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Video Content

v1.0.0

Get polished video clips ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something lik...

0· 59·0 current·0 all-time

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for vynbosserman65/video-content.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Video Content" (vynbosserman65/video-content) from ClawHub.
Skill page: https://clawhub.ai/vynbosserman65/video-content
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required env vars: NEMO_TOKEN
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install video-content

ClawHub CLI

Package manager switcher

npx clawhub@latest install video-content
Security Scan
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OpenClawOpenClaw
Suspicious
medium confidence
Purpose & Capability
Name/description align with the API endpoints and the single required env var (NEMO_TOKEN). However, the SKILL.md frontmatter includes a configPaths entry (~/.config/nemovideo/) while the registry metadata lists no required config paths — this mismatch is unexplained and suggests either stale metadata or hidden file access expectations.
Instruction Scope
The SKILL.md gives explicit runtime instructions for creating/using an anonymous token, creating sessions, uploading video, streaming SSE, polling render status, and returning download URLs — all consistent with a cloud video-editing service. It requires uploading user files (expected) and instructs 'auto-detect' of platform from an install path (which may require access to agent install path or local filesystem). The instructions do not ask for unrelated secrets, but the auto-detect/install-path requirement and the frontmatter configPaths are scope elements to verify.
Install Mechanism
Instruction-only skill with no install spec or code files — lowest-risk install mechanism. Nothing is downloaded or written to disk by an installer.
Credentials
Only one credential (NEMO_TOKEN) is required and is necessary to call the service. The SKILL.md also documents how to obtain a short-lived anonymous token via the service's anonymous-token endpoint. The earlier-noted frontmatter configPaths entry is unexplained and could imply reading local config files; that should be clarified.
Persistence & Privilege
always is false and the skill does not request elevated or persistent system privileges. It asks the agent to save session_id for the session lifecycle (normal). There is no instruction to modify other skills or system-wide agent settings.
What to consider before installing
This skill appears to be what it says — a cloud video-editing integration that needs a single service token. Before installing, check the following: 1) Verify the service domain (mega-api-prod.nemovideo.ai) is legitimate for the provider you expect and that the skill owner is known/trusted; 2) Confirm whether the skill will actually read ~/.config/nemovideo/ or any local install path (the SKILL.md frontmatter lists that path but registry metadata did not) — ask the publisher why it's listed; 3) Prefer using the anonymous short-lived token flow described rather than supplying a long-lived token; 4) Remember any uploaded videos will be sent to the vendor's servers — do not upload sensitive footage unless you trust their privacy policy; 5) If you need higher assurance, ask the publisher for a homepage or source repo so you can inspect exact behavior and headers (the skill requires specific X-Skill-* headers that the agent will attach). Provide those details and/or the skill's source code and I can re-evaluate with higher confidence.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🎬 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk971bbamy3v3rc72q5gywmxfb584wcw2
59downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "edit my raw video footage"
  • "export 1080p MP4"
  • "cut the pauses, add background music,"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

Video Content — Edit and Export Video Clips

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 screen recording or phone video and want to cut the pauses, add background music, and export as a short clip — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 60 seconds process significantly faster.

Matching Input to Actions

User prompts referencing video content, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...ActionSkip 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.

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

HeaderValue
X-Skill-Sourcevideo-content
X-Skill-Versionfrontmatter version
X-Skill-Platformauto-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.

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.

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 saysYou 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)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "cut the pauses, add background music, and export as a short clip" — 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 and devices.

Common Workflows

Quick edit: Upload → "cut the pauses, add background music, and export as a short clip" → 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.

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