Skill: dsewell-583h0/text-to-video-for-studying (version 1.0.0)

--- name: text-to-video-for-studying version: "1.0.0" displayName: "Text to Video for Studying — Convert Study Notes into Videos" description: > Get study explainer videos ready to post, without touching a single slider. Upload your text notes (TXT, DOCX, PDF, MD, up to 50MB), say something like "turn my chapter 5 notes into a short explainer video with visuals and narration", and download 1080p MP4 when it's done. Built for students who move fast and need to turn dense notes into watchable video summaries for easier revision. metadata: {"openclaw": {"emoji": "📚", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "short_prompts"}} --- ## Getting Started > Share your text notes and I'll get started on AI video creation. Or just tell me what you're thinking. **Try saying:** - "convert my text notes" - "export 1080p MP4" - "turn my chapter 5 notes into" ### 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. # Text to Video for Studying — Convert Study Notes into Videos Drop your text notes in the chat and tell me what you need. I'll handle the AI video creation on cloud GPUs — you don't need anything installed locally. Here's a typical use: you send a a 300-word biology study guide, ask for turn my chapter 5 notes into a short explainer video with visuals and narration, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default. One thing worth knowing — shorter note sections under 200 words produce more focused and accurate video segments. ## Matching Input to Actions User prompts referencing text to video for studying, 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. Skill attribution — read from this file's YAML frontmatter at runtime: - `X-Skill-Source`: `text-to-video-for-studying` - `X-Skill-Version`: from frontmatter `version` - `X-Skill-Platform`: detect from install path (`~/.clawhub/` → `clawhub`, `~/.cursor/skills/` → `cursor`, else `unknown`) Every API call needs `Authorization: Bearer <NEMO_TOKEN>` plus the three attribution headers above. If any header is missing, exports return 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 ### 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. ### 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 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 "turn my chapter 5 notes into a short explainer video with visuals and narration" — concrete instructions get better results. Max file size is 50MB. Stick to TXT, DOCX, PDF, MD for the smoothest experience. Export as MP4 for widest compatibility across phones, tablets, and learning platforms. ## Common Workflows **Quick edit**: Upload → "turn my chapter 5 notes into a short explainer video with visuals and narration" → 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: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)