Image To Video Miricanvas

Turn three product photos in JPG format into 1080p animated video clips just by typing what you need. Whether it's converting static images into shareable vi...

Security Scan

The skill mostly matches its stated purpose (upload images, call a cloud render API) but contains several instructions and metadata that are unnecessary or opaque (reads install paths/config, hides tokens/responses), so you should review before installing.

Purpose & Capability

Name/description (image→video) aligns with the runtime instructions and endpoints (upload, render, export). Requesting a single service token (NEMO_TOKEN) is appropriate. However the declared configPaths (~/.config/nemovideo/) and runtime requirement to read install paths (~/.clawhub/, ~/.cursor/skills/) are not justified by the user-facing description and look unnecessary.

Instruction Scope

Instructions direct the agent to obtain/store anonymous tokens, create sessions, upload user images to a remote service, and poll for render results — these are expected. But the skill also instructs reading the skill's YAML frontmatter and detecting install paths in the user's home directory for attribution, and explicitly tells the agent not to display raw API responses or token values to users. Hiding responses/tokens reduces transparency; filesystem probing of home paths is broader scope than the documented image->video task and may access user state unnecessarily.

Install Mechanism

No install script or external downloads — instruction-only skill. This minimizes supply-chain risk.

Credentials

Only one credential (NEMO_TOKEN) is declared as required and is the primary credential, which fits the service integration. The skill's metadata also lists a config path (~/.config/nemovideo/) that is not referenced meaningfully in the instructions — this is unnecessary permission scope and should be justified or removed.

Persistence & Privilege

always:false and normal autonomous invocation. The skill asks to store session_id for ongoing requests (ephemeral session state) but does not request permanent always-on presence or modify other skills. No elevated privileges requested.

What to consider before installing

This skill will upload your images to a third-party backend (mega-api-prod.nemovideo.ai) to render videos and will create or use an NEMO_TOKEN for authentication. Before installing: (1) Confirm you are willing to upload content to that domain and review its privacy/terms externally, (2) be aware the skill instructs the agent to auto-generate anonymous tokens and to hide raw API responses/tokens from users — this reduces transparency, (3) ask the publisher why the skill declares access to ~/.config/nemovideo/ and why it probes install paths in your home directory; that access may be unnecessary, and (4) because the skill source/homepage are unknown, prefer caution — only install if you trust the skill owner or can verify the backend and data handling policies. If you need, request the skill author to remove the unused configPath requirement and to make token/session handling transparent (e.g., ask to prompt the user before uploading and to show request metadata).

Getting Started

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

Try saying:

• "convert my static images"
• "export 1080p MP4"
• "turn my images into a smooth"

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.

Image to Video MiriCanvas — Convert Images into Video

Drop your static images 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 three product photos in JPG format, ask for turn my images into a smooth video slideshow with transitions, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — using fewer images with longer durations per image produces smoother results.

Matching Input to Actions

User prompts referencing image to video miricanvas, 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.

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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

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

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.

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

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

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.

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 images into a smooth video slideshow with transitions" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across social platforms.

Common Workflows

Quick edit: Upload → "turn my images into a smooth video slideshow 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.