Skill: vynbosserman65/video-background-remover-ai (version 1.0.0)

--- name: video-background-remover-ai version: "1.0.0" displayName: "Video Background Remover AI — Remove Backgrounds from Videos" description: > remove video files into clean background videos with this video-background-remover-ai skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators use it for removing video backgrounds — processing takes 30-60 seconds on cloud GPUs and you get 1080p MP4 files. metadata: {"openclaw": {"emoji": "🎬", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "greeting_v2"}} --- ## Getting Started > Send me your video files and I'll remove it. No file? Just describe what you need. **Try saying:** - "remove background from my talking head video" - "replace backdrop with office setting" - "cut out the background and make it transparent" ### Getting Connected Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...". **If `NEMO_TOKEN` is in the environment**, use it directly and create a session. Otherwise, acquire a free starter token: - Generate a UUID as client identifier - POST to `https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token` with the `X-Client-Id` header - The response includes a `token` with 100 free credits valid for 7 days — use it as NEMO_TOKEN **Then create a session** by POSTing to `https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent` with Bearer authorization and body `{"task_name":"project","language":"en"}`. The `session_id` in the response is needed for all following requests. Tell the user you're ready. Keep the technical details out of the chat. # How Video Background Remover Ai Works Here Drop your video files in the chat and tell me what you need. I'll handle the AI background removal on cloud GPUs — you don't need anything installed locally. Here's a typical run: you send a a 20-second talking-head clip, ask for remove the background and replace it with a solid color, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default. One thing worth knowing — works best with good lighting and minimal camera movement. ## Matching Input to Actions Each message you send gets routed to the right handler based on what you're asking for. | 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 | ❌ | ## Under the Hood Your video background remover ai requests hit a cloud rendering backend with GPU acceleration. Files get processed server-side so your machine doesn't need any special hardware. Skill attribution — read from this file's YAML frontmatter at runtime: - `X-Skill-Source`: `video-background-remover-ai` - `X-Skill-Version`: from frontmatter `version` - `X-Skill-Platform`: detect from install path (`~/.clawhub/` → `clawhub`, `~/.cursor/skills/` → `cursor`, else `unknown`) Include `Authorization: Bearer <NEMO_TOKEN>` and all attribution headers on every request — omitting them triggers a 402 on export. **API base**: `https://mega-api-prod.nemovideo.ai` **Create session**: POST [REDACTED_SECRET] — body `{"task_name":"project","language":"<lang>"}` — returns `task_id`, `session_id`. **Send message (SSE)**: POST `/run_sse` — body `{"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}}` with `Accept: text/event-stream`. Max timeout: 15 minutes. **Upload**: POST `/api/upload-video/nemo_agent/me/<sid>` — file: multipart `-F "files=@/path"`, or URL: `{"urls":["<url>"],"source_type":"url"}` **Credits**: GET `/api/credits/balance/simple` — returns `available`, `frozen`, `total` **Session state**: GET `/api/state/nemo_agent/me/<sid>/latest` — key fields: `data.state.draft`, `data.state.video_infos`, `data.state.generated_media` **Export** (free, no credits): POST `/api/render/proxy/lambda` — body `{"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}`. Poll GET `/api/render/proxy/lambda/<id>` every 30s until `status` = `completed`. Download URL at `output.url`. Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac. ### 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. ### 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 | Draft JSON uses short keys: `t` for tracks, `tt` for track type (0=video, 1=audio, 7=text), `sg` for segments, `d` for duration in ms, `m` for metadata. Example timeline summary: ``` Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s) ``` ### Error Handling | Code | Meaning | Action | |------|---------|--------| | 0 | Success | Continue | | 1001 | Bad/expired token | Re-auth via anonymous-token (tokens expire after 7 days) | | 1002 | Session not found | New session §3.0 | | 2001 | No credits | Anonymous: show registration URL with `?bind=<id>` (get `<id>` from create-session or state response when needed). Registered: "Top up credits in your account" | | 4001 | Unsupported file | Show supported formats | | 4002 | File too large | Suggest compress/trim | | 400 | Missing X-Client-Id | Generate Client-Id and retry (see §1) | | 402 | Free plan export blocked | Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export." | | 429 | Rate limit (1 token/client/7 days) | Retry in 30s once | ## Best Practices Start with short clips when testing. A 15-second video files processes fast and lets you dial in the look before committing to a full-length project. Always check the preview before exporting. You can tweak settings and re-render without using extra credits. H.264 MP4 gives the best balance of quality and file size. ## Common Workflows **From scratch**: Describe what you want and the AI generates a draft. You refine from there. **Polish existing content**: Upload your clip files, ask for specific changes — remove the background and replace it with a solid color, adjust colors, swap music. The backend handles rendering. **Export ready**: Once you're happy, export at 1080p in MP4. File lands in your downloads.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)