Editor Highlight

v1.0.0

Turn a 30-minute interview recording into 1080p highlight reel clips just by typing what you need. Whether it's automatically cutting the best moments from l...

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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 mhogan2013-9/editor-highlight.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Editor Highlight" (mhogan2013-9/editor-highlight) from ClawHub.
Skill page: https://clawhub.ai/mhogan2013-9/editor-highlight
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 editor-highlight

ClawHub CLI

Package manager switcher

npx clawhub@latest install editor-highlight
Security Scan
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Purpose & Capability
The skill claims to perform cloud-based highlight extraction and its instructions consistently call a nemo video API, require a NEMO_TOKEN, and describe upload, SSE, session, and export endpoints — all appropriate for the stated purpose. Minor metadata mismatch: the skill registry lists no required config paths but the SKILL.md frontmatter references ~/.config/nemovideo/ (likely informational).
Instruction Scope
Instructions are focused on connecting to nemovideo.ai, creating a session, uploading files, and exporting rendered media. It explicitly instructs how to obtain an anonymous token, how to send SSE and upload video, and how to poll for results. Note: the workflow will upload user video to an external service (expected for this skill). The document also says to detect install platform from common install paths (~/.clawhub/, ~/.cursor/skills/), which implies the agent may inspect its environment to set an X-Skill-Platform header — this is minor but worth being aware of.
Install Mechanism
Instruction-only skill with no install spec and no code files. No packages or external downloads are performed by an installer, which minimizes on-disk risk.
Credentials
Only NEMO_TOKEN is required (declared as primaryEnv) and that is appropriate for authenticating to the described cloud API. The SKILL.md also documents a way to request an anonymous token if one isn't provided. No unrelated secrets or excessive env vars are requested.
Persistence & Privilege
Skill does not request always:true and is user-invocable. It instructs saving session_id and using the session to poll job status (normal for long-running cloud jobs). No instructions to modify other skills or system-wide config.
Assessment
This skill will upload any videos you give it to the nemovideo.ai backend and requires a NEMO_TOKEN (which can be obtained anonymously via the documented API). Before installing/using: (1) confirm you trust the nemovideo.ai service and are comfortable uploading the footage (check privacy/billing policies), (2) understand that a session_id will be stored to track render jobs and jobs may continue on the cloud if you close the client, (3) the skill may inspect install paths to set an attribution header (benign but note it may read agent environment metadata), and (4) do not supply unrelated sensitive credentials — only NEMO_TOKEN is needed. If you need higher assurance, ask the publisher for a canonical homepage, privacy policy, and API documentation for mega-api-prod.nemovideo.ai before proceeding.

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

Runtime requirements

✂️ Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk9701gg1y32j1tp2r92p3r2bb185ngfq
45downloads
0stars
1versions
Updated 23h ago
v1.0.0
MIT-0

Getting Started

Got raw video footage to work with? Send it over and tell me what you need — I'll take care of the AI highlight extraction.

Try saying:

  • "extract a 30-minute interview recording into a 1080p MP4"
  • "pull the best moments and compile them into a 2-minute highlight reel"
  • "automatically cutting the best moments from long recordings for content creators"

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.

Editor Highlight — Extract and Export Key Moments

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

Say you have a 30-minute interview recording and want to pull the best moments and compile them into a 2-minute highlight reel — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter source clips produce more accurate highlight detection.

Matching Input to Actions

User prompts referencing editor highlight, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is editor-highlight, 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).

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

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 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)

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

Common Workflows

Quick edit: Upload → "pull the best moments and compile them into a 2-minute highlight reel" → 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 "pull the best moments and compile them into a 2-minute highlight reel" — 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.

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