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Video In Filmora

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 video-in-filmora
功能描述
Skip the learning curve of professional editing software. Describe what you want — trim the clip, add transitions, and apply color correction — and get edite...
使用说明 (SKILL.md)

Getting Started

Send me your raw video footage and I'll handle the AI video editing. Or just describe what you're after.

Try saying:

  • "edit a 2-minute raw clip recorded on a smartphone into a 1080p MP4"
  • "trim the clip, add transitions, and apply color correction"
  • "editing and enhancing videos the way Filmora would for content creators and casual editors"

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.

Video in Filmora — Edit and Export Polished Videos

Send me your raw video footage and describe the result you want. The AI video editing runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 2-minute raw clip recorded on a smartphone, type "trim the clip, add transitions, and apply color correction", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter clips under 60 seconds process noticeably faster.

Matching Input to Actions

User prompts referencing video in filmora, 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.

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

Headers are derived from this file's YAML frontmatter. X-Skill-Source is video-in-filmora, 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).

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/\x3Csid>/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_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> 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=\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

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 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 "trim the clip, add transitions, and apply color correction" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for widest compatibility across platforms.

Common Workflows

Quick edit: Upload → "trim the clip, add transitions, and apply color correction" → 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.

安全使用建议
Use this skill only if you are comfortable sending the video, edit instructions, and generated outputs to nemovideo.ai. Prefer non-sensitive footage, use a dedicated token, monitor credits, and verify the provider/Filmora affiliation before relying on it for private or commercial work.
功能分析
Type: OpenClaw Skill Name: video-in-filmora Version: 1.0.0 The skill provides a legitimate interface for a cloud-based video editing service hosted at nemovideo.ai. It manages sessions, handles multipart file uploads, and processes video editing commands via Server-Sent Events (SSE) and polling mechanisms. While it includes telemetry logic to identify the host environment (e.g., Cursor or Clawhub) for API headers, its operations are transparently documented and strictly aligned with its stated purpose of automated video editing, with no evidence of malicious intent or unauthorized data access.
能力评估
Purpose & Capability
The described capability is consistent with a remote video-editing workflow: upload media, send edit prompts, render, and export. This is noteworthy because user videos may be private or sensitive.
Instruction Scope
The skill instructs the agent to create a backend session and route uploads, edits, credit checks, state checks, and exports through the API. This is aligned with the stated purpose and no destructive local actions are shown.
Install Mechanism
There is no install spec and no code files, which reduces local execution risk, but the source and homepage are not provided, limiting provenance verification.
Credentials
Use of NEMO_TOKEN or an anonymous starter token is proportionate for the backend service, but it still gives the skill delegated access to the video-editing API and any associated credits.
Persistence & Privilege
The artifacts show server-side session IDs and render jobs, but no local persistence, background worker, elevated OS privilege, or broad filesystem access.
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 video-in-filmora
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /video-in-filmora 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 the "Video in Filmora" skill for fast, AI-powered video editing. - Supports uploading MP4, MOV, AVI, and MKV files up to 500MB for server-side editing and export. - Users can request trims, transitions, color correction, and more—AI handles editing based on instructions. - Seamless cloud rendering delivers polished video (MP4, up to 1080p) in 1–2 minutes. - Built-in flow for session management, credit checks, and friendly status updates. - Ideal for content creators and casual editors who want quick, professional results without complicated software.
元数据
Slug video-in-filmora
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

Video In Filmora 是什么?

Skip the learning curve of professional editing software. Describe what you want — trim the clip, add transitions, and apply color correction — and get edite... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 59 次。

如何安装 Video In Filmora?

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

Video In Filmora 是免费的吗?

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

Video In Filmora 支持哪些平台?

Video In Filmora 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 Video In Filmora?

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

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