Ai Image Ai

generate images or prompts into AI-generated videos with this skill. Works with JPG, PNG, WEBP, MP4 files up to 200MB. content creators, marketers use it for...

Security Scan

The skill's behavior (auto-creating and using a backend token, reading install paths, and hidden token handling) generally matches an image→video service, but there are a few incoherencies and transparency concerns you should review before installing.

Purpose & Capability

The declared purpose (convert images/prompts into videos) matches the API endpoints and flows described in SKILL.md. The skill targets a single third-party backend (mega-api-prod.nemovideo.ai), which is coherent with the stated functionality.

Instruction Scope

Runtime instructions stay within the conversion/rendering scope (upload files, start sessions, read SSE for progress). However the instructions also (a) automatically obtain an anonymous NEMO token if one isn't supplied, (b) tell the agent not to show raw API responses or token values to the user, and (c) read the skill frontmatter and detect local install paths for attribution headers — these are operational choices that reduce transparency and broaden what the agent reads beyond just uploaded files.

Install Mechanism

No install spec and no code files — the skill is instruction-only, so it does not download or install third-party binaries or archives. This is the lower-risk install pattern.

Credentials

The skill declares a single primary credential (NEMO_TOKEN), which is appropriate for the backend. But there's an inconsistency: the registry metadata lists NEMO_TOKEN as required, while SKILL.md documents an automatic anonymous-token acquisition flow if no token is present. Also the SKILL.md frontmatter references a config path (~/.config/nemovideo/) even though the registry summary listed no required config paths — another mismatch to verify.

Persistence & Privilege

always:false and normal autonomous invocation are used. The skill asks to store a session_id and use tokens for requests, which is expected; it does not request elevated or system-wide privileges or modify other skills.

What to consider before installing

This skill talks to a third-party backend (mega-api-prod.nemovideo.ai) to render videos and will either use a provided NEMO_TOKEN or automatically request an anonymous token and keep it for session use. Before installing, verify the service origin and privacy terms (what happens to uploaded images and generated videos). Note the registry declares NEMO_TOKEN as required but the skill can create an anonymous token — ask the publisher which behavior is expected. Also confirm where tokens and session IDs are stored and how long uploads/results are retained. Avoid uploading sensitive images to an unverified backend, and consider whether you’re comfortable letting the agent contact that external endpoint and hide raw API responses from users.

Getting Started

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

Try saying:

• "generate three product photos or a text description into a 1080p MP4"
• "turn my product images into a promotional video with transitions"
• "generating videos from AI-created or uploaded images for content creators, marketers"

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.

AI Image to AI Video — Convert Images Into Videos

This tool takes your images or prompts and runs AI image generation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have three product photos or a text description and want to turn my product images into a promotional video with transitions — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: using high-resolution images produces sharper output video frames.

Matching Input to Actions

User prompts referencing ai image 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.

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

• X-Skill-Source: ai-image-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

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn my product images into a promotional video with transitions" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, WEBP, MP4 for the smoothest experience.

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "turn my product images into a promotional video with transitions" → Download MP4. Takes 30-60 seconds 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.