Ai Video Help

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...

Security Scan

The skill's declared purpose (cloud video editing) matches what its instructions request and do, but there are a few small inconsistencies and privacy considerations you should understand before installing.

Purpose & Capability

Name/description describe cloud AI video editing. The skill only asks for a NEMO_TOKEN (the service API token) and describes API endpoints for uploading, editing, and exporting videos — these requirements align with the stated purpose. Minor metadata inconsistency: the SKILL.md frontmatter mentions a config path (~/.config/nemovideo/), while the registry metadata lists no config paths.

Instruction Scope

SKILL.md contains step-by-step runtime instructions: use NEMO_TOKEN if present, otherwise request an anonymous token from https://mega-api-prod.nemovideo.ai, create a session, upload files, stream SSE, poll render status, and return download URLs. These network operations and file uploads are expected for a cloud video-editing service. It also instructs the agent to read the skill's own frontmatter and detect install path (e.g., ~/.clawhub/), which requires reading the agent's filesystem location — benign but notable for platform detection.

Install Mechanism

No install spec or code files — instruction-only skill. Nothing is written to disk or downloaded during install per the provided metadata, which is the lowest-risk install model.

Credentials

Only one credential is requested: NEMO_TOKEN (declared as primaryEnv). That is reasonable for a hosted video-editing API. Be aware the skill will use any NEMO_TOKEN present in the agent environment — if you reuse the same token elsewhere, that token will be sent to the external service. The frontmatter's configPaths (~/.config/nemovideo/) is reasonable for storing service config but was not declared in registry metadata, which is an inconsistency to clarify.

Persistence & Privilege

always is false and there is no install-time code that modifies other skills or agent config. The skill operates per-session using backend sessions/tokens; no privileged or persistent system presence is requested.

Assessment

This skill will upload your video files to a third-party cloud (mega-api-prod.nemovideo.ai) for processing and will send an Authorization: Bearer <NEMO_TOKEN> header if a NEMO_TOKEN exists in the environment. Before installing or enabling it: (1) Confirm you trust nemovideo.ai and are comfortable with your videos being uploaded and processed on their servers; check their privacy/retention policy. (2) Do not place a sensitive or reuse token named NEMO_TOKEN in your global environment — prefer a token created specifically for this skill or let the skill obtain a temporary anonymous token. (3) Ask the publisher for source/homepage and clarify the metadata inconsistency about config paths. (4) If you need deterministic data handling (no external upload), do not enable this skill. Overall the skill appears coherent with its purpose, but review privacy and token-use implications before proceeding.

Getting Started

Share your raw video footage and I'll get started on AI video assistance. Or just tell me what you're thinking.

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "trim the pauses, add captions, and"

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.

AI Video Help — Edit and Export Videos with AI

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

Say you have a 2-minute unedited screen recording and want to trim the pauses, add captions, and export as a clean MP4 — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 60 seconds process significantly faster.

Matching Input to Actions

User prompts referencing ai video help, 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.

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

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

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

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.

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "trim the pauses, add captions, 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 for widest compatibility across platforms.

Common Workflows

Quick edit: Upload → "trim the pauses, add captions, 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.