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carpet-wash-video地毯清洗视频

Agent Skill

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。

总安装

3,246

周安装

138

GitHub Stars

公开资料未说明

下载量

1,137
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

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能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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75.22%
按下载量换算855

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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

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