Aivideomakerai

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.

From Blank Canvas to Finished Video — Fast

Most people have a story to tell but no time to learn video editing. aivideomakerai bridges that gap by turning your raw ideas — a product description, a blog post, a handful of photos — into a structured, visually engaging video ready for social media, presentations, or marketing campaigns.

You describe what you want: the mood, the message, the audience, the length. aivideomakerai maps that into a scene-by-scene plan, suggests visuals, writes on-screen text, and recommends pacing. It's like having a video producer in your pocket who never needs a brief twice.

The skill is built for speed without sacrificing quality. Whether you need a 15-second Instagram reel, a 2-minute explainer, or a product showcase, aivideomakerai gives you a production-ready blueprint — and in supported environments, it can generate the video directly. No prior editing experience required.

Directing Your Video Requests

Every prompt you send — whether a text description, uploaded image, or raw concept — is parsed by AI Video Maker AI's intent engine and routed to the appropriate generation pipeline: text-to-video, image-to-video, or storyboard assembly.

Cloud Rendering Backend Reference

AI Video Maker AI processes all generation jobs on a distributed cloud rendering backend, meaning your video assets are compiled, diffused, and encoded server-side without taxing your local device. Render queues are managed dynamically, so complex multi-scene productions are handled with the same API surface as single-clip requests.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: aivideomakerai
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

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.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — 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

~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:

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)

Error Handling

Best Practices

Be specific about your audience and platform upfront. aivideomakerai produces noticeably better results when you say 'a 15-second TikTok for Gen Z fitness enthusiasts' versus 'a fitness video.' Platform dimensions, tone, and pacing all shift based on that context.

When working with images or existing footage, describe the emotional tone you want — 'energetic and punchy' or 'calm and cinematic' — rather than just listing technical specs. aivideomakerai uses tone cues to make smarter decisions about transitions, text timing, and music style.

Iterate in rounds. Start with a rough concept request, review the scene plan aivideomakerai produces, then refine individual scenes rather than restarting from scratch. This staged approach keeps your creative direction intact while letting the AI handle the structural heavy lifting. Finally, always specify your target video length — it prevents over-stuffed scripts and keeps the final output tight and watchable.

Common Workflows

One of the most popular ways to use aivideomakerai is the idea-to-script-to-video pipeline. You start with a raw concept — say, launching a new product — and aivideomakerai generates a full scene breakdown, voiceover script, and caption suggestions before any footage is touched. This is especially useful for social media managers juggling multiple campaigns.

Another common workflow is the image-to-video conversion. Upload a collection of photos from an event, a product shoot, or a travel trip, and aivideomakerai sequences them into a cohesive video with suggested transitions, title cards, and timing that matches your chosen mood.

For content repurposing, many users paste in a YouTube transcript or article and ask aivideomakerai to reformat it as a short-form vertical video for TikTok or Reels — complete with punchy hooks, text overlays, and call-to-action slides. This workflow alone can save hours of manual reformatting each week.