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Ai Video Maker Renderforest

作者 linmillsd7 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
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在 OpenClaw 中安装
/install ai-video-maker-renderforest
功能描述
Skip the learning curve of professional editing software. Describe what you want — create a 30-second promo video with animations and background music — and...
使用说明 (SKILL.md)

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: \x3Cuuid>. 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":"\x3Clang>"}. 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/\x3Csid> POST Upload a file (multipart) or URL.
/api/credits/balance/simple GET Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/\x3Csid>/latest GET Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda POST Start export. Body: {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"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 \x3CNEMO_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=\x3Cid>, 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.

安全使用建议
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.
功能分析
Type: OpenClaw Skill Name: ai-video-maker-renderforest Version: 1.0.0 The skill 'ai-video-maker-renderforest' is classified as suspicious due to significant brand discrepancy and environment fingerprinting. While it claims to be a 'Renderforest' tool in SKILL.md, all API interactions are directed to an unrelated domain (nemovideo.ai), which is a common indicator of deceptive 'brandjacking.' Furthermore, the instructions require the agent to fingerprint the user's environment by inspecting installation paths (e.g., identifying if the user is on Cursor or Clawhub) to populate telemetry headers like 'X-Skill-Platform'. The _meta.json also contains a suspicious future-dated 'publishedAt' timestamp (year 2026).
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ai-video-maker-renderforest
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-maker-renderforest 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
AI Video Maker Renderforest — Initial Release - Instantly create animated promo videos from text, images, or audio with automatic cloud rendering. - Upload MP4, JPG, PNG, or MP3 files up to 200MB; AI handles editing and compositing. - Receive 30-second, 1080p video exports within 1–2 minutes. - Session-based workflow: tracks video state for instant preview, edits, and export. - Simple setup: automatic token generation for quick start, no account required (100 free credits, 7-day expiry).
元数据
Slug ai-video-maker-renderforest
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ai Video Maker Renderforest 是什么?

Skip the learning curve of professional editing software. Describe what you want — create a 30-second promo video with animations and background music — and... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 94 次。

如何安装 Ai Video Maker Renderforest?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-video-maker-renderforest」即可一键安装,无需额外配置。

Ai Video Maker Renderforest 是免费的吗?

是的,Ai Video Maker Renderforest 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ai Video Maker Renderforest 支持哪些平台?

Ai Video Maker Renderforest 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Ai Video Maker Renderforest?

由 linmillsd7(@linmillsd7)开发并维护,当前版本 v1.0.0。

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