Image To Video No Filter

Turn a single product photo or portrait image into 1080p unfiltered video clips just by typing what you need. Whether it's converting still images to video w...

Security Scan

The skill's declared purpose (convert images to video) matches the network calls it describes, but its runtime instructions ask the agent to obtain and persist anonymous credentials and to read installation paths not declared in the metadata — behaviors that deserve review before installing.

Purpose & Capability

Name and description (image→video) align with the endpoints, file types, and rendering pipeline described. Requiring a NEMO_TOKEN and a nemovideo config path is coherent for a hosted render service.

Instruction Scope

The SKILL.md instructs the agent to auto-request an anonymous token, create and store session_id, and to 'detect' platform by reading install paths (e.g., ~/.clawhub, ~/.cursor/skills) to set attribution headers. Reading install paths was not declared in the metadata's configPaths and is out-of-scope for a simple converter; automatic credential creation/persistence is sensitive and not fully specified (where/how to store secrets, and for how long).

Install Mechanism

Instruction-only skill with no install spec or code to download — lowest install risk. All runtime behavior is via network calls to the described API.

Credentials

Only NEMO_TOKEN is declared as required, which fits a hosted service. However, the skill instructs generating an anonymous token when none is present and storing it (the SKILL.md treats this token as NEMO_TOKEN). That implies the skill will create and persist credentials on the host — a capability users should be comfortable with. Also, the skill expects to read install paths (undocumented in requires.configPaths) to populate X-Skill-Platform attribution headers.

Persistence & Privilege

always:false and no claims to modify system-wide settings or other skills. The skill does request storing session_id and possibly the anonymous token, which is normal for a session-based remote service but should be understood by the user.

What to consider before installing

This skill talks to a remote rendering service (mega-api-prod.nemovideo.ai). Before installing: 1) Decide if you trust uploading images to that external service (they run renders on their GPUs). 2) Note the skill will automatically request an anonymous NEMO_TOKEN if you don't provide one and will persist that token/session locally — ask where tokens are saved and how to delete them. 3) The instructions tell the agent to read install paths to set attribution headers; this filesystem access wasn’t listed in the declared configPaths. If you need tighter control, provide your own NEMO_TOKEN (from the vendor) instead of allowing anonymous token creation, and confirm how/where tokens and session IDs are stored and expired. If you cannot verify the skill's source or token storage behavior, treat it as potentially risky and avoid installing.

Getting Started

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

Try saying:

• "convert a single product photo or portrait image into a 1080p MP4"
• "convert this image to a video with no filters or effects applied"
• "converting still images to video without any color grading or filters for content creators"

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 No Filter — Convert Images to Video Unfiltered

Send me your images and describe the result you want. The unfiltered video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a single product photo or portrait image, type "convert this image to a video with no filters or effects applied", and you'll get a 1080p MP4 back in roughly 20-40 seconds. All rendering happens server-side.

Worth noting: using a high-resolution source image produces sharper video output with no filter applied.

Matching Input to Actions

User prompts referencing image to video no filter, 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: image-to-video-no-filter
• 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 Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s 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.

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 "convert this image to a video with no filters or effects applied" — 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.

Common Workflows

Quick edit: Upload → "convert this image to a video with no filters or effects applied" → 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.