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Ai Video Editor Change Clothes

作者 vynbosserman65 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
1
版本数
在 OpenClaw 中安装
/install ai-video-editor-change-clothes
功能描述
Get reclothed video clips ready to post, without touching a single slider. Upload your video footage (MP4, MOV, AVI, WebM, up to 500MB), say something like "...
使用说明 (SKILL.md)

Getting Started

Send me your video footage and I'll handle the AI outfit replacement. Or just describe what you're after.

Try saying:

  • "edit a 30-second walkthrough video of a person in casual clothes into a 1080p MP4"
  • "change the outfit in my video to a formal suit and keep all movements natural"
  • "swapping clothing on video subjects without reshooting for content creators, fashion marketers, TikTok creators"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer \x3Ctoken>, Content-Type: application/json, and body {"task_name":"project","language":"\x3Cdetected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

AI Video Editor Change Clothes — Swap Outfits in Any Video

Drop your video footage in the chat and tell me what you need. I'll handle the AI outfit replacement on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 30-second walkthrough video of a person in casual clothes, ask for change the outfit in my video to a formal suit and keep all movements natural, and about 1-3 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips with minimal background movement produce the most accurate clothing swaps.

Matching Input to Actions

User prompts referencing ai video editor change clothes, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says... Action Skip SSE?
"export" / "导出" / "download" / "send me the video" → §3.5 Export
"credits" / "积分" / "balance" / "余额" → §3.3 Credits
"status" / "状态" / "show tracks" → §3.4 State
"upload" / "上传" / user sends file → §3.2 Upload
Everything else (generate, edit, add BGM…) → §3.1 SSE

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

All calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

  1. SessionPOST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"\x3Clang>"}. Gives you a session_id.
  2. Chat (SSE)POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
  3. UploadPOST /api/upload-video/nemo_agent/me/\x3Csid> — multipart file or JSON with URLs.
  4. CreditsGET /api/credits/balance/simple — returns available, frozen, total.
  5. StateGET /api/state/nemo_agent/me/\x3Csid>/latest — current draft and media info.
  6. ExportPOST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s for completed status and download URL.

Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Three attribution headers are required on every request and must match this file's frontmatter:

Header Value
X-Skill-Source ai-video-editor-change-clothes
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

All requests must include: Authorization: Bearer \x3CNEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says You do
"click [button]" / "点击" Execute via API
"open [panel]" / "打开" Query session state
"drag/drop" / "拖拽" Send edit via SSE
"preview in timeline" Show track summary
"Export button" / "导出" Execute export workflow

SSE Event Handling

Event Action
Text response Apply GUI translation (§4), present to user
Tool call/result Process internally, don't forward
heartbeat / empty data: Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Error Codes

  • 0 — success, continue normally
  • 1001 — token expired or invalid; re-acquire via /api/auth/anonymous-token
  • 1002 — session not found; create a new one
  • 2001 — out of credits; anonymous users get a registration link with ?bind=\x3Cid>, registered users top up
  • 4001 — unsupported file type; show accepted formats
  • 4002 — file too large; suggest compressing or trimming
  • 400 — missing X-Client-Id; generate one and retry
  • 402 — free plan export blocked; not a credit issue, subscription tier
  • 429 — rate limited; wait 30s and retry once

Common Workflows

Quick edit: Upload → "change the outfit in my video to a formal suit and keep all movements natural" → Download MP4. Takes 1-3 minutes for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "change the outfit in my video to a formal suit and keep all movements natural" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 for widest compatibility across social platforms.

