Objectremover Video

Skip the learning curve of professional editing software. Describe what you want — remove the car parked in the background from the entire video — and get cl...

Security Scan

The skill mostly matches its stated purpose (uploading videos to a cloud service for object removal) but contains inconsistencies and instructions that could lead to unexpected filesystem access and automatic token creation/usage — ask clarifying questions before installing.

Purpose & Capability

The skill's name/description (remote AI object removal) aligns with the actions described (upload, render, export). Requested primary credential (NEMO_TOKEN) is appropriate for a cloud API. However, the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata earlier reported no required config paths — this mismatch should be clarified (will the skill read/write local config?).

Instruction Scope

Instructions direct the agent to perform network operations (anonymous token generation, session creation, SSE streaming, file uploads) which are expected for a cloud-render service. They also instruct 'auto-detect' platform from the install path and reference a local config path — that implies the agent may read filesystem locations beyond just environment variables. The skill also auto-generates and uses anonymous tokens if NEMO_TOKEN is not present; this behavior should be explicit to the user and the storage location for created tokens should be confirmed.

Install Mechanism

No install spec or code files are present (instruction-only). That minimizes disk-write risk; the skill does not download or install arbitrary third-party code.

Credentials

Only NEMO_TOKEN is declared (primary credential), which is proportionate. But the SKILL.md will create/use an anonymous NEMO_TOKEN if none is provided and may expect or write config under ~/.config/nemovideo/ (frontmatter). The registry said no config paths, so it's unclear whether the skill will persist tokens or client IDs locally. Automatic token creation and implicit storage increases privacy/credential risk if not made explicit.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. Still, its runtime behavior includes creating tokens and long-lived sessions/SSE connections and may persist a client id or token to a config path (per frontmatter). Confirm whether the skill will persist credentials or other state and where.

What to consider before installing

This skill will upload your videos to an external service (mega-api-prod.nemovideo.ai) for processing and will use or create a NEMO_TOKEN for API access. Before installing: 1) Confirm the service/operator and privacy policy for uploaded media (sensitive videos will leave your device). 2) Prefer supplying your own NEMO_TOKEN if you want control over identity/credentials rather than letting the skill obtain an anonymous token. 3) Ask the publisher to clarify the config path behavior: will the skill read/write ~/.config/nemovideo/ or other local files? Where are generated tokens/client IDs stored and for how long? 4) If you are uncomfortable with automatic uploads or persistent tokens, do not install or set disable-model-invocation/autonomous usage to restrict automatic runs. 5) Because this is instruction-only (no code to audit), treat network calls and credential creation as the primary risk surface.

Getting Started

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

Try saying:

• "edit my video clips"
• "export 1080p MP4"
• "remove the car parked in the"

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.

Object Remover Video — Remove Objects from Video Clips

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

A quick example: upload a 30-second clip with a person walking in the background, type "remove the car parked in the background from the entire video", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter clips with less motion around the target object produce cleaner results.

Matching Input to Actions

User prompts referencing objectremover video, 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.

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

Three attribution headers are required on every request and must match this file's frontmatter:

Header	Value
X-Skill-Source	objectremover-video
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

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

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.

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 "remove the car parked in the background from the entire video" — 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.

Common Workflows

Quick edit: Upload → "remove the car parked in the background from the entire video" → 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.