text-to-video-explainer-ai

Skip the learning curve of professional editing software. Describe what you want — turn this script into an animated explainer video with voiceover and subtitles — and get animated explainer videos back in 1-2 minutes. Upload TXT, DOCX, PDF, SRT files up to 50MB, and the AI handles AI video creation automatically. Ideal for marketers, educators, startup founders who want explainer videos without hiring animators or video editors.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "text-to-video-explainer-ai" with this command: npx skills add peand-rover/text-to-video-explainer-ai

Getting Started

Share your text script and I'll get started on AI video creation. Or just tell me what you're thinking.

Try saying:

  • "convert my text script"
  • "export 1080p MP4"
  • "turn this script into an animated"

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.

Text to Video Explainer AI — Convert Scripts into Explainer Videos

Send me your text script and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 200-word product explainer script, type "turn this script into an animated explainer video with voiceover and subtitles", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter scripts under 150 words produce tighter, more focused explainer videos.

Matching Input to Actions

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

User says...ActionSkip 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

EndpointMethodPurpose
/api/tasks/me/with-session/nemo_agentPOSTStart a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_ssePOSTSend 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>POSTUpload a file (multipart) or URL.
/api/credits/balance/simpleGETCheck remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latestGETFetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambdaPOSTStart 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-explainer-ai
  • 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

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess 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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn this script into an animated explainer video with voiceover and subtitles" — concrete instructions get better results.

Max file size is 50MB. Stick to TXT, DOCX, PDF, SRT for the smoothest experience.

Export as MP4 for widest compatibility across YouTube, LinkedIn, and presentations.

Common Workflows

Quick edit: Upload → "turn this script into an animated explainer video with voiceover and subtitles" → 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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

gitlab-mr-reviewer

当需要审核 GitLab 合并请求、检查 MR diff 风险、发布 GitLab 审查评论、执行 approve/request changes,或发送 MR 审查通知时使用。

Registry SourceRecently Updated
1490whrime
General

Voice Transcriber Toolkit

Voice-to-Text Transcription Toolkit - 语音识别转文字,支持Whisper/Vosk引擎,批量处理,字幕导出 | Speech recognition & transcription with Whisper/Vosk engines, batch processing, su...

Registry SourceRecently Updated
General

Gigo Lobster Taster

🦞 GIGO · gigo-lobster-taster: 正式试吃模式:跑完整评测,默认上传云端、生成个人结果页并进入排行榜。 Triggers: 试吃我的龙虾 / 品鉴我的龙虾 / lobster taste / lobster taster.

Registry SourceRecently Updated
General

Gigo Lobster Local

🦞 GIGO · gigo-lobster-local: 本地模式:跑完整评测,但不上云、不注册个人结果页,证书二维码回到官网首页。 Triggers: 本地试吃龙虾 / 离线试吃龙虾 / local lobster taste / offline lobster taste.

Registry SourceRecently Updated