Skill flagged — suspicious patterns detected

ClawHub Security flagged this skill as suspicious. Review the scan results before using.

Video Editing Ai Descript

v1.0.0

Get transcript-edited videos ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something...

0· 42·0 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 vcarolxhberger/video-editing-ai-descript.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Video Editing Ai Descript" (vcarolxhberger/video-editing-ai-descript) from ClawHub.
Skill page: https://clawhub.ai/vcarolxhberger/video-editing-ai-descript
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-editing-ai-descript

ClawHub CLI

Package manager switcher

npx clawhub@latest install video-editing-ai-descript
Security Scan
VirusTotalVirusTotal
Benign
View report →
OpenClawOpenClaw
Suspicious
medium confidence
Purpose & Capability
Name and description match a cloud video-editing service and the primary credential (NEMO_TOKEN) is consistent with that purpose. However, the registry metadata and the SKILL.md disagree: the top-level metadata reported no required config paths, but the SKILL.md frontmatter metadata lists configPaths (~/.config/nemovideo/). Also the registry lists NEMO_TOKEN as required, yet the SKILL.md instructs the agent to generate an anonymous token if NEMO_TOKEN is not present. These contradictions reduce trustworthiness.
!
Instruction Scope
Runtime instructions perform expected actions for a remote render pipeline (session creation, SSE messaging, uploads, polling exports). But they also direct the agent to automatically POST to an external API to obtain anonymous tokens (and store them), detect the agent's install path to set X-Skill-Platform, and read YAML frontmatter for version/source headers. The instructions also tell the agent not to display raw API responses or token values — which is reasonable for security but could hide sensitive details. The combination (auto-create/store tokens, probe install path, and suppress display) is scope-expanding compared with a simple ‘upload & render’ helper and should be understood.
Install Mechanism
No install spec and no code files — instruction-only skill. This is the lowest install risk because nothing is downloaded or written by a packaged installer.
!
Credentials
Only one credential (NEMO_TOKEN) is declared as primary, which is proportionate for a cloud API. However, SKILL.md both treats NEMO_TOKEN as required and simultaneously provides a flow to create an anonymous token if it's missing — a contradiction. The frontmatter's request to reference ~/.config/nemovideo/ (not declared in registry requirements) suggests the skill may read local config files. Those inconsistencies are unclear and warrant caution.
Persistence & Privilege
Skill does ask to 'store the returned session_id' and to reuse tokens for requests, but it does not request always: true, nor does it ask to modify other skills or system-wide settings. Persisting a session/token for the skill's own API calls is expected behavior for a remote-render integration.
What to consider before installing
This skill appears to call a remote video-processing API and will upload your footage to that service. Before installing: 1) Confirm you trust the domain (mega-api-prod.nemovideo.ai) and its privacy terms — uploaded media and transcripts will be sent there. 2) Ask the developer to clarify the NEMO_TOKEN requirement: the registry marks it as required but the skill can auto-generate an 'anonymous' token; decide whether you want the agent to create/store tokens automatically. 3) Request clarification about reading config paths / install-path detection — if you don't want the skill probing your filesystem or agent install path, get an explicit statement of what it reads and why. 4) If you require auditability, ask for logs or confirm that the skill will not hide API responses or tokens from administrators. If these questions aren't answered clearly, consider treating this skill as potentially risky for sensitive content.

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

Runtime requirements

🎙️ Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97d591dzpybcx7tt7g2r1r04s85j6sz
42downloads
0stars
1versions
Updated 2d ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "edit my raw video footage"
  • "export 1080p MP4"
  • "remove filler words, trim silences, 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 Editing AI Descript — Edit Videos by Editing Text

Send me your raw video footage and describe the result you want. The AI transcript-based editing runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 10-minute interview recording in MP4, type "remove filler words, trim silences, and add captions automatically", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter segments under 5 minutes process significantly faster and give cleaner transcript results.

Matching Input to Actions

User prompts referencing video editing ai descript, 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.

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.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: video-editing-ai-descript
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

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

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

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

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess 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.

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 → "remove filler words, trim silences, and add captions automatically" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "remove filler words, trim silences, and add captions automatically" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for widest compatibility across platforms.

Comments

Loading comments...