Maker Ai
/install maker-ai
Getting Started
Ready when you are. Drop your raw footage here or describe what you want to make.
Try saying:
- "create a 2-minute raw screen recording into a 1080p MP4"
- "turn my footage into a polished video with transitions and music"
- "automatically creating edited videos from raw clips for content creators"
Automatic Setup
On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".
Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.
Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: \x3Cuuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).
Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.
Confirm to the user you're connected and ready. Don't print tokens or raw JSON.
Maker AI — Create and Export Finished Videos
Drop your raw footage 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 2-minute raw screen recording, ask for turn my footage into a polished video with transitions and music, 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 source clips under 60 seconds process significantly faster.
Matching Input to Actions
User prompts referencing 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.
Headers are derived from this file's YAML frontmatter. X-Skill-Source is maker-ai, 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).
Include Authorization: Bearer \x3CNEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.
API base: https://mega-api-prod.nemovideo.ai
Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.
Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.
Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}
Credits: GET /api/credits/balance/simple — returns available, frozen, total
Session state: GET /api/state/nemo_agent/me/\x3Csid>/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_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> 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.
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.
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)
Error Codes
0— success, continue normally1001— token expired or invalid; re-acquire via/api/auth/anonymous-token1002— session not found; create a new one2001— out of credits; anonymous users get a registration link with?bind=\x3Cid>, registered users top up4001— unsupported file type; show accepted formats4002— file too large; suggest compressing or trimming400— missingX-Client-Id; generate one and retry402— free plan export blocked; not a credit issue, subscription tier429— rate limited; wait 30s and retry once
Tips and Tricks
The backend processes faster when you're specific. Instead of "make it look better", try "turn my footage into a polished video with transitions and music" — concrete instructions get better results.
Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.
Export as MP4 for widest compatibility across platforms and devices.
Common Workflows
Quick edit: Upload → "turn my footage into a polished video with transitions and music" → 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.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install maker-ai - 安装完成后,直接呼叫该 Skill 的名称或使用
/maker-ai触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Maker Ai 是什么?
create raw footage into polished MP4 videos with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators use it for automatically crea... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 82 次。
如何安装 Maker Ai?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install maker-ai」即可一键安装,无需额外配置。
Maker Ai 是免费的吗?
是的,Maker Ai 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Maker Ai 支持哪些平台?
Maker Ai 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Maker Ai?
由 mhogan2013-9(@mhogan2013-9)开发并维护,当前版本 v1.0.0。