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Adobe Video Editor

作者 francemichaell-15 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install adobe-video-editor
功能描述
edit raw video footage into polished edited clips with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators and marketers use it fo...
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 unedited screen recording into a 1080p MP4"
  • "trim the pauses, add transitions, and export as a clean MP4"
  • "editing raw footage into polished videos without Adobe Premiere for content creators and 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.

Adobe Video Editor — 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 unedited screen recording, type "trim the pauses, add transitions, and export as a clean MP4", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter clips under 3 minutes process significantly faster and yield cleaner AI edits.

Matching Input to Actions

User prompts referencing adobe video editor, 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.

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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

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

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.

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 → "trim the pauses, add transitions, and export as a clean MP4" → 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 "trim the pauses, add transitions, and export as a clean MP4" — concrete instructions get better results.

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

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

安全使用建议
This skill will upload any videos you provide to a third-party API (mega-api-prod.nemovideo.ai) for cloud GPU processing — that's how it works, but it has privacy implications. Before installing: (1) confirm the vendor/source and privacy/data-retention policy (homepage is missing); (2) avoid setting a long-lived or privileged NEMO_TOKEN — prefer the anonymous token flow for untrusted content; (3) do not upload sensitive or private footage unless you trust the service; (4) ask the publisher to explain the configPath metadata mismatch (~/.config/nemovideo/ vs registry) and to provide a homepage or contact; (5) if you need stricter control, consider blocking network access for this skill or require user confirmation before uploads. These steps will reduce the main risks (data exfiltration and untrusted remote processing).
功能分析
Type: OpenClaw Skill Name: adobe-video-editor Version: 1.0.0 The 'adobe-video-editor' skill is a functional integration for a cloud-based video processing service hosted at nemovideo.ai. It defines standard API interactions for uploading, editing (via SSE), and exporting videos, including an automated setup process for acquiring anonymous authentication tokens. The skill's behavior, including the use of attribution headers and session management, is clearly aligned with its stated purpose of providing AI-driven video editing without identified indicators of malicious intent or data exfiltration.
能力评估
Purpose & Capability
The skill name/description (cloud video editing) aligns with the instructions that POST uploads and render requests to a remote nemo API and requires a NEMO_TOKEN. However the SKILL.md frontmatter mentions a config path (~/.config/nemovideo/) while registry metadata lists no required config paths — this mismatch is an inconsistency to verify with the publisher. Source/homepage are also absent, which reduces ability to validate the backend service.
Instruction Scope
The SKILL.md explicitly instructs the agent to obtain or use a token, create sessions, upload user files (multipart or URL), stream SSE, and poll render endpoints — all coherent for remote video processing. These instructions will cause user video/audio to be transmitted to an external service; that is expected for this purpose but is a significant privacy consideration. The instructions do not appear to read unrelated local secrets, but they do ask the agent to 'auto-detect' platform from install path which may require reading agent metadata/install path.
Install Mechanism
No install spec and no code files (instruction-only) — lowest disk/write risk. Nothing is downloaded or written by an installer step in the package.
Credentials
Only a single credential (NEMO_TOKEN) is declared and used, which is proportional for calling the remote API. The skill also documents how to obtain an anonymous token via the vendor endpoint if NEMO_TOKEN is not set. Verify that you do not supply a privileged or long-lived token; using the anonymous token flow is safer for untrusted content. The discrepancy between registry 'no config paths' and frontmatter listing ~/.config/nemovideo/ is concerning and should be clarified.
Persistence & Privilege
always:false and normal model invocation — no forced-global presence. The skill asks the agent to store session_id/state for the render job (normal for a remote service) but does not request system-wide privileges or modifications to other skills.
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 adobe-video-editor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /adobe-video-editor 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 Adobe Video Editor skill. - Upload and edit raw video footage (MP4, MOV, AVI, WebM up to 500MB) into polished 1080p MP4 clips via cloud GPU processing. - No Adobe Premiere needed: simply describe desired edits (trim, transitions, text, audio, etc.) and receive a ready-to-download video in 1–2 minutes. - Automatic token handling for both registered and anonymous users, including credit tracking and session management. - Supports common editing workflows: quick edits, batch processing, and iterative refinements within persistent sessions. - Clear status updates, detailed error handling, and concise timeline summaries for all editing actions. - Export videos for free in industry-standard formats, optimized for content creators and marketers.
元数据
Slug adobe-video-editor
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

Adobe Video Editor 是什么?

edit raw video footage into polished edited clips with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators and marketers use it fo... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 65 次。

如何安装 Adobe Video Editor?

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

Adobe Video Editor 是免费的吗?

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

Adobe Video Editor 支持哪些平台?

Adobe Video Editor 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 Adobe Video Editor?

由 francemichaell-15(@francemichaell-15)开发并维护,当前版本 v1.0.0。

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