Best Video Learn

Get structured learning videos ready to post, without touching a single slider. Upload your video clips (MP4, MOV, AVI, WebM, up to 500MB), say something lik...

Security Scan

The skill's behavior (uploading and rendering videos to an external API using NEMO_TOKEN) matches its description, but small inconsistencies and a few instructions that access local install/config paths warrant caution before installing or providing credentials.

Purpose & Capability

The name/description (remote video editing & export) align with the runtime instructions: session creation, upload endpoints, SSE streaming, render/poll/export. Requesting a NEMO_TOKEN credential and the described API calls are appropriate for that purpose.

Instruction Scope

Most instructions stay within the video-editing scope (create session, upload files, poll render). However the SKILL.md asks the agent to "detect from install path" and to include an attribution header based on install location, which implies reading local installation paths (~/.clawhub/, ~/.cursor/skills/) or other filesystem info. That filesystem detection is not strictly necessary for core functionality and should be clarified.

Install Mechanism

This is an instruction-only skill with no install spec and no code files. Nothing is written to disk by an installer, which minimizes install-time risk.

Credentials

The declared primary credential is a single NEMO_TOKEN, which is appropriate for a hosted video service. However, the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) and asks the runtime to detect install paths — the registry metadata provided with the skill listed no required config paths. This inconsistency is concerning because it suggests the skill may read local config files (possible additional credentials or metadata) without that being clearly declared.

Persistence & Privilege

The skill does not request always:true and is user-invocable; it does not attempt to modify other skills or request persistent system-wide privileges. Autonomous invocation is allowed (platform default) but is not combined with any elevated flags here.

What to consider before installing

This skill appears to do what it says (upload your videos to a remote renderer) and needs a NEMO_TOKEN to authenticate. Before installing or supplying credentials: 1) Verify you trust the domain (https://mega-api-prod.nemovideo.ai) and its privacy/data-retention policies—your uploaded videos will be sent off your machine. 2) Prefer using an ephemeral/anonymous token (the skill can request a 7-day starter token) rather than a long-lived account token. 3) Ask the publisher to clarify why the skill needs to "detect install path" or read ~/.config/nemovideo/ (this could expose local config files); if you are uncomfortable, do not grant file-system access. 4) Because registry metadata and the SKILL.md frontmatter disagree about config paths, request the author/publisher to resolve that inconsistency or provide a source/homepage. 5) Do not provide other unrelated credentials. If you need stronger assurance, request the skill source or a privacy policy and test it first with non-sensitive sample videos.

Getting Started

Share your video clips and I'll get started on AI learning video creation. Or just tell me what you're thinking.

Try saying:

• "convert my video clips"
• "export 1080p MP4"
• "break this lecture into short learning"

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.

Best Video Learn — Create and Export Video Lessons

Send me your video clips and describe the result you want. The AI learning video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 10-minute lecture recording, type "break this lecture into short learning segments with chapter titles and captions", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: splitting content into 3-5 minute segments improves viewer retention significantly.

Matching Input to Actions

User prompts referencing best video learn, 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.

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

• X-Skill-Source: best-video-learn
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else 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.

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

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

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

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

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

Session state: GET /api/state/nemo_agent/me/<sid>/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_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> 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.

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.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says	You do
"click [button]" / "点击"	Execute via API
"open [panel]" / "打开"	Query session state
"drag/drop" / "拖拽"	Send edit via SSE
"preview in timeline"	Show track summary
"Export button" / "导出"	Execute 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)

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

Common Workflows

Quick edit: Upload → "break this lecture into short learning segments with chapter titles and captions" → 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 "break this lecture into short learning segments with chapter titles and captions" — 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 learning platforms and devices.