Video Editing Ai By Prompt

Get prompt-edited videos ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something lik...

Security Scan

The skill's requests and runtime instructions are broadly consistent with a cloud-based video-editing service that uses a single service token, but there are a few small metadata inconsistencies and privacy considerations you should review before use.

Purpose & Capability

The skill is a cloud video-editing-by-prompt wrapper and only asks for a single service token (NEMO_TOKEN) and an optional local config path. That aligns with needing an API token to create sessions, upload videos, and start renders.

Instruction Scope

SKILL.md instructs the agent to obtain/use an API token, create sessions, upload user-supplied video files, stream SSE messages, and poll render status — all expected for a cloud render workflow. The instructions do not ask the agent to read unrelated files, secrets, or system state beyond detecting an install path and handling user-provided file paths/URLs.

Install Mechanism

No install spec or downloaded code is present (instruction-only), so nothing is written to disk by an installer. This is the lowest-risk install model for a skill.

Credentials

The skill only requires one credential, NEMO_TOKEN, which is proportionate to the described cloud API. However there is an inconsistency: the registry summary lists no required config paths while the SKILL.md/YAML frontmatter includes configPaths ("~/.config/nemovideo/"). Confirm whether the skill will attempt to read that local config directory before installing.

Persistence & Privilege

The skill is not marked always:true and does not request system-wide privileges. It may detect an install path and read user-supplied file paths for uploads, which is expected behavior for a file-uploading skill.

Scan Findings in Context

[no-findings] expected: The static scanner found no code to analyze. This is expected because the skill is instruction-only; the runtime instructions are the primary surface to inspect.

Assessment

This skill appears to be what it says: a cloud-based video editor that requires a NEMO_TOKEN and uploads your videos to mega-api-prod.nemovideo.ai. Before installing/use: (1) Confirm you are comfortable uploading videos and any private content to that external service; check its privacy/retention policy. (2) Provide only a token scoped for this service (avoid reusing broad or long-lived credentials). (3) Verify whether the skill will read ~/.config/nemovideo/ on your machine (the frontmatter mentions it) and remove or audit that directory if it contains secrets. (4) Note the skill can generate an anonymous token itself if NEMO_TOKEN is not set (100 credits, 7-day expiry) — that is normal but means temporary tokens could be created on your behalf. (5) If you need higher assurance, ask the skill author for a privacy/data-retention statement and confirm the API host is the official Nemo Video service.

Getting Started

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

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "cut the pauses, add background music,"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

Video Editing AI by Prompt — Edit Videos With Text Prompts

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

Here's a typical use: you send a a 2-minute unedited vlog clip, ask for cut the pauses, add background music, and put a text title at the start, and about 30-90 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — the more specific your prompt, the more accurate the edit — mention timestamps if you can.

Matching Input to Actions

User prompts referencing video editing ai by prompt, 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.

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is video-editing-ai-by-prompt, 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).

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.

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

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

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.

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 → "cut the pauses, add background music, and put a text title at the start" → Download MP4. Takes 30-90 seconds 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 the pauses, add background music, and put a text title at the start" — 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.