Skill: dsewell-583h0/ai-video-editor-eye-contact (version 1.0.0)

--- name: ai-video-editor-eye-contact version: "1.0.0" displayName: "AI Video Editor Eye Contact — Fix Eye Contact in Videos" description: > correct recorded video footage into eye contact corrected video with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators, remote workers, online educators use it for fixing eye contact in webcam recordings and video calls — processing takes 1-2 minutes 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 recorded video footage and I'll handle the AI eye contact correction. Or just describe what you're after. **Try saying:** - "correct a 3-minute webcam interview recording into a 1080p MP4" - "fix my eye contact so I look directly at the camera instead of the screen" - "fixing eye contact in webcam recordings and video calls for content creators, remote workers, online educators" ### 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 <token>`, `Content-Type: application/json`, and body `{"task_name":"project","language":"<detected>"}`. 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 Eye Contact — Fix Eye Contact in Videos This tool takes your recorded video footage and runs AI eye contact correction through a cloud rendering pipeline. You upload, describe what you want, and download the result. Say you have a 3-minute webcam interview recording and want to fix my eye contact so I look directly at the camera instead of the screen — the backend processes it in about 1-2 minutes and hands you a 1080p MP4. Tip: works best when your face is well-lit and centered in the frame. ## Matching Input to Actions User prompts referencing ai video editor eye contact, 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 requests** must include: `Authorization: Bearer <NEMO_TOKEN>`, `X-Skill-Source`, `X-Skill-Version`, `X-Skill-Platform`. Missing attribution headers will cause export to fail with 402. Headers are derived from this file's YAML frontmatter. `X-Skill-Source` is `ai-video-editor-eye-contact`, `X-Skill-Version` comes from the `version` field, and `X-Skill-Platform` is detected from the install path (`~/.clawhub/` = `clawhub`, `~/.cursor/skills/` = `cursor`, otherwise `unknown`). **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. ### 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=<id>`, 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 ### Translating GUI Instructions The backend responds as if there's a visual interface. Map its instructions to API calls: - "click" or "点击" → execute the action via the relevant endpoint - "open" or "打开" → query session state to get the data - "drag/drop" or "拖拽" → send the edit command through SSE - "preview in timeline" → show a text summary of current tracks - "Export" or "导出" → run the export workflow ### Reading the SSE Stream Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty `data:` lines mean the backend is still working — show "⏳ Still working..." every 2 minutes. About 30% of edit operations close the stream without any text. When that happens, poll `/api/state` to confirm the timeline changed, then tell the user what was updated. **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) ``` ## Tips and Tricks The backend processes faster when you're specific. Instead of "make it look better", try "fix my eye contact so I look directly at the camera instead of the screen" — concrete instructions get better results. Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience. Export as MP4 with H.264 codec for the best balance of quality and file size. ## Common Workflows **Quick edit**: Upload → "fix my eye contact so I look directly at the camera instead of the screen" → Download MP4. Takes 1-2 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.


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