Skill: vynbosserman65/video-lecture-maker-ai (version 1.0.0)

--- name: video-lecture-maker-ai version: "1.0.0" displayName: "AI Video Lecture Maker — Turn Slides Into Lecture Videos" description: > Get narrated lecture videos ready to post, without touching a single slider. Upload your slides or scripts (PDF, PPTX, MP4, MOV, up to 500MB), say something like "turn my slides into a narrated lecture video with AI voiceover", and download 1080p MP4 when it's done. Built for educators and online course creators who move fast and want to produce lecture videos without recording themselves on camera. metadata: {"openclaw": {"emoji": "🎓", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "greeting_v2"}} --- ## Getting Started > Ready when you are. Drop your slides or scripts here or describe what you want to make. **Try saying:** - "create a 20-slide PowerPoint presentation on biology into a 1080p MP4" - "turn my slides into a narrated lecture video with AI voiceover" - "converting presentation slides into AI-narrated lecture videos for educators and online course creators" ### 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. # AI Video Lecture Maker — Turn Slides Into Lecture Videos Send me your slides or scripts and describe the result you want. The AI lecture video creation runs on remote GPU nodes — nothing to install on your machine. A quick example: upload a 20-slide PowerPoint presentation on biology, type "turn my slides into a narrated lecture video with AI voiceover", and you'll get a 1080p MP4 back in roughly 1-3 minutes. All rendering happens server-side. Worth noting: breaking long lectures into 10-minute segments keeps viewers engaged and speeds up processing. ## Matching Input to Actions User prompts referencing video lecture maker ai, 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. Base URL: `https://mega-api-prod.nemovideo.ai` | Endpoint | Method | Purpose | |----------|--------|---------| | [REDACTED_SECRET] | POST | Start a new editing session. Body: `{"task_name":"project","language":"<lang>"}`. Returns `session_id`. | | `/run_sse` | POST | Send a user message. Body includes `app_name`, `session_id`, `new_message`. Stream response with `Accept: text/event-stream`. Timeout: 15 min. | | `/api/upload-video/nemo_agent/me/<sid>` | POST | Upload a file (multipart) or URL. | | `/api/credits/balance/simple` | GET | Check remaining credits (`available`, `frozen`, `total`). | | `/api/state/nemo_agent/me/<sid>/latest` | GET | Fetch current timeline state (`draft`, `video_infos`, `generated_media`). | | `/api/render/proxy/lambda` | POST | Start export. Body: `{"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}`. Poll status every 30s. | Accepted file types: 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` | `video-lecture-maker-ai` | | `X-Skill-Version` | frontmatter `version` | | `X-Skill-Platform` | auto-detect: `clawhub` / `cursor` / `unknown` from install path | **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. ### 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 ### 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. ### 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 **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) ``` ## Common Workflows **Quick edit**: Upload → "turn my slides into a narrated lecture video with AI voiceover" → 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 "turn my slides into a narrated lecture video with AI voiceover" — concrete instructions get better results. Max file size is 500MB. Stick to PDF, PPTX, MP4, MOV for the smoothest experience. Export as MP4 for widest compatibility with LMS platforms like Moodle or Canvas.


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