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whitejohnk-26

Editor Kids

作者 whitejohnk-26 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install editor-kids
功能描述
edit raw video footage into kid-friendly edited videos with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. parents and family content creators...
使用说明 (SKILL.md)

Getting Started

Got raw video footage to work with? Send it over and tell me what you need — I'll take care of the AI kids video editing.

Try saying:

  • "edit a 2-minute birthday party recording into a 1080p MP4"
  • "cut out boring parts, add fun transitions and upbeat background music for kids"
  • "editing family and kids videos with fun effects and music for parents and family content creators"

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.

Editor Kids — Edit and Export Kids Videos

Drop your raw video footage in the chat and tell me what you need. I'll handle the AI kids video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 2-minute birthday party recording, ask for cut out boring parts, add fun transitions and upbeat background music for kids, 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 — shorter clips under 3 minutes process faster and give cleaner AI edit results.

Matching Input to Actions

User prompts referencing editor kids, 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.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: editor-kids
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

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

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)

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 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

Common Workflows

Quick edit: Upload → "cut out boring parts, add fun transitions and upbeat background music for kids" → 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 "cut out boring parts, add fun transitions and upbeat background music for kids" — 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 across devices and sharing platforms.

安全使用建议
Before installing, be aware this skill will: (1) send any video files you drop in chat to mega-api-prod.nemovideo.ai for processing — do not upload private or sensitive footage unless you trust that endpoint and its privacy/retention policy; (2) attempt to automatically connect on first use and will create an anonymous token if you haven't provided NEMO_TOKEN, then store session IDs/tokens for later calls; (3) check for local install paths (~/.clawhub/, ~/.cursor/skills/, ~/.config/nemovideo/) to add an attribution header — if you prefer the agent not to probe your filesystem, do not install or disable that behavior; (4) there is an unexplained metadata discrepancy (SKILL.md requests a config path that the registry manifest did not list) — ask the publisher to clarify what local paths are accessed and where tokens/session IDs are stored. If you proceed, supply a scoped/account token you control (not long-lived master credentials), review the service's privacy terms, and consider manually providing a token rather than letting the skill generate/store one automatically.
功能分析
Type: OpenClaw Skill Name: editor-kids Version: 1.0.0 The skill acts as a wrapper for a cloud-based video editing service (nemovideo.ai), facilitating video uploads, AI-driven editing via SSE, and rendering. It manages its own authentication tokens (NEMO_TOKEN) and performs basic environment fingerprinting (checking for ~/.cursor/ or ~/.clawhub/ paths) solely for API attribution headers (X-Skill-Platform). The behavior is consistent with its stated purpose, and there is no evidence of unauthorized data exfiltration or malicious execution.
能力评估
Purpose & Capability
The skill claims to edit kid-friendly videos and the instructions call out uploading user media and calling a video-processing API (mega-api-prod.nemovideo.ai), which is coherent. However, the SKILL.md frontmatter requests a config path (~/.config/nemovideo/) and instructions that probe local install paths for attribution headers, while the registry metadata lists no required config paths — this mismatch is unexplained.
Instruction Scope
At runtime the agent is instructed to automatically connect to the external API on first interaction, generate an anonymous token if NEMO_TOKEN is missing, upload user-provided video files to the remote service, poll for job status, and save session tokens/IDs. The instructions also direct the agent to detect local install paths (~/.clawhub/, ~/.cursor/skills/) to set an attribution header. Automatic network calls, file uploads of potentially sensitive videos, and filesystem checks expand the skill’s scope beyond a purely in-chat transformation and should be considered before enabling.
Install Mechanism
There is no install spec and no code files; this is instruction-only so it will not write new binaries or run an installer. That minimizes disk-level risk.
Credentials
The only declared required credential is NEMO_TOKEN (primaryEnv), which is consistent with a cloud-editing service. However, the SKILL.md frontmatter also lists a config path (~/.config/nemovideo/) and the runtime instructions expect to read the install path to set X-Skill-Platform — these filesystem requirements are not reflected in the registry's earlier manifest and are not justified by the description.
Persistence & Privilege
The skill instructs saving session_id and may persist an anonymous NEMO_TOKEN if generated. always:false and normal autonomous invocation are used (no forced always-on). Persisting tokens/sessions for later API calls is reasonable for this service, but users should be aware the skill will create and store short-lived credentials and will initiate outbound network activity automatically on first use.
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 editor-kids
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /editor-kids 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
Editor Kids 1.0.0 initial release: - Edit raw video footage into kid-friendly 1080p MP4s with cloud-based AI, supporting MP4, MOV, AVI, and WebM up to 500MB. - Simple workflows for parents and family creators: upload, describe your edits, and get back downloadable video with fun effects and kids’ music. - Automatic setup with free credits and session management via secure tokens. - Supports multi-language commands for actions like export, upload, check credits/balance, or preview tracks. - Detailed error handling and user guidance for common video upload and export scenarios.
元数据
Slug editor-kids
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

Editor Kids 是什么?

edit raw video footage into kid-friendly edited videos with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. parents and family content creators... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 70 次。

如何安装 Editor Kids?

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

Editor Kids 是免费的吗?

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

Editor Kids 支持哪些平台?

Editor Kids 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 Editor Kids?

由 whitejohnk-26(@whitejohnk-26)开发并维护,当前版本 v1.0.0。

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