Ai Video Maker Ai

Turn five product images and a logo file into 1080p AI-generated videos just by typing what you need. Whether it's creating videos from images or clips using...

Security Scan

The skill's runtime instructions largely match an AI video-rendering integration and only need a NEMO_TOKEN and network access, but there are inconsistencies (notably a config path declared in the SKILL.md frontmatter that isn't in the registry metadata) and a few scope/persistence details you should verify before installing.

Purpose & Capability

Name and description match the actions described (upload images/clips, create renders, check credits, export). Requesting a NEMO_TOKEN and calling nemovideo.ai endpoints is proportionate. However, SKILL.md frontmatter lists a config path (~/.config/nemovideo/) that the registry metadata did not declare, which is an inconsistency to clarify.

Instruction Scope

Instructions direct the agent to obtain/use a bearer token (NEMO_TOKEN or anonymous-token flow), create sessions, upload files, start renders, poll SSE, and save session_id. Those are all within the stated video-creation purpose. The SKILL.md requires attribution headers tied to the skill frontmatter and asks the agent to 'auto-detect' platform/install path — this implies the agent may read install path metadata. No instructions request unrelated files or other credentials.

Install Mechanism

Instruction-only skill with no install spec and no code files. Lowest install risk: nothing is downloaded or written by an installer step in the package.

Credentials

Only one environment credential is declared (NEMO_TOKEN / primaryEnv). That aligns with needing an API bearer token for the external service. The SKILL.md also supports an anonymous token flow (no secret), which reduces credential exposure. No unrelated credentials are requested.

Persistence & Privilege

The SKILL.md frontmatter indicates use of ~/.config/nemovideo/ (config path) for session/token storage, but the registry metadata shows no required config paths — a mismatch. The instructions also say to save session_id and use tokens for subsequent calls, implying local persistence. Confirm where session tokens are stored and what is written to disk before installing.

What to consider before installing

What to check before installing: 1) Confirm the config-path mismatch — ask the author whether the skill will read/write ~/.config/nemovideo/ (and what exactly is stored there). 2) Verify the external domain (mega-api-prod.nemovideo.ai) is the intended upstream and acceptable for your data; uploaded media will go to their cloud GPUs. 3) If you prefer not to store long-lived tokens locally, use the anonymous-token flow and verify how/where that token/session is persisted and how long it lives. 4) Be cautious about uploading sensitive images or proprietary assets to an external service. 5) Check privacy/retention and billing (credits/subscription) behavior — SKILL.md references credit limits and potential paid tiers. 6) If anything is unclear or you cannot verify the config/storage behavior, treat the skill as untrusted or revoke tokens after use.

Getting Started

Got images or clips to work with? Send it over and tell me what you need — I'll take care of the AI video creation.

Try saying:

• "create five product images and a logo file into a 1080p MP4"
• "turn these images into a 30-second promo video with music and text overlays"
• "creating videos from images or clips using AI automation for marketers and 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: <uuid>. 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.

AI Video Maker AI — Create Videos with AI Automation

Drop your images or clips 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 five product images and a logo file, ask for turn these images into a 30-second promo video with music and text overlays, 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 — using fewer than 10 images speeds up generation significantly.

Matching Input to Actions

User prompts referencing ai video 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
/api/tasks/me/with-session/nemo_agent	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	ai-video-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.

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 JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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 these images into a 30-second promo video with music and text overlays" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, JPG, PNG for the smoothest experience.

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "turn these images into a 30-second promo video with music and text overlays" → 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.