Video Trimmer Linux

trim video clips into trimmed MP4 clips with this skill. Works with MP4, MKV, AVI, MOV files up to 500MB. Linux users and developers use it for cutting and t...

Security Scan

The skill's instructions generally match a cloud-based video trimming service, but there are inconsistencies and privacy-sensitivity (automatic anonymous token generation, automatic backend connection, and a mismatch in declared config-path requirements) that warrant caution before installing.

Purpose & Capability

The skill claims to trim videos using a remote GPU backend and its endpoints/flows in SKILL.md align with that purpose. However, the registry metadata above listed no required config paths while the SKILL.md frontmatter includes a config path (~/.config/nemovideo/). That mismatch is unexplained and worth clarifying.

Instruction Scope

Runtime instructions direct the agent to connect automatically to an external service (mega-api-prod.nemovideo.ai), generate/store anonymous tokens if NEMO_TOKEN isn't present, upload user files, and poll render state — all consistent with cloud rendering. The automatic 'first-time connection' behavior (creating a token and starting a session without explicit user approval) and the requirement to upload user videos are privacy-sensitive and should be surfaced to users before execution.

Install Mechanism

This is an instruction-only skill with no install spec and no code files; nothing is written to disk by an installer. Low mechanical installation risk.

Credentials

Only one credential is declared (NEMO_TOKEN / primaryEnv), which is proportionate for a remote service. However, the skill will auto-obtain an anonymous token if NEMO_TOKEN is missing and instructs storing it for subsequent requests — how/where it stores credentials is unspecified. The implied read of install paths to form X-Skill-Platform headers and the discrepancy about configPaths should be clarified.

Persistence & Privilege

always:false and no install script are appropriate; the skill does request storing a session_id/token for ongoing requests but does not declare system-wide changes. Autonomous invocation is allowed (platform default) — combine that with auto-connection and it increases blast radius, but on its own it's not a misconfiguration.

What to consider before installing

This skill implements a cloud-render workflow and will contact mega-api-prod.nemovideo.ai, upload video files, and create/store an anonymous token if you don't provide NEMO_TOKEN. Before installing, consider: (1) Are you comfortable uploading videos (possibly sensitive) to that external domain? (2) Ask the publisher to explain where the token and session_id are stored and whether they persist beyond the session. (3) Clarify the registry vs SKILL.md mismatch about config paths (~/.config/nemovideo/). (4) Prefer explicit user consent before the skill auto-connects on first open. If you need stronger assurances, request the service's privacy/retention policy or a signed/verifiable origin (official homepage or repository) before use.

Getting Started

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

Try saying:

• "trim my video clips"
• "export 1080p MP4"
• "trim the first 2 minutes 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 Trimmer for Linux — Trim and Export Video Clips

Send me your video clips and describe the result you want. The AI video trimming runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 10-minute screen recording in MKV format, type "trim the first 2 minutes and cut the last 30 seconds of dead air", and you'll get a 1080p MP4 back in roughly 20-40 seconds. All rendering happens server-side.

Worth noting: shorter clips under 5 minutes process significantly faster in the browser.

Matching Input to Actions

User prompts referencing video trimmer linux, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is video-trimmer-linux, 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).

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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 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)

Common Workflows

Quick edit: Upload → "trim the first 2 minutes and cut the last 30 seconds of dead air" → Download MP4. Takes 20-40 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 "trim the first 2 minutes and cut the last 30 seconds of dead air" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across Linux media players and editors.