Carpet Wash Video
/install carpet-wash-video
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_KEYmust be set.- Node.js 18+; images must be
httpsURLs.
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 asrequires.env/primaryEnvso 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.jsdefaults tohttps://api.weryai.comandhttps://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
- Confirm scenario (text-to-video and/or image-to-video as documented).
- Collect the user's brief, optional
httpsimage URL, tier (best / good / fast) or explicitmodel. - 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. - Check the expanded
promptagainstprompt_length_limitif listed for the model; trim if needed. - Verify
duration,aspect_ratio,generate_audio, and other fields against this doc. - Show the confirmation table with the full expanded
prompt; wait for confirm or edits. - After confirmation, run
node {baseDir}/scripts/video_gen.js wait --json '...'with the expanded prompt. - 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 \x3Cid>
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 satisfyingFrom this moldy carpet photo—rinse and dark water pouring offOCD clean macro: pile goes grey to bright, foam and squeeze momentSatisfying carpet deep clean 9:16, dirty-to-clean reveal line
Default parameters
| Field | Value |
|---|---|
| Model | KLING_V3_0_PRO |
| Aspect | 9:16 |
| Duration | 5 (short hook) |
| Audio | On (brush, water, squeeze ASMR) |
| Look | Top-down close, soft light, extreme before/after color, slow grime flow, minimal background |
API validity (
KLING_V3_0_PRO): Text:duration5 / 10 / 15,aspect_ratio9:16, 1:1, 16:9; image:aspect_ratio9:16, 16:9, 1:1; noresolution. VEO fast:VEO_3_1_FAST/CHATBOT_VEO_3_1_FAST,duration8,aspect_ratio9:16 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.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install carpet-wash-video - 安装完成后,直接呼叫该 Skill 的名称或使用
/carpet-wash-video触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Carpet Wash Video 是什么?
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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 110 次。
如何安装 Carpet Wash Video?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install carpet-wash-video」即可一键安装,无需额外配置。
Carpet Wash Video 是免费的吗?
是的,Carpet Wash Video 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Carpet Wash Video 支持哪些平台?
Carpet Wash Video 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Carpet Wash Video?
由 parallel world(@zoucdr)开发并维护,当前版本 v0.1.0。