Video For Social Media Course

Turn a 3-minute talking-head recording shot on a phone into 1080p social-ready video clips just by typing what you need. Whether it's repurposing course or t...

Security Scan

The skill's requests and runtime instructions align with a cloud-based video-editing integration