Video Editing Ai Jobs

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

Security Scan

The skill's behavior largely matches a remote video-editing integration, but there are small metadata inconsistencies and the skill will obtain/store tokens and read install paths — review the unknown service (nemovideo.ai) and where tokens/sessions are kept before installing.

Purpose & Capability

Name/description align with the actions described (upload video, remote render, export). Requested credential (NEMO_TOKEN) and API endpoints under nemovideo.ai match the stated purpose. However, registry metadata reported no required config paths while the SKILL.md frontmatter declares a configPaths entry (~/.config/nemovideo/), an inconsistency worth noting.

Instruction Scope

SKILL.md stays within video-editing behavior: it uploads files, starts render jobs, polls status, and streams SSE responses. It does instruct the agent to read its own YAML frontmatter and to detect an install path on disk (to set X-Skill-Platform), and to persist a session_id for subsequent requests — these filesystem reads and local persistence are not strictly necessary for core editing but are justified by attribution/session needs. No instructions direct data to endpoints outside mega-api-prod.nemovideo.ai.

Install Mechanism

Instruction-only skill with no install spec or downloaded code — lowest-risk install model. Nothing is written to disk by an installer step in the package metadata.

Credentials

Only one credential (NEMO_TOKEN) is declared as required/primary, which fits a remote API integration. The SKILL.md also instructs creating an anonymous token by POSTing to the service if NEMO_TOKEN is absent (so the env var is not strictly required). The frontmatter's configPaths entry implies writing/reading ~/.config/nemovideo/ for session storage — reasonable but a persistence detail you should confirm. No unrelated credentials are requested.

Persistence & Privilege

always: false and normal model invocation. The skill asks to store session_id and may use a config directory for local session data, but it does not request system-wide privileges or modify other skills. Autonomous invocation is allowed (platform default) but not by itself a problem here.

What to consider before installing

This skill appears to implement a legitimate remote video-editing integration, but exercise caution before installing: (1) The source/homepage is unknown — verify you trust the nemovideo.ai service. (2) SKILL.md indicates it will obtain and store short-lived anonymous tokens and session IDs (and references ~/.config/nemovideo/) — ask where on disk these tokens are stored and whether they persist beyond your session. (3) There's a metadata mismatch (registry says no config paths, but the SKILL.md declares one) — ask the publisher to clarify. If you proceed, avoid supplying a long-lived or high-privilege NEMO_TOKEN until you confirm token handling and storage behavior; prefer letting the skill obtain a short anonymous token if you don't trust the service. If you need higher assurance, request code or a provenance link (homepage/repo) and inspect where session data is stored and what permissions it receives.

Getting Started

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

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "cut out silences, add transitions, and"

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.

Video Editing AI Jobs — Automate and Export Edited Videos

Send me your raw video footage and describe the result you want. The AI-assisted video editing runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 3-minute unedited interview recording, type "cut out silences, add transitions, and export a clean final cut", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter clips under 2 minutes process significantly faster and use fewer credits.

Matching Input to Actions

User prompts referencing video editing ai jobs, 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: video-editing-ai-jobs
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

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

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

Draft JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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 → "cut out silences, 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 "cut out silences, add transitions, and export a clean final cut" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for widest client and platform compatibility.