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rust-restore-videoRust restore video 图像

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。

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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:rust-restore-video(Rust restore video 图像)
来源仓库:https://github.com/zoucdr/rust-restore-video
安装命令:
openclaw skills install rust-restore-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 rust-restore-video

简介

生成垂直锈迹修复短片,支持文本转视频或生锈物体图像处理。

  • 适用于工业除锈演示、科普教育或产品营销视频制作。
  • 基于 WeryAI 技术实现打磨抛光运动模拟,输出高质量短片。
  • 使用时需明确分辨率、时长和素材版权归属要求。rust-restore-video 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 涉及人物或品牌元素时应额外核对授权协议与展示规范。

SKILL.md

name
rust-restore-video
version
1.0.2
description
Generate vertical satisfying rust restoration shorts (WeryAI): text-to-video or rusty-object image to grind and polish motion. Use when you need rust restoration satisfying video, derust refresh clips, or users ask for scale flaking off, metal brightening, mirror finish. SEO: rust restore video; rust restoration video generation.
tags
[restoration, satisfying, asmr, craft, vintage, short-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

Rusty object restoration video generation

Rusty knives, blackened padlocks, vintage lighters—the moment the wheel touches, scale tears away and bare metal lights up along the grind path. “Junk reborn” is a high-completion lane for satisfying / men’s content; one line or one rust photo, straight to clip.

Dependencies: WERYAI_API_KEY + Node.js 18+. When you run the CLI, scripts/video_gen.js must exist; WERYAI_VIDEO_API.md must exist under resources/ (supply both via publish or pre-use assembly). Full commands and JSON fields: see resources/WERYAI_VIDEO_API.md. No other Cursor skills. Default parameters and model tiers are in the tables below; live API limits follow weryai models. Before installing or running, review the bundled video_gen.js to confirm it meets your requirements. Pay particular attention to how it handles local files if you choose to allow their usage, ensuring this behavior aligns with the skill's intended workflow for image-to-video requests.

Prerequisites

  • WERYAI_API_KEY must be set in the environment before running video_gen.js.
  • Node.js 18+ is required. Prefer public https image URLs. If the assembled scripts/video_gen.js supports local file paths, review and verify the script first, then get explicit consent before it reads a local image and uploads it to WeryAI to obtain a public URL.
  • 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.
  • Local image handling disclosure: Prefer public https image URLs. If the assembled scripts/video_gen.js supports local file paths, it may read a local image and upload it to WeryAI to obtain a public URL; require review / verification and explicit consent before using that path.
  • 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. Verify whether the runtime can read local image files and upload them to WeryAI, and obtain explicit consent before using that path.

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

  • Restore arc: rust/oxidized → abrasive/polish/coat steps → shine reveal; macro metal texture.
  • Motion: rotary tool, wipe, cloth buff—readable cause→effect; sparks optional if stylized safe.
  • Reflection: final specular read proves the restore.

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

Workflow

  1. Confirm the user request matches this skill's scenario (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 in this document and WERYAI_VIDEO_API.md.
  6. Show the pre-submit parameter table including the full expanded prompt; wait for confirm or edits.
  7. After confirmation, run node 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 scripts/video_gen.js wait --json '{"model":"…","prompt":"…","duration":5,"aspect_ratio":"9:16"}'
node scripts/video_gen.js wait --json '…' --dry-run
node scripts/video_gen.js status --task-id <id>

Full reference: WERYAI_VIDEO_API.md.

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 outside WeryAI, traditional NLE projects, or API field combinations not documented in this SKILL or WERYAI_VIDEO_API.md.
  • Do not link to weryai-model-capabilities.md or shared ../references/ paths; use resources/WERYAI_VIDEO_API.md for CLI/API details.
  • Do not hard-code absolute paths in this doc; run from the skill package root (next to SKILL.md) so scripts/ and resources/ paths resolve.

Example prompts

  • Rusty wrench sanded and polished to mirror metal, vertical satisfying restore
  • From this rusty pan: scale curls off, bottom brightens
  • Old-tool restore vibe—each step readable, not too fast
  • Rusty tool restoration 9:16, corrosion flakes off then mirror shine

Default parameters

FieldValue
ModelKLING_V3_0_PRO
Aspect9:16 (fixed, vertical short)
DurationShort (duration: 5)
LookTight macro, dark workshop (wood bench / anvil), side light on metal texture, slow-mo sparks and rust dust (fixed)
AudioOn (wheel + metal friction ASMR is core)
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 and the API validity notes above; do not send unsupported fields such as resolution.

Text-to-video: restore from description

Give object type (old knife / padlock / lighter / wrench / gear) and the beat to show (grind / polish / oil / reassembly); the skill fills visual detail.

Flow:

Collect object + action, pick the strongest phase (scale peel / shine appears / mirror finish) → build prompt → show parameters and wait for confirmation → run node scripts/video_gen.js wait --json '…'.

Full parameters are shown before generate; wait for confirmation.

Parameters:

FieldValue
modelKLING_V3_0_PRO
aspect_ratio9:16
duration5
generate_audiotrue

Expanded prompt: Compose at generation time per ## Prompt expansion (mandatory) from the user's actual brief—do not reuse fixed sample paragraphs.

Expected outcome: Rust vs. shine boundary is the main hook; grind path + wheel ASMR; mirror reflection as emotional peak.


Rusty image → restoration motion

Provide a photo of a rusty object; generate motion from rusted surface to bright metal. Good for turning spare junk photos into content.

Prefer a public https image URL. If the assembled scripts/video_gen.js supports local file paths, review/verify the script and explicitly consent before local read-and-upload to WeryAI.

After the URL, optionally name the phase (grind / polish / oil); if omitted, pick the phase with strongest rust→clean contrast from the image.

Flow:

Confirm URL, infer object and rust level, choose angle for max contrast → show parameters and wait → run node scripts/video_gen.js wait --json '…' (JSON includes image).

Full parameters are shown before generate; wait for confirmation.

Parameters:

FieldValue
modelKLING_V3_0_PRO
aspect_ratio9:16
duration5
generate_audiotrue
imageUser image URL

Sample prompt:

Starting from the rusty metal object in the image, a grinding wheel or abrasive pad moves methodically across the corroded surface, the rust layer is stripped away in one continuous slow-motion pass at 240fps revealing the bright original metal beneath, the clean-rust boundary line advances steadily through frame. Camera angle and framing match the image composition, single directional side light rakes across the surface to maximize texture contrast between corroded and polished areas. Rust powder and fine metallic sparks scatter in the air. ASMR grinding and metal friction sound.

Expected outcome: Grind direction matches rust coverage in the upload; strong return of true metal color.


Prompt tips

Rust texture

  • Rust surface: heavy orange-brown corrosion pitting, flaking rust scale, uneven oxidation crust, rust powder suspended in air
  • Grind advance: clean-rust boundary advances, bright metal exposed millimeter by millimeter, grinding path reveals polished steel
  • Polish finish: mirror finish emerges, reflection sharpens as polishing progresses, surface transforms from matte to specular

Common notes

  • Smaller objects (lighter / knife): tighter macro—add extreme close-up macro
  • Full arc (derust → polish → assembly): use duration 10 and say so at confirmation
  • Prefer public https URLs; private hosts or in-app-only links may fail. If the runtime supports local paths, review scripts/video_gen.js and explicitly consent before local read-and-upload to WeryAI.
Note: KLING_V3_0_PRO supports negative_prompt to exclude unwanted hands, tool occlusion, etc.—mention at confirmation if you want it added.

适合场景

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能力概览

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补充不同宿主或平台的使用分布数据

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。

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