Ai Video Bootcamp

v1.0.0

Skip the learning curve of professional editing software. Describe what you want — cut this into short lessons, add chapter titles, and export as a course-re...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

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

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

ClawHub CLI

Package manager switcher

npx clawhub@latest install ai-video-bootcamp
Security Scan
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Benign
high confidence
Purpose & Capability
Name/description: cloud video-to-course processing. Declared primary credential NEMO_TOKEN and config path (~/.config/nemovideo/) match the documented API host (mega-api-prod.nemovideo.ai) and session-based flow — these are expected for a cloud backend service.
Instruction Scope
SKILL.md instructs the agent to check NEMO_TOKEN, optionally obtain an anonymous token, create a session, upload video files, poll render status, and return download URLs. All network calls target the same nemovideo API and are consistent with the video-processing use case. The file-upload behavior implies the agent will accept user-supplied files (or paths) for upload — expected for this skill. The doc does reference derivation of attribution headers from YAML/install path (detecting install path to set X-Skill-Platform), which requires the agent to inspect installation location if implemented, but this is explainable and not excessive.
Install Mechanism
Instruction-only skill (no install spec, no code files). Lowest-risk delivery: nothing is written to disk by an installer in the skill bundle itself.
Credentials
Only NEMO_TOKEN is required as the primary credential. The skill also supports obtaining an anonymous token from the same API if no token is present. No unrelated secrets or multiple service credentials are requested.
Persistence & Privilege
always:false and no install actions. The skill expects to store and use session_id during operation (normal for a session-based API). It does not request persistent platform-wide privileges or modify other skills.
Assessment
This skill uploads user-supplied video files to a cloud backend (mega-api-prod.nemovideo.ai) and requires a NEMO_TOKEN (or will request an anonymous token). Before using: (1) Confirm you trust the nemovideo.ai domain and review its privacy/retention policy for uploaded media; (2) avoid uploading sensitive or private video content unless you accept remote processing and storage; (3) prefer an ephemeral anonymous token if you don't want to supply an account token; (4) the skill may need to read install path or local file paths to form headers and upload files — ensure it only accesses files you explicitly provide; (5) because this is instruction-only, the runtime implementation (the agent) will perform API calls — verify network activity if you have monitoring. Overall the skill appears internally consistent with its stated purpose.

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

Runtime requirements

🎓 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk9710mg8d26s4bzrwg0xahgcc185j5e4
42downloads
0stars
1versions
Updated 1d ago
v1.0.0
MIT-0

Getting Started

Ready when you are. Drop your video clips here or describe what you want to make.

Try saying:

  • "create a 10-minute screen recording of a tutorial session into a 1080p MP4"
  • "cut this into short lessons, add chapter titles, and export as a course-ready video"
  • "turning raw recordings into structured AI video bootcamp lessons for educators and course creators"

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.

AI Video Bootcamp — Turn Recordings Into Course Videos

This tool takes your video clips and runs AI video training through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 10-minute screen recording of a tutorial session and want to cut this into short lessons, add chapter titles, and export as a course-ready video — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: breaking your content into 5-10 minute segments before uploading speeds up processing significantly.

Matching Input to Actions

User prompts referencing ai video bootcamp, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-video-bootcamp, 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).

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

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

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 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 "cut this into short lessons, add chapter titles, and export as a course-ready 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 with learning management systems.

Common Workflows

Quick edit: Upload → "cut this into short lessons, add chapter titles, and export as a course-ready 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.

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