Ai Video Maker Renderforest

Skip the learning curve of professional editing software. Describe what you want — create a 30-second promo video with animations and background music — and...

Security Scan

The skill's behavior mostly matches a remote video-rendering helper, but there are small metadata inconsistencies and an unknown source/homepage that reduce trust — review token handling and config-path access before installing.

Purpose & Capability

The name/description (AI video creation) align with the runtime instructions (endpoints for uploads, SSE, render/export). Requested credential (NEMO_TOKEN) is expected for a cloud-rendering service. However, SKILL.md metadata declares a config path (~/.config/nemovideo/) that the registry summary said was not required — this mismatch should be clarified.

Instruction Scope

Instructions are focused on interacting with a remote render API (obtain token, create session, upload files, stream SSE, start export). They instruct generating an anonymous token via POST and saving session_id/NEMO_TOKEN and to detect the install path (~/.clawhub, ~/.cursor/skills) to set an attribution header. That implies reading filesystem paths and persisting token/session data; these actions are coherent for the described purpose but worth noting because they touch local config and persistent storage.

Install Mechanism

No install spec and no code files — instruction-only skill. Low installation risk because nothing will be downloaded or written by an installer, but runtime instructions do describe storing tokens/session state.

Credentials

Only one credential is declared (NEMO_TOKEN) which is proportional to a cloud-rendering integration. The SKILL.md also instructs generating an anonymous NEMO_TOKEN if none exists. The concern is the metadata/config-path mismatch: SKILL.md references ~/.config/nemovideo/, which could contain credentials or config; the registry's earlier summary said no config paths were required. Confirm whether the agent will read or write that path and what it will store.

Persistence & Privilege

always:false and no claims of modifying other skills or system-wide settings. The skill will persist session_id/token-like values for continued use, which is normal for a remote service integration; autonomous invocation is allowed by default and is not, by itself, a problem.

What to consider before installing

This skill appears to be a straightforward wrapper around a remote video-rendering API and only asks for one credential (NEMO_TOKEN), which is reasonable. However: the package has no homepage or known source (lower trust), and SKILL.md claims a config path (~/.config/nemovideo/) while the registry record did not — ask the publisher to clarify whether the skill will read/write that directory. Before installing: only provide a limited/anonymous NEMO_TOKEN (avoid reusing AWS/Github/other sensitive tokens), avoid uploading sensitive media, confirm where tokens and session IDs are stored and for how long, and prefer to test with anonymous/free tokens. If you need stronger assurance, request the skill's source or an official homepage and a clear statement of what it stores locally.

Getting Started

Send me your text or images and I'll handle the AI video creation. Or just describe what you're after.

Try saying:

• "create a short product description and three brand images into a 1080p MP4"
• "create a 30-second promo video with animations and background music"
• "creating branded promo videos from text and images for marketers"

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.

AI Video Maker Renderforest — Create Animated Videos from Text

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

A quick example: upload a short product description and three brand images, type "create a 30-second promo video with animations and background music", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter scripts generate faster and stay more focused.

Matching Input to Actions

User prompts referencing ai video maker renderforest, 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 ai-video-maker-renderforest, 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).

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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

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.

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)

Common Workflows

Quick edit: Upload → "create a 30-second promo video with animations and background music" → Download MP4. Takes 1-2 minutes 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 "create a 30-second promo video with animations and background music" — concrete instructions get better results.

Max file size is 200MB. Stick to MP4, JPG, PNG, MP3 for the smoothest experience.

Export as MP4 for widest compatibility across social platforms.