Ai Video Editor Kaise Kare

edit raw video footage into edited video clips with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. Hindi-speaking content creators use it for...

Security Scan

The skill's requests and runtime instructions are broadly consistent with a cloud-based AI video editor, but there are a few small metadata/instruction mismatches and privacy considerations to review before installing.

Purpose & Capability

The skill is a cloud video-editing front-end and only requires a single service credential (NEMO_TOKEN), which matches the stated purpose. Minor inconsistency: the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) while the registry metadata lists no required config paths — this mismatch should be clarified.

Instruction Scope

Runtime instructions focus on connecting to the nemovideo backend, creating sessions, uploading videos, streaming edits, polling render status, and returning download URLs. The instructions do not ask the agent to read unrelated local files or other environment variables. They do instruct the agent to automatically obtain an anonymous token if NEMO_TOKEN is absent and to store session_id for subsequent requests — behavior consistent with a cloud workflow.

Install Mechanism

This is an instruction-only skill with no install spec and no code files, so nothing is written to disk by an installer. That makes the install risk low.

Credentials

Only NEMO_TOKEN is declared as required which is proportionate for a cloud editing service. The skill also documents an anonymous-token flow so it can operate without a pre-provided token; that's reasonable but means the skill will make outbound requests to obtain and use auth tokens. Confirm whether NEMO_TOKEN is used elsewhere in your environment before supplying it. The frontmatter's configPaths hint at a local config directory (~/.config/nemovideo/) but the registry metadata omits it — clarify whether the skill will read local config files.

Persistence & Privilege

The skill does not request always:true, does not modify other skills, and only asks to store its own session_id for ongoing interactions. Autonomous invocation is allowed (platform default) and appropriate for this interactive skill.

Assessment

This skill appears to be a legitimate cloud video-editing front-end that communicates with https://mega-api-prod.nemovideo.ai. Before installing: (1) verify you trust that endpoint and its privacy/data retention practices — you will upload raw video (possibly sensitive) to that service; (2) confirm what NEMO_TOKEN represents and whether it is tied to other accounts or services in your environment before providing it; (3) ask the publisher to clarify the conflicting metadata about ~/.config/nemovideo/ (does the skill read or write local config files?); (4) note the skill can auto-request anonymous tokens if no token is supplied — meaning it will make outbound auth requests on first run. Because the package is instruction-only and contains no install steps, there is low install risk, but exercise normal caution with content you upload to third-party cloud services.

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 phone recording into a 1080p MP4"
• "trim unnecessary parts, add transitions, and insert background music"
• "editing raw videos into polished clips using AI for Hindi-speaking content creators"

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 Kaise Kare — Edit and Export Videos with AI

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 2-minute raw phone recording and want to trim unnecessary parts, add transitions, and insert background music — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 60 seconds process faster and give cleaner AI results.

Matching Input to Actions

User prompts referencing ai video editor kaise kare, 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	ai-video-editor-kaise-kare
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

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

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 unnecessary parts, add transitions, and insert background music" — 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 like YouTube and Instagram.

Common Workflows

Quick edit: Upload → "trim unnecessary parts, add transitions, and insert background music" → 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.