安全使用建议
This skill appears to be a cloud video-editing connector that uses a single service token (NEMO_TOKEN) and makes network calls to mega-api-prod.nemovideo.ai. Before installing or using it: (1) Verify the service and publisher — there is no homepage or known owner provided. If you value provenance, ask the publisher for a website or docs. (2) Decide whether to supply your own NEMO_TOKEN (from an account you control) rather than letting the skill obtain an anonymous token and store it for you. That prevents unexpected tokens from being generated and used. (3) Ask where the skill will store the session/token (environment variable, user config file like ~/.config/nemovideo/, or elsewhere) and how long it will persist; avoid secrets being written to shared config locations. (4) Be cautious about privacy: uploaded videos may contain faces and sensitive content — confirm the service's retention, sharing, and deletion policy. (5) The SKILL.md tells the agent not to show raw API responses or token values — treat this as a red flag and request that responses containing request/response metadata be visible to you (or at least auditable). (6) If you need higher assurance, request an official homepage, privacy policy, or sample API docs, and avoid providing unrelated credentials. If the publisher cannot clarify these points, treat the skill as untrusted and do not upload sensitive media or credentials.
功能分析
Type: OpenClaw Skill Name: ai-video-editor-change-clothes Version: 1.0.0 The skill bundle provides instructions for an AI agent to interface with a legitimate-appearing video processing API (nemovideo.ai). It outlines standard procedures for authentication, session management, and file uploads required for AI-driven video editing. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found in SKILL.md or _meta.json.
能力标签
crypto
能力评估
Purpose & Capability
Name/description align with the declared requirement for a single service token (NEMO_TOKEN) and cloud rendering via nemovideo.ai endpoints. However, the SKILL.md frontmatter includes a configPaths entry (~/.config/nemovideo/) while the registry metadata lists no required config paths — that inconsistency should be clarified. The lack of a homepage or known publisher reduces confidence in the claimed purpose.
Instruction Scope
Runtime instructions tell the agent to call specific nemovideo.ai endpoints (anonymous token, create session, upload, SSE chat, export). They do not instruct access to unrelated files or credentials, which is good. Concerns: (1) The skill instructs the agent to auto-generate and persist a token if NEMO_TOKEN is not present — the storage location and persistence semantics are not specified. (2) It explicitly tells the agent not to display raw API responses or token values to the user, which can hide potentially important information about what was sent/returned. (3) The instructions require adding attribution headers and auto-detecting platform from install path (this implies some local path inspection).
Install Mechanism
No install spec and no code files (instruction-only). This is the lowest-risk install footprint — nothing is written to disk by an installer in the package. Risk comes from the network calls the agent will perform at runtime, not from an installer.
Credentials
Only one declared credential (NEMO_TOKEN) which is appropriate for a cloud-rendering service. That is proportionate. Minor inconsistency: SKILL.md metadata also lists a config path (~/.config/nemovideo/) that is not reflected in the registry 'Required config paths' field; clarify whether the skill will read/write that path. Also note the instruction to obtain an anonymous token autonomously if none is set — this means the skill may create and store credentials without explicit user action.
Persistence & Privilege
always:false and no install-time persistence are appropriate. The skill will ask to store a session_id/token for requests (normal for a session-based API) but does not request permanent privileged presence or to modify other skills. No 'always' privilege or other elevated capabilities were requested.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ai-video-editor-change-clothes
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-editor-change-clothes 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of AI Video Editor Change Clothes. - Swap outfits in any uploaded video (MP4, MOV, AVI, WebM up to 500MB) using AI-powered clothing replacement. - Simple workflow: upload your clip, describe the desired outfit, and receive a 1080p MP4 in 1–3 minutes. - Designed for content creators, marketers, and TikTok users to change appearance without reshooting. - Automatic backend session handling, authentication, and export via cloud GPU. - Supports batch processing, iterative edits, credit tracking, and user-friendly error messages. - Detailed onboarding and tips included for best results.
元数据
Slug ai-video-editor-change-clothes
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ai Video Editor Change Clothes 是什么?

Get reclothed video clips ready to post, without touching a single slider. Upload your video footage (MP4, MOV, AVI, WebM, up to 500MB), say something like "... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 63 次。

如何安装 Ai Video Editor Change Clothes?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-video-editor-change-clothes」即可一键安装,无需额外配置。

Ai Video Editor Change Clothes 是免费的吗?

是的,Ai Video Editor Change Clothes 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ai Video Editor Change Clothes 支持哪些平台?

Ai Video Editor Change Clothes 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 Ai Video Editor Change Clothes?

由 vynbosserman65(@vynbosserman65)开发并维护,当前版本 v1.0.0。

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