Photo Video Maker Linkedin

turn photos and images into LinkedIn-ready video with this photo-video-maker-linkedin skill. Works with JPG, PNG, HEIC, WebP files up to 200MB. professionals...

Security Scan

The skill's declared purpose (turning photos into LinkedIn-ready videos) aligns with its runtime instructions and required credential (NEMO_TOKEN); the package is instruction-only and primarily performs network calls to the nemo video API — nothing in the bundle appears to be trying to do unrelated or hidden work, though there are a few small inconsistencies to review before installing.

Purpose & Capability

Name/description, endpoints, and required credential (NEMO_TOKEN) all describe a cloud video-rendering integration. Requiring a service token for nemo video is appropriate for this functionality. One minor inconsistency: the registry metadata shown earlier listed no required config paths, but the SKILL.md frontmatter includes a configPaths entry (~/.config/nemovideo/). That mismatch should be clarified but does not by itself contradict the stated purpose.

Instruction Scope

The SKILL.md instructs the agent to (a) call nemo API endpoints, (b) upload user-supplied files, (c) stream SSE responses, and (d) generate an anonymous token if NEMO_TOKEN is missing. These actions are consistent with a cloud rendering workflow. The file also asks the agent to auto-detect a platform value from an install path for an attribution header, which implies inspecting the agent's install path (filesystem) — this is an extra read beyond API calls and file uploads and should be noted. The instructions do not request unrelated system secrets or broad data collection.

Install Mechanism

This is an instruction-only skill with no install spec and no code files, so nothing is being downloaded or written by an installer. That is the lowest-risk install model.

Credentials

Only one credential (NEMO_TOKEN) is declared as required and is appropriate for a third-party rendering API. The SKILL.md also describes creating a short-lived anonymous token if NEMO_TOKEN is absent. The aforementioned frontmatter config path (~/.config/nemovideo/) is present in SKILL.md but not reflected in the registry’s required config paths; clarify whether the skill will read or write that path. No other unrelated secrets are requested.

Persistence & Privilege

always is false and the skill does not request elevated or permanent platform-wide privileges. It will operate by calling external APIs and may persist a session_id (normal for session-based APIs) — the instructions do not indicate modifying other skills or system-wide configuration.

Assessment

This skill appears to do what it claims: it uploads images and calls the nemo video API to produce MP4s and needs a NEMO_TOKEN (or it will create an anonymous token). Before installing: (1) confirm you trust the nemo API domain (mega-api-prod.nemovideo.ai) and its privacy policy because you will be uploading images to that service; (2) verify the NEMO_TOKEN source — prefer a token you control rather than anonymous tokens if you care about auditability; (3) ask the publisher to clarify the frontmatter config path (~/.config/nemovideo/) and whether the skill will read/write that directory or any other files; and (4) avoid uploading sensitive personal data or corporate secrets unless you have confirmed the service’s security and retention practices.

Getting Started

Send me your photos and images and I'll handle the AI video creation. Or just describe what you're after.

Try saying:

• "turn five professional headshots and company logo into a 1080p MP4"
• "turn my photos into a 30-second LinkedIn intro video with text overlays and background music"
• "creating professional LinkedIn videos from static photos for professionals and marketers"

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.

Photo Video Maker for LinkedIn — Turn Photos into LinkedIn Videos

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

Say you have five professional headshots and logo and want to turn my photos into a 30-second LinkedIn intro video with text overlays and background music — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: use a 1920x1080 landscape crop on your photos before uploading for the cleanest result.

Matching Input to Actions

User prompts referencing photo video maker linkedin, 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint	Method	Purpose
/api/tasks/me/with-session/nemo_agent	POST	Start a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_sse	POST	Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

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

Header	Value
X-Skill-Source	photo-video-maker-linkedin
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

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 says	You 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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn my photos into a 30-second LinkedIn intro video with text overlays and background music" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, HEIC, WebP for the smoothest experience.

Export as MP4 for widest compatibility with LinkedIn's native video player.

Common Workflows

Quick edit: Upload → "turn my photos into a 30-second LinkedIn intro video with text overlays and background music" → Download MP4. Takes 30-60 seconds 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.