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Batch Video

作者 peandrover adam · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install batch-video
功能描述
process multiple video files into processed MP4 files with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. marketers use it for applying the sa...
使用说明 (SKILL.md)

Getting Started

Send me your multiple video files and I'll handle the bulk video processing. Or just describe what you're after.

Try saying:

  • "process ten 30-second product clips into a 1080p MP4"
  • "add a logo watermark and trim the first 3 seconds from all videos"
  • "applying the same edits to multiple videos at once for marketers"

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.

Batch Video — Process Multiple Videos at Once

This tool takes your multiple video files and runs bulk video processing through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have ten 30-second product clips and want to add a logo watermark and trim the first 3 seconds from all videos — the backend processes it in about 1-3 minutes and hands you a 1080p MP4.

Tip: group clips with similar lengths together so batch jobs finish at a consistent pace.

Matching Input to Actions

User prompts referencing batch video, 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.

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

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

All requests must include: Authorization: Bearer \x3CNEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 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=\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

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 "add a logo watermark and trim the first 3 seconds from all videos" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "add a logo watermark and trim the first 3 seconds from all videos" → Download MP4. Takes 1-3 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.

安全使用建议
This skill appears to be a legitimate cloud-based batch video processor, but there are two things to check before installing or providing credentials: 1) Source and trust: there is no publisher homepage or provenance. Verify the nemovideo domain and the publisher identity. Ask for a homepage, documentation, or a company/maintainer identity you trust before sending private or sensitive videos. 2) Local config access: SKILL.md frontmatter references ~/.config/nemovideo/ but the registry metadata did not list any required config paths. Ask the publisher why that local config is needed and whether the skill will read any files there. If you do not want the skill to access local config, prefer using an anonymous starter token (the skill documents how to request one) rather than supplying a long-lived NEMO_TOKEN. Also consider data-privacy questions: where uploaded videos are stored, retention period, whether outputs are private, and whether the service encrypts data at rest/in transit. If you must provide NEMO_TOKEN, prefer creating a scoped/short-lived credential and test with non-sensitive content first.
功能分析
Type: OpenClaw Skill Name: batch-video Version: 1.0.0 The skill is a legitimate integration for a batch video processing service hosted at nemovideo.ai. It defines a clear workflow for the AI agent to authenticate via the NEMO_TOKEN environment variable or an anonymous token, manage video editing sessions via Server-Sent Events (SSE), and handle file uploads and rendering. The requested access to ~/.config/nemovideo/ is scoped to the tool's own configuration, and the instructions in SKILL.md are consistent with providing a functional user interface for the described cloud rendering pipeline.
能力评估
Purpose & Capability
The name/description (batch video processing) aligns with the runtime instructions which call a cloud render API and accept uploads. Requesting a NEMO_TOKEN is consistent with a cloud service credential. However, the SKILL.md frontmatter mentions a config path (~/.config/nemovideo/) that is not listed in the registry metadata — this is an unexplained mismatch.
Instruction Scope
The SKILL.md explicitly instructs the agent to create sessions, upload user files, stream via SSE, poll status, and include attribution headers — all expected for a remote render service. The concerning bit is the frontmatter reference to a local config path (~/.config/nemovideo/) and the instruction to 'use NEMO_TOKEN if in environment, otherwise generate one' which implies the skill may look for or prefer local credentials/config. There are no instructions to access unrelated files, but the implicit local config access is scope-creep unless justified.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so nothing gets written to disk by an installer. That is the lowest-risk install footprint.
Credentials
The only declared credential is NEMO_TOKEN, which is proportional to a cloud video service. However, the frontmatter's configPaths entry ( ~/.config/nemovideo/ ) would grant the skill access to files in the user's config directory; that path is not declared in the registry metadata and is not explained in the docs. Requiring both an env token and potentially a config path is disproportionate unless the publisher explains why the local config is needed.
Persistence & Privilege
The skill does not request always:true, does not modify other skills, and has no install-time persistent agent privileges. It runs as an instruction-only skill and can be invoked by the user; autonomous invocation is allowed by default but not, by itself, a red flag.
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 batch-video
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /batch-video 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 Batch Video skill. - Process multiple MP4, MOV, AVI, or WebM files (up to 500MB) at once, applying the same edits to all. - Supports fast batch tasks like watermarking, trimming, and adding audio/text overlays, with easy session management using cloud GPUs. - Simple onboarding: automatically fetches a temporary token if needed; minimal setup required. - Provides clear feedback on upload, processing, export, and error states. - Handles user commands for uploading, exports, credit checks, and batch editing through simple chat prompts.
元数据
Slug batch-video
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

Batch Video 是什么?

process multiple video files into processed MP4 files with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. marketers use it for applying the sa... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 100 次。

如何安装 Batch Video?

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

Batch Video 是免费的吗?

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

Batch Video 支持哪些平台?

Batch Video 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 Batch Video?

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

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