Hug Video Generator Free

generate photos or images into animated hug videos with this skill. Works with JPG, PNG, WEBP, HEIC files up to 200MB. social media users use it for creating...

Security Scan

The skill's instructions mostly match its stated purpose (upload photos to a cloud API to create videos) but there are small inconsistencies and privacy/network behaviors you should be aware of before installing.

Purpose & Capability

Name/description match the runtime instructions (cloud API for generating videos) and the single required env var (NEMO_TOKEN) is consistent with that purpose. However the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata reported no required config paths — this discrepancy is unexplained. Also the skill has no source/homepage listed, which reduces ability to verify the backend service.

Instruction Scope

The SKILL.md directs the agent to: read NEMO_TOKEN if present, otherwise obtain an anonymous token by POSTing to an external API; create sessions; upload user files; stream SSE responses; and poll render status. These actions are coherent with a cloud-rendering video service, but they involve sending user images and metadata to an external host (mega-api-prod.nemovideo.ai). The instructions also say to detect install path to set an X-Skill-Platform header (implies checking filesystem/install path), which is out-of-band for a pure ‘instruction-only’ skill and should be confirmed.

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing is written to disk by the skill itself. This is the lowest install risk.

Credentials

Only one credential is declared (NEMO_TOKEN) and the SKILL.md explains how a temporary anonymous token can be requested if NEMO_TOKEN is absent — appropriate for a cloud API integration. Still, the token grants an external service the ability to process user images; ensure you trust the service before providing a persistent NEMO_TOKEN. The frontmatter's configPaths mention (~/.config/nemovideo/) although the registry did not declare any required config paths — this mismatch should be clarified.

Persistence & Privilege

always is false and the skill does not request privileged system-wide presence or modification of other skills. It will create per-session tokens for cloud jobs and poll status as expected for a render pipeline.

What to consider before installing

This skill will upload your images and interact with an external API (mega-api-prod.nemovideo.ai). Before installing: 1) Verify the service and token source — ask the publisher for a homepage or source repo and confirm the domain is trustworthy. 2) Prefer using a temporary/anonymous token or scoped token rather than a long-lived NEMO_TOKEN. 3) Be aware that user photos are sent to the remote backend; check its privacy/TOS if images are sensitive. 4) Ask the author to resolve the metadata mismatch (SKILL.md lists a config path while registry metadata does not) and to explain why the skill needs to detect install paths for X-Skill-Platform. If you cannot verify the backend or the publisher, treat the skill as untrusted.

Getting Started

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

Try saying:

• "generate my photos or images"
• "export 1080p MP4"
• "generate a video of these two"

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.

Hug Video Generator Free — Generate AI Hug Videos Free

This tool takes your photos or images and runs AI hug video creation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have two portrait photos of people and want to generate a video of these two people hugging each other — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: clear, well-lit face photos produce the most realistic hug animations.

Matching Input to Actions

User prompts referencing hug video generator free, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is hug-video-generator-free, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise 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

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 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 "generate a video of these two people hugging each other" — 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 → "generate a video of these two people hugging each other" → 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.