Video Editing With Macbook Pro

Skip the learning curve of professional editing software. Describe what you want — trim the footage, add transitions, and export a clean final cut — and get...

Security Scan

The skill's declared purpose (cloud video editing) matches most of its instructions, but inconsistent metadata, an unknown upstream domain/owner, and minor scope/metadata mismatches mean you should verify before installing.

Purpose & Capability

The name/description claim cloud-based AI video editing; the SKILL.md shows exactly that (upload files, create sessions, start renders). Requesting a single service token (NEMO_TOKEN) is appropriate. However, the skill metadata in the frontmatter lists a config path (~/.config/nemovideo/) even though the registry summary said no required config paths; the source/homepage are missing and the owner is an opaque ID, which reduces transparency.

Instruction Scope

Runtime instructions are focused on editing workflows (session creation, file upload, SSE, render polling). This requires uploading user videos to an external API (mega-api-prod.nemovideo.ai) — expected for cloud editing but a privacy-sensitive action. The doc also instructs generating an anonymous token if NEMO_TOKEN is absent and to set custom attribution headers; nothing in the SKILL.md instructs reading arbitrary local files or unrelated credentials. Minor scope creep: platform detection mentions reading the install path and frontmatter values are required for headers.

Install Mechanism

Instruction-only skill with no install spec and no code files; nothing is written to disk by the skill package itself. This is the lowest install risk.

Credentials

The skill requests one environment credential (NEMO_TOKEN) which is proportionate for a cloud API. However, the frontmatter also enumerates a config path (~/.config/nemovideo/) that could imply local config access — the top-level registry said no required config paths, so this is an inconsistency to clarify. The skill's behavior of auto-requesting an anonymous token if none is present is reasonable but should be explicitly consented to by the user.

Persistence & Privilege

always:false and no install hooks are present. The skill does not request persistent platform-wide privileges or to modify other skills. Autonomous invocation is enabled (default) but not combined with an 'always:true' or broad credential set, so no extra privilege flags.

What to consider before installing

This skill appears to do what it says (cloud video editing) but before installing: 1) Confirm you trust the backend domain (mega-api-prod.nemovideo.ai) and the unknown owner—there's no homepage or additional provenance. 2) Understand that using the skill uploads your video files to that external service; do not send sensitive footage unless you're comfortable with that privacy tradeoff. 3) The skill asks for a NEMO_TOKEN; if you don't supply one it will obtain an anonymous token via the API—decide whether you prefer to supply your own token or use anonymous mode. 4) Ask the publisher to clarify the conflicting metadata (the frontmatter lists a config path) and to provide a homepage/privacy policy describing retention and access to uploaded media. If you need stronger assurance, test with non-sensitive videos and monitor network requests or request the vendor's documentation before providing real data.

Getting Started

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

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "trim the footage, add transitions, 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.

Video Editing with MacBook Pro — Edit and Export Polished Videos

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

Say you have a 3-minute MacBook screen recording or camera clip and want to trim the footage, add transitions, and export a clean final cut — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: MOV files from MacBook record natively and upload without conversion.

Matching Input to Actions

User prompts referencing video editing with macbook pro, 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.

Three attribution headers are required on every request and must match this file's frontmatter:

Header	Value
X-Skill-Source	video-editing-with-macbook-pro
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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

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 → "trim the footage, add transitions, and export a clean final cut" → 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 "trim the footage, add transitions, and export a clean final cut" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, ProRes for the smoothest experience.

Export as MP4 H.264 for the best balance of quality and file size across all platforms.