Free Video Generator Kiss

Get animated kiss videos ready to post, without touching a single slider. Upload your images or clips (JPG, PNG, MP4, MOV, up to 200MB), say something like "...

Security Scan

The skill's declared credential (NEMO_TOKEN) and API workflow mostly match a cloud video renderer, but there are inconsistencies (declared config path in the skill text vs registry, and instructions to probe install/config locations) that warrant caution before installing.

Purpose & Capability

Requesting NEMO_TOKEN and calling a remote rendering API fits the described 'cloud video generator' purpose. However the SKILL.md frontmatter declares a required config path (~/.config/nemovideo/) and asks the agent to detect install paths for attribution headers — the registry metadata above this SKILL shows no required config paths. That mismatch is unexplained and not necessary for the core task.

Instruction Scope

Runtime instructions are mainly API-driven (session creation, SSE, upload, export) which is coherent. But the skill explicitly instructs the agent to detect the install path (~/.clawhub, ~/.cursor/skills/) and to read its own YAML frontmatter and (per frontmatter) a user config path. Probing user home directories or config locations is outside the minimal needs of generating/ uploading the user-supplied media and is privacy‑sensitive.

Install Mechanism

This is an instruction-only skill with no install spec and no code files; nothing is written to disk by an installer. That lowers supply-chain risk.

Credentials

Only NEMO_TOKEN is declared as required which is proportional to calling a protected API. However SKILL.md describes generating an anonymous token and implies saving session_id and possibly storing tokens; combined with the frontmatter-config path (which the registry did not list) this suggests the skill may persist credentials/config locally — the storage location and justification are not clearly explained.

Persistence & Privilege

The skill does not request 'always:true' and uses normal autonomous invocation settings. There is no install-time behavior described that modifies other skills or system-wide settings.

What to consider before installing

This skill largely behaves like a cloud video renderer — it will upload any images/clips you give it to an external API (mega-api-prod.nemovideo.ai) and uses a NEMO_TOKEN API token. Before installing or using: (1) Confirm the skill's source/author (homepage is missing); (2) avoid uploading sensitive or private images/video unless you trust the service; (3) ask the author how and where session tokens/anonymous tokens are stored (the SKILL mentions a config path but registry metadata did not); (4) be aware the skill asks the agent to inspect your home directories to determine 'X-Skill-Platform' — if you don't want local paths probed, ask for that to be removed; (5) prefer using a short‑lived/anonymous token rather than any long‑lived secret, and never supply unrelated credentials. If the author can clarify the config-path usage and remove unnecessary filesystem probing, the incoherence would be resolved and risk reduced.

Getting Started

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

Try saying:

• "generate my images or clips"
• "export 1080p MP4"
• "generate a short romantic kiss scene"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

Free Video Generator Kiss — Generate Kiss Scene Videos Free

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

A quick example: upload two portrait photos of people, type "generate a short romantic kiss scene video from these two photos", and you'll get a 1080p MP4 back in roughly 30-60 seconds. All rendering happens server-side.

Worth noting: high-resolution face photos produce more realistic results.

Matching Input to Actions

User prompts referencing free video generator kiss, 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.

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

• X-Skill-Source: free-video-generator-kiss
• 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

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.

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)

Common Workflows

Quick edit: Upload → "generate a short romantic kiss scene video from these two photos" → 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 "generate a short romantic kiss scene video from these two photos" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, MP4, MOV for the smoothest experience.

Export as MP4 for widest compatibility.