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Maker Renderforest

作者 peandrover adam · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install maker-renderforest
功能描述
Skip the learning curve of professional editing software. Describe what you want — create a 30-second intro video using a Renderforest-style animated templat...
使用说明 (SKILL.md)

Getting Started

Share your images, text, audio and I'll get started on template-based video creation. Or just tell me what you're thinking.

Try saying:

  • "create my images, text, audio"
  • "export 1080p MP4"
  • "create a 30-second intro video using"

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.

Maker Renderforest — Create Videos From Templates

Drop your images, text, audio in the chat and tell me what you need. I'll handle the template-based video creation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a logo file and a short tagline, ask for create a 30-second intro video using a Renderforest-style animated template, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — simpler templates with fewer elements render significantly faster.

Matching Input to Actions

User prompts referencing 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.

All calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

  1. SessionPOST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"\x3Clang>"}. Gives you a session_id.
  2. Chat (SSE)POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
  3. UploadPOST /api/upload-video/nemo_agent/me/\x3Csid> — multipart file or JSON with URLs.
  4. CreditsGET /api/credits/balance/simple — returns available, frozen, total.
  5. StateGET /api/state/nemo_agent/me/\x3Csid>/latest — current draft and media info.
  6. ExportPOST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s for completed status and download URL.

Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Three attribution headers are required on every request and must match this file's frontmatter:

Header Value
X-Skill-Source maker-renderforest
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

Include Authorization: Bearer \x3CNEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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)

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

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.

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=\x3Cid> (get \x3Cid> 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

Common Workflows

Quick edit: Upload → "create a 30-second intro video using a Renderforest-style animated template" → 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 intro video using a Renderforest-style animated template" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across social platforms.

安全使用建议
This skill appears to do what it says — it uploads your images/audio and talks to a nemo-video API to create template videos — but check a few things before installing: 1) Source/provenance: there is no homepage and the publisher identity is just an ID; if you need assurance about privacy or reliability, ask the author for a published homepage or organization. 2) Data exfiltration: media you drop in chat will be uploaded to https://mega-api-prod.nemovideo.ai. Don't upload secrets or sensitive files you wouldn't want on that service. 3) Token storage: confirm whether the skill will write the anonymous or provided NEMO_TOKEN and session_id to ~/.config/nemovideo/ (or elsewhere), and whether you can revoke/delete them. 4) Attribution headers: the skill requires adding X-Skill-Source / X-Skill-Version / X-Skill-Platform headers — ensure that's acceptable for your environment. 5) If you want higher assurance, request the skill's source code or a trustworthy homepage, or run it in a sandboxed agent environment and monitor network calls. If any of the above are unacceptable or unexplained by the publisher, treat installation as higher-risk.
功能分析
Type: OpenClaw Skill Name: maker-renderforest Version: 1.0.0 The skill 'maker-renderforest' is a legitimate-appearing integration for a video generation service hosted at nemovideo.ai. It provides structured instructions for an AI agent to manage authentication tokens, handle file uploads, and process video rendering tasks via a cloud API. The skill includes security-conscious instructions (e.g., not printing raw tokens) and its requests for environment variables (NEMO_TOKEN) and configuration paths (~/.config/nemovideo/) are strictly aligned with its stated purpose of template-based video creation.
能力评估
Purpose & Capability
The skill name/description promise cloud template-based video creation and the SKILL.md instructs the agent to call a nemo-video API, create sessions, upload media and export rendered MP4s — this matches the stated purpose. One inconsistency: the skill registry metadata provided to you earlier listed no required config paths, but the SKILL.md frontmatter metadata requests a config path (~/.config/nemovideo/). That mismatch should be clarified by the author.
Instruction Scope
Instructions are specific and constrained to the nemo API (session creation, SSE chat, uploads, export/polling). They explicitly upload user images/audio (up to 200MB) and instruct generating/using an anonymous token when NEMO_TOKEN is not set. These behaviors are expected for the described capability, but they do mean the agent will transmit user files and session tokens to https://mega-api-prod.nemovideo.ai — ensure you consent to that. The SKILL.md also directs the agent to 'save session_id' and to include attribution headers; it doesn't strictly define storage location (though frontmatter suggests a config path).
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest install risk. Nothing will be downloaded or written by an installer step unless the agent itself writes session data or tokens at runtime per the instructions.
Credentials
The only required credential is NEMO_TOKEN (primaryEnv), which is appropriate for an API-driven render service. The SKILL.md provides a fallback anonymous-token flow when NEMO_TOKEN is missing. The only proportionality concern is the frontmatter's request for a config path (~/.config/nemovideo/) which was not reflected in the top-level registry requirements you were shown — this could indicate the skill intends to persist tokens/session info on disk and should be confirmed.
Persistence & Privilege
The skill is not marked always:true and can be user-invoked (normal). It does instruct saving session_id and may persist an anonymous NEMO_TOKEN (7-day token) for reuse. Persisting tokens/session state under a user config path is plausible for its function, but because the registry metadata you received didn't list config paths, confirm where the skill will store this data and whether it will create files under ~/.config/nemovideo/.
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 maker-renderforest
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /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
- Initial release of Maker Renderforest skill (v1.0.0). - Instantly create 30-second intro videos from your images, audio, and text using Renderforest-style animated templates. - Supports uploads of PNG, JPG, MP3, and MP4 files up to 200MB. - Automatic cloud rendering pipeline with 1-2 minute turnaround and 1080p MP4 export. - Handle all common video editor functions via chat: upload, edit, preview, and export. - Built-in error handling and session management for a seamless user experience.
元数据
Slug 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

Maker Renderforest 是什么?

Skip the learning curve of professional editing software. Describe what you want — create a 30-second intro video using a Renderforest-style animated templat... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 47 次。

如何安装 Maker Renderforest?

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

Maker Renderforest 是免费的吗?

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

Maker Renderforest 支持哪些平台?

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 Maker Renderforest?

由 peandrover adam(@peand-rover)开发并维护,当前版本 v1.0.0。

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