Ai Video Editor In Chennai

Skip the learning curve of professional editing software. Describe what you want — trim the footage, add Tamil and English subtitles, and export a clean reel...

Security Scan

The skill's requested credential and behavior are coherent with a cloud-based AI video editing service: it needs a NEMO_TOKEN, uploads local videos to nemovideo.ai, and manages sessions — nothing obviously unrelated or deceptive was found, but the source is unknown so exercise caution.

Purpose & Capability

Name/description, required env var (NEMO_TOKEN), and config path (~/.config/nemovideo/) all match a cloud video-processing backend. No unrelated binaries, credentials, or services are requested.

Instruction Scope

Instructions are focused on authenticating, creating a session, uploading video files, handling SSE streams, and polling renders — all consistent with an editor service. Note: the skill instructs generating an anonymous token by calling an external API and to store session_id/token for subsequent calls. It also infers an install path to set an X-Skill-Platform header (may cause the agent to inspect common install paths). These are reasonable for the stated purpose but mean the agent will contact an external host and upload user files.

Install Mechanism

Instruction-only skill with no install spec or code to write to disk. Lowest install risk.

Credentials

Only one credential (NEMO_TOKEN) is required and declared as primary; the declared config path aligns with the backend. No unrelated secrets or many environment variables requested.

Persistence & Privilege

always:false (normal). The skill permits autonomous model invocation (default), which is expected for skills that call a backend. Combined with network uploads and token use, autonomous invocation increases blast radius — users should be aware the agent can upload files and contact nemovideo.ai without interactive confirmation if invoked autonomously.

Assessment

This skill appears internally consistent for a cloud-based video editor, but it will contact an external domain (mega-api-prod.nemovideo.ai), may generate and store anonymous tokens, and will upload your local video files to that service. Before installing: (1) verify you trust nemovideo.ai (check a homepage/privacy policy or vendor identity — none was provided in the skill metadata), (2) avoid uploading sensitive or private footage unless you confirm retention and sharing policies, (3) consider pre-setting your own NEMO_TOKEN instead of allowing anonymous token generation so you control credentials, (4) be aware the agent may run autonomously and upload files without extra prompts — disable autonomous invocation if you want manual control, and (5) inspect any local config (~/.config/nemovideo/) the skill might write to and remove tokens when done. If you want higher assurance, ask the publisher for a homepage, privacy policy, and an official SDK or release URL.

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

• "edit a 2-minute raw event recording shot on a phone in Chennai into a 1080p MP4"
• "trim the footage, add Tamil and English subtitles, and export a clean reel"
• "editing local business or event videos with AI assistance for Chennai-based content creators and small business owners"

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 Editor in Chennai — Edit and Export Local Videos

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

Here's a typical use: you send a a 2-minute raw event recording shot on a phone in Chennai, ask for trim the footage, add Tamil and English subtitles, and export a clean reel, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips under 60 seconds process significantly faster.

Matching Input to Actions

User prompts referencing ai video editor in chennai, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-video-editor-in-chennai, 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).

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 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.

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 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 → "trim the footage, add Tamil and English subtitles, and export a clean reel" → 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 Tamil and English subtitles, and export a clean reel" — 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 social platforms.