Ai Image To Video Adobe

Skip the learning curve of professional editing software. Describe what you want — turn these images into a smooth video with transitions and motion effects...

Security Scan

The skill's declared purpose (convert images to video) matches most of its instructions, but there are small inconsistencies and privacy-relevant behaviors you should understand before installing.

Purpose & Capability

The skill is an instruction-only connector to a cloud video-rendering backend (mega-api-prod.nemovideo.ai). Requesting a NEMO_TOKEN to call that API is consistent with the stated purpose. However, the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) for metadata while the registry metadata reported 'Required config paths: none' — that mismatch is an incoherence to clarify.

Instruction Scope

The runtime instructions direct the agent to upload user images and polling/push to a third‑party API, create sessions, use SSE, and potentially read the skill's YAML frontmatter and detect the agent install path to set attribution headers. Uploading user images to a remote service is expected for this skill, but reading install paths and the file's frontmatter is extra filesystem access that should be justified. The instructions also include an automatic anonymous-token acquisition flow if NEMO_TOKEN is absent, which means the agent will contact that external endpoint even without an explicit user token.

Install Mechanism

Instruction-only skill with no install spec or shipped code — minimal install risk (nothing is written to disk by an installer).

Credentials

Only a single credential (NEMO_TOKEN) is declared, which is proportional for a cloud API client. The skill also includes logic to request an anonymous token from the same service when no token is present; this is reasonable but means the agent can obtain and use service credentials automatically. The earlier-mentioned config path in frontmatter suggests optional access to a local config directory that wasn't declared in the registry metadata and should be clarified.

Persistence & Privilege

The skill does not request 'always: true' and has no install hooks; it does not request elevated persistence or to modify other skills. Autonomous invocation is allowed (platform default) but not combined with other high-risk privileges.

What to consider before installing

This skill connects to a third‑party rendering service and will upload whatever images you provide — review the service domain (mega-api-prod.nemovideo.ai) and its privacy/retention policies before sending sensitive images. The skill expects a NEMO_TOKEN but will obtain an anonymous token automatically if you don't provide one; if you prefer control, supply your own token from a trusted account or don't use the skill. Ask the author to explain the discrepancy between the registry (no config paths) and the SKILL.md frontmatter (~/.config/nemovideo/) and why the skill needs to detect the agent install path and read its own frontmatter. If you require strict confidentiality or auditability, avoid using instruction-only skills that upload data to external services without clear provenance.

Getting Started

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

Try saying:

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

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

AI Image to Video Adobe — Convert Images Into Video Clips

Drop your still 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 these images into a smooth video with transitions and motion effects, 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 — high-contrast images with clear subjects produce the smoothest motion effects.

Matching Input to Actions

User prompts referencing ai image to video adobe, 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.

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

• X-Skill-Source: ai-image-to-video-adobe
• 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.

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.

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)

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

Common Workflows

Quick edit: Upload → "turn these images into a smooth video with transitions and motion effects" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn these images into a smooth video with transitions and motion effects" — 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 social platforms and editing tools.