aspect_ratio 9:16, 1:1, 16:9; image: aspect_ratio 9:16, 16:9, 1:1; no resolution. VEO fast: VEO_3_1_FAST / CHATBOT_VEO_3_1_FAST, duration 8, aspect_ratio 9:16 or 16:9. Other keys: follow tables here.

Text-to-video: wash process

User provides: rug type (short pile / shag / weave / vintage / pet mat), stain type (overall grey / embedded fur / mud spot / drink / years of dust), optional method (pressure / brush / steam / extractor).

Flow: collect → build English prompt (runoff, color return, fiber lift) → run:

node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"(English prompt)","aspect_ratio":"9:16","duration":5,"generate_audio":true}'

Parameters: model KLING_V3_0_PRO, 9:16, 5, generate_audio true.

Expanded prompt: Build per ## Prompt expansion (mandatory) from the user's rug/stain brief; do not paste fixed samples.

Expected: Visible dirty water, strong color bounce-back, ASMR-friendly audio.


Image-to-video: clean the photo rug

User provides: https image URL + effect (pressure / brush / extract / steam).

Flow: validate URL → prompt matched to stains in image →

node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"(English)","image":"(URL)","aspect_ratio":"9:16","duration":5,"generate_audio":true}'

Expected: Grime matches photo; true color emerges; pattern continuity.


Prompt blocks

Grime: thick dark water flows outward, murky brown runoff cascades, grime releases in satisfying rivulets

Color return: vivid original color emerges beneath, saturated hues pop, before-after color contrast in single frame

Pile: fibers lift and separate, pile stands upright after cleaning, fluffy texture restored

Sound: ASMR scrubbing, pressure washer hiss, wet squeegee drag

Upload local shots to a public host first; API needs reachable HTTPS.

平台分布

OpenClaw

75.22%
按下载量换算855

安全审计

VirusTotal

未展示

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。

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