Ai Video Learn

convert video clips into structured learning videos with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. students and educators use it for turn...

Security Scan

The skill mostly matches a video-processing integration, but there are inconsistencies and small red flags (missing source/homepage, metadata vs SKILL.md mismatch, automatic token creation + instruction to hide tokens) that deserve caution before installing or using it with sensitive content.

Purpose & Capability

The skill's declared purpose (convert videos into structured lessons) aligns with the API endpoints, upload/export flow, and a single credential (NEMO_TOKEN). However the SKILL.md requests detecting install path and references a config directory (~/.config/nemovideo/) which is not declared elsewhere in the registry metadata — this mismatch is unexpected and worth asking the author about.

Instruction Scope

Instructions tell the agent to automatically create anonymous tokens, store session IDs, upload user files to an external cloud backend, and "don't display raw API responses or token values to the user." The automatic token issuance and explicit instruction to hide token values are unusual and reduce transparency. The SKILL.md also instructs deriving X-Skill-Platform by probing install paths, which implies filesystem checks beyond simply reading NEMO_TOKEN from the environment.

Install Mechanism

This is instruction-only (no install spec, no downloaded code), so nothing is written to disk by an installer. That lowers infrastructural risk.

Credentials

Only one credential (NEMO_TOKEN) is declared and used, which is appropriate for an API-backed service. But the skill also includes a flow to auto-create a token if NEMO_TOKEN is not present — this makes the declared requirement ambiguous (do you need to provide a token or not?). The SKILL.md also references a config path not present in the top-level requirements, another inconsistency.

Persistence & Privilege

The skill does not request always:true or any special platform-wide privileges. It stores session IDs for its own requests, which is normal for a session-based API.

What to consider before installing

This skill appears to implement a third-party video-processing backend (mega-api-prod.nemovideo.ai) and will upload user media to that service. Before installing or using it: - Be cautious about uploading sensitive or private videos (student data, personal recordings). Confirm the service's privacy and data-retention policy and whether uploads are stored/used for training. - The skill can auto-generate anonymous tokens and explicitly instructs the agent to hide token values — ask the maintainer why tokens must be hidden and whether tokens are stored long-term on the host. - Note inconsistencies: SKILL.md references a config path (~/.config/nemovideo/) and install-path detection that are not declared in the registry metadata; ask for clarification. - Because there is no source or homepage and no install artifact to inspect, prefer providing your own vetted NEMO_TOKEN (if you trust the service) or avoid using it for sensitive content. - If you proceed, monitor network traffic and limit token scope/expiration where possible; remove any stored tokens or session IDs after use.

Getting Started

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

Try saying:

• "convert my video clips"
• "export 1080p MP4"
• "break this lecture into chapters with"

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.

AI Video Learn — Turn Videos Into Structured Lessons

This tool takes your video clips and runs AI learning analysis through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 10-minute tutorial recording and want to break this lecture into chapters with summaries and key takeaways — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter topic-focused clips generate more accurate chapter breakdowns.

Matching Input to Actions

User prompts referencing ai 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.

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 ai-video-learn, 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).

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

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s 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.

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)

Common Workflows

Quick edit: Upload → "break this lecture into chapters with summaries and key takeaways" → 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 chapters with summaries and key takeaways" — 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.