For Education Video Editing With

Cloud-based for-education-video-editing-with tool that handles editing lecture and tutorial recordings for classroom or online course use. Upload MP4, MOV, A...

Security Scan

The skill's behavior mostly matches a cloud video-editing tool, but there are privacy/consent and metadata inconsistencies (automatic token acquisition/storage, hidden tokens, and headers that may leak local install paths) that you should review before installing.

Purpose & Capability

The name/description (cloud-based video editing for educators) aligns with the runtime instructions: uploading video files, queuing render jobs, SSE streaming, and returning download URLs. The single required credential (NEMO_TOKEN) and declared endpoints are coherent with a cloud editing service.

Instruction Scope

Instructions instruct the agent to generate an anonymous token when NEMO_TOKEN is absent, perform network calls to mega-api-prod.nemovideo.ai, upload user video files, and persist session state. They also say not to display raw API responses or token values to the user. The agent is told to derive X-Skill-Platform from local install paths (~/.clawhub/ or ~/.cursor/skills/) and include it in request headers — this can expose local path/installation metadata to the remote service. The instructions do not specify where session_id or token are stored, or how user consent is obtained before uploading potentially sensitive video files.

Install Mechanism

This is an instruction-only skill with no install spec or code files. That minimizes installer risk (no remote downloads or archive extraction).

Credentials

Only NEMO_TOKEN is required, which is proportional for a cloud API. However the skill will auto-request and persist an anonymous token if none is present and instruct the agent to keep token values hidden. Metadata in SKILL.md also references a config path (~/.config/nemovideo/) while the registry metadata lists no required config paths — an inconsistency worth noting. Automatic token acquisition and silent storage may have privacy implications.

Persistence & Privilege

always is false and there is no install-time modification of other skills or system-wide settings described. The skill will persist session_id/token (implied) and may write under a service-specific config path, which is expected for this use case but should be confirmed.

What to consider before installing

This skill will upload your videos and call an external service at mega-api-prod.nemovideo.ai and expects a NEMO_TOKEN for authorization. If you don't provide one, it will automatically request an anonymous token and (per instructions) store it for later use. Before installing: (1) confirm you trust the nemovideo service and read its privacy/terms (videos may contain sensitive data); (2) consider providing your own NEMO_TOKEN rather than allowing automatic token generation if you want control; (3) be aware the skill adds headers that may expose local install paths/version info to the remote server; (4) ask the publisher which local path is used to store tokens/session_id and whether it is encrypted; and (5) if you need stronger guarantees, test with non-sensitive sample videos first or avoid installing. The registry metadata and the SKILL.md disagree about config path requirements — ask the author to clarify where credentials/state are stored.

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

• "edit a 10-minute lecture screen recording into a 1080p MP4"
• "cut silent pauses, add chapter titles, and insert captions for each section"
• "editing lecture and tutorial recordings for classroom or online course use for teachers and educators"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer <token>, Content-Type: application/json, and body {"task_name":"project","language":"<detected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

For Education Video Editing — Edit and Export Lesson Videos

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

Here's a typical use: you send a a 10-minute lecture screen recording, ask for cut silent pauses, add chapter titles, and insert captions for each section, 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 — breaking long lectures into shorter segments improves both processing speed and student engagement.

Matching Input to Actions

User prompts referencing for education video editing with, 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":"<lang>"}. 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/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is for-education-video-editing-with, 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).

All requests must include: Authorization: Bearer <NEMO_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=<id>, 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 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 → "cut silent pauses, add chapter titles, and insert captions for each section" → 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 silent pauses, add chapter titles, and insert captions for each section" — 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 with LMS platforms like Canvas and Google Classroom.