Trimmer App

Skip the learning curve of professional editing software. Describe what you want — trim the first 30 seconds and cut the dead air at the end — and get trimme...

Security Scan

The skill's declared needs mostly match a cloud video-trimming service, but there are small inconsistencies around the required NEMO_TOKEN and a declared config path that aren't fully justified by the instructions—verify token handling and where uploads/tokens are stored before installing.

Purpose & Capability

The skill is described as a cloud video-trimming front end and all runtime instructions call a remote video-processing API (upload, render, status, credits). That matches the stated purpose. However, the registry declares NEMO_TOKEN as a required environment variable and a config path (~/.config/nemovideo/) even though the SKILL.md explicitly supports creating an anonymous token if NEMO_TOKEN is absent. The presence of the config path in metadata is not explained by the instructions.

Instruction Scope

SKILL.md limits actions to contacting the nemovideo backend (auth, session, upload, render, credits, state) and streaming SSE. It does not instruct reading arbitrary user files or system secrets beyond the NEMO_TOKEN. It does ask to detect install path to set X-Skill-Platform header, which may require inspecting where the skill is located, but otherwise stays within its editing remit.

Install Mechanism

There is no install spec and no code files; the skill is instruction-only, so nothing is written to disk by an installer. This is the lowest-risk install mechanism.

Credentials

The skill declares a single primary env var (NEMO_TOKEN), which is reasonable for a cloud API. However, the metadata marks it required while the SKILL.md provides a fallback anonymous-token flow if the variable is missing. The metadata also lists a config path (~/.config/nemovideo/) that the instructions never explicitly read or write—this mismatch could indicate sloppy metadata or an undocumented persistence behaviour (e.g., storing tokens locally). Verify whether tokens or job metadata are stored locally and whether providing your own NEMO_TOKEN is necessary or safe.

Persistence & Privilege

The skill is not force-included (always: false) and requests no special platform privileges. It does not instruct modifying other skills or global agent settings. Autonomous invocation is permitted (the platform default) but not combined with other high-risk indicators here.

What to consider before installing

This skill appears to be a thin client for the nemovideo.ai cloud trimming service and will upload your video files to that backend. Before installing or providing credentials: 1) Confirm you trust https://mega-api-prod.nemovideo.ai and review its privacy/retention policy for uploaded media. 2) Note the metadata says NEMO_TOKEN is required but the instructions can create an anonymous token—if you set your own NEMO_TOKEN, it may grant longer access/credits; only provide it if you trust the service. 3) Ask the author whether the skill will store tokens or job data under ~/.config/nemovideo/ (metadata lists this path but SKILL.md doesn’t explain it); if so, consider where that data is stored and its protections. 4) If you have sensitive footage, avoid uploading it until you validate the backend. Given the metadata/instructions mismatch, treat the skill as coherent with caution and request clarification from the publisher before trusting private content or supplying a permanent token.

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 30 seconds and"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Trimmer App — Trim and Export Video Clips

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

Here's a typical use: you send a a 10-minute raw interview recording, ask for trim the first 30 seconds and cut the dead air at the end, and about 20-40 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter source clips process faster and use fewer credits.

Matching Input to Actions

User prompts referencing trimmer app, 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 trimmer-app, 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.

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 30 seconds and cut the dead air at the end" → 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 30 seconds and cut the dead air at the end" — 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.