Linkedin Editor Ai

Cloud-based linkedin-editor-ai tool that handles editing and optimizing videos for LinkedIn posts and profiles. Upload MP4, MOV, AVI, WebM files (up to 500MB...

Security Scan

The skill's manifest and runtime instructions are internally consistent with a cloud video-editing tool and the single requested credential (NEMO_TOKEN) is appropriate for that purpose, though there are a few minor metadata inconsistencies and privacy considerations to review before installing.

Purpose & Capability

Name/description (LinkedIn video editing) matches the runtime instructions (uploading video files, creating sessions, rendering on a remote service). Requesting a NEMO_TOKEN for API auth is proportionate to the stated purpose.

Instruction Scope

SKILL.md instructs the agent to obtain an anonymous token if NEMO_TOKEN is missing, create sessions, upload local files, and poll rendering endpoints via the nemovideo.ai API. Those actions are expected for a cloud video editor, but they do require the agent to access user-provided local file paths for uploads and to make outbound network calls to mega-api-prod.nemovideo.ai. The skill explicitly advises not to expose raw API responses or token values to users.

Install Mechanism

No install spec or downloaded code is present; this is instruction-only, so nothing is written to disk by an installer step.

Credentials

Only a single credential (NEMO_TOKEN) is declared as required, which is appropriate. However, the YAML frontmatter includes a configPaths entry (~/.config/nemovideo/) while registry metadata lists none — a small inconsistency that could imply the skill expects to read or store config under that path. The SKILL.md itself doesn't instruct broad access to unrelated credentials.

Persistence & Privilege

always is false and the skill does not request system-wide privileges. It instructs storing a session_id for API use, which is normal for a session-based API client.

Assessment

This skill appears to do what it says: it uploads user video files to a nemovideo.ai backend, creates a short-lived anonymous token if you don't provide one, and returns a processed video URL. Before installing, consider: (1) privacy — uploaded videos leave your device and are processed on mega-api-prod.nemovideo.ai; do not upload sensitive footage you don't want transmitted. (2) Token handling — the skill may auto-generate and use an anonymous token (valid ~7 days); if you prefer control, set your own NEMO_TOKEN. (3) Local file access — the agent will need to read file paths you ask it to upload. (4) Metadata inconsistency — the frontmatter references ~/.config/nemovideo/ though registry metadata shows no required config paths; be cautious if the skill starts writing files there. If any of these are unacceptable, do not install or restrict uploads and credentials accordingly.

Getting Started

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

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "trim silences, add captions, and resize"

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.

LinkedIn Editor AI — Edit and Export LinkedIn Videos

Drop your raw video footage in the chat and tell me what you need. I'll handle the AI LinkedIn video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 2-minute talking-head recording for a LinkedIn post, ask for trim silences, add captions, and resize to square format for LinkedIn, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — square (1:1) or vertical (4:5) formats perform better in the LinkedIn feed than widescreen.

Matching Input to Actions

User prompts referencing linkedin editor ai, 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.

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 linkedin-editor-ai, 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 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

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

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.

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)

Common Workflows

Quick edit: Upload → "trim silences, add captions, and resize to square format for LinkedIn" → 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 "trim silences, add captions, and resize to square format for LinkedIn" — 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 best LinkedIn upload compatibility.