Image To Video Jiggle

Turn a single portrait photo or product image into 1080p looping animated clips just by typing what you need. Whether it's turning static photos into jigglin...

Security Scan

The skill's requirements and runtime instructions are coherent with its stated purpose (animating images via a cloud API), but there are a few small metadata inconsistencies and privacy/usage considerations you should review before installing.

Purpose & Capability

Name and description match the actions described in SKILL.md (upload image, create session, request render, download MP4). Required credential (NEMO_TOKEN) is appropriate for a cloud API. Minor inconsistency: SKILL.md declares a config path (~/.config/nemovideo/) in its frontmatter, but the registry metadata lists no required config paths.

Instruction Scope

The instructions remain focused on the image-to-video flow (session creation, SSE for edits, upload endpoint, render endpoint). They explicitly instruct generating an anonymous token if no NEMO_TOKEN is present and uploading user files to the remote service — this is expected but important to note because user images are sent to an external API. The SKILL.md does not mention data retention, privacy, or allowed content.

Install Mechanism

Instruction-only skill with no install spec and no code files — lowest filesystem/execution risk. All network activity is via the described HTTPS API endpoints.

Credentials

Only NEMO_TOKEN is requested as an environment credential, which is proportionate. The SKILL.md frontmatter also lists a config path (~/.config/nemovideo/) — registry metadata omitted that, which is an inconsistency to be aware of. The skill will also create/hold a session_id and may call an anonymous-token endpoint on your behalf if no token exists.

Persistence & Privilege

The skill does not request always:true or system-wide changes. It requires normal session state for rendering but does not ask to modify other skills or system-wide configs.

Assessment

This skill appears to do what it says: it uploads images to nemovideo's cloud API, creates a session, renders a looping MP4, and returns a download URL. Before installing: 1) Be aware that any image you upload will be sent to https://mega-api-prod.nemovideo.ai — review the service's privacy/retention policy or avoid uploading sensitive images. 2) NEMO_TOKEN is the main credential: only provide a scoped or disposable token if possible; the skill will also generate an anonymous token if none is provided. 3) Note the small metadata mismatch (SKILL.md mentions a config path that registry metadata does not); this is likely benign but worth verifying. 4) The skill is instruction-only (no local install), so its primary risk is remote data handling — verify the domain and prefer temporary tokens or explicit consent for uploads. If you need higher assurance, ask the publisher for a homepage/privacy policy or test with non-sensitive images and a disposable token first.

Getting Started

Got still images to work with? Send it over and tell me what you need — I'll take care of the AI motion animation.

Try saying:

• "animate a single portrait photo or product image into a 1080p MP4"
• "make this photo jiggle and bounce like a looping animated video"
• "turning static photos into jiggling motion videos for social media for TikTok creators"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

• Generate a UUID as client identifier
• POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
• The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

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.

Image to Video Jiggle — Animate Photos into Motion Clips

This tool takes your still images and runs AI motion animation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a single portrait photo or product image and want to make this photo jiggle and bounce like a looping animated video — the backend processes it in about 20-40 seconds and hands you a 1080p MP4.

Tip: images with clear subjects and simple backgrounds produce the cleanest jiggle effect.

Matching Input to Actions

User prompts referencing image to video jiggle, 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	image-to-video-jiggle
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.

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 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 "make this photo jiggle and bounce like a looping animated video" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across TikTok, Instagram, and YouTube Shorts.

Common Workflows

Quick edit: Upload → "make this photo jiggle and bounce like a looping animated video" → Download MP4. Takes 20-40 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.