Batch Video

process multiple video files into processed MP4 files with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. marketers use it for applying the sa...

Security Scan

The skill's declared purpose (batch video processing) matches the API calls and token usage, but there's an inconsistency around reading a local config path and no publisher information — ask for clarification before installing or providing credentials.

Purpose & Capability

The name/description (batch video processing) aligns with the runtime instructions which call a cloud render API and accept uploads. Requesting a NEMO_TOKEN is consistent with a cloud service credential. However, the SKILL.md frontmatter mentions a config path (~/.config/nemovideo/) that is not listed in the registry metadata — this is an unexplained mismatch.

Instruction Scope

The SKILL.md explicitly instructs the agent to create sessions, upload user files, stream via SSE, poll status, and include attribution headers — all expected for a remote render service. The concerning bit is the frontmatter reference to a local config path (~/.config/nemovideo/) and the instruction to 'use NEMO_TOKEN if in environment, otherwise generate one' which implies the skill may look for or prefer local credentials/config. There are no instructions to access unrelated files, but the implicit local config access is scope-creep unless justified.

Install Mechanism

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

Credentials

The only declared credential is NEMO_TOKEN, which is proportional to a cloud video service. However, the frontmatter's configPaths entry ( ~/.config/nemovideo/ ) would grant the skill access to files in the user's config directory; that path is not declared in the registry metadata and is not explained in the docs. Requiring both an env token and potentially a config path is disproportionate unless the publisher explains why the local config is needed.

Persistence & Privilege

The skill does not request always:true, does not modify other skills, and has no install-time persistent agent privileges. It runs as an instruction-only skill and can be invoked by the user; autonomous invocation is allowed by default but not, by itself, a red flag.

What to consider before installing

This skill appears to be a legitimate cloud-based batch video processor, but there are two things to check before installing or providing credentials: 1) Source and trust: there is no publisher homepage or provenance. Verify the nemovideo domain and the publisher identity. Ask for a homepage, documentation, or a company/maintainer identity you trust before sending private or sensitive videos. 2) Local config access: SKILL.md frontmatter references ~/.config/nemovideo/ but the registry metadata did not list any required config paths. Ask the publisher why that local config is needed and whether the skill will read any files there. If you do not want the skill to access local config, prefer using an anonymous starter token (the skill documents how to request one) rather than supplying a long-lived NEMO_TOKEN. Also consider data-privacy questions: where uploaded videos are stored, retention period, whether outputs are private, and whether the service encrypts data at rest/in transit. If you must provide NEMO_TOKEN, prefer creating a scoped/short-lived credential and test with non-sensitive content first.

Getting Started

Send me your multiple video files and I'll handle the bulk video processing. Or just describe what you're after.

Try saying:

• "process ten 30-second product clips into a 1080p MP4"
• "add a logo watermark and trim the first 3 seconds from all videos"
• "applying the same edits to multiple videos at once for marketers"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

• Generate a UUID as client identifier
• POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
• The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Batch Video — Process Multiple Videos at Once

This tool takes your multiple video files and runs bulk video processing through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have ten 30-second product clips and want to add a logo watermark and trim the first 3 seconds from all videos — the backend processes it in about 1-3 minutes and hands you a 1080p MP4.

Tip: group clips with similar lengths together so batch jobs finish at a consistent pace.

Matching Input to Actions

User prompts referencing batch 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.

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	batch-video
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

• "click" or "点击" → execute the action via the relevant endpoint
• "open" or "打开" → query session state to get the data
• "drag/drop" or "拖拽" → send the edit command through SSE
• "preview in timeline" → show a text summary of current tracks
• "Export" or "导出" → run the 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 "add a logo watermark and trim the first 3 seconds from all videos" — 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 → "add a logo watermark and trim the first 3 seconds from all videos" → Download MP4. Takes 1-3 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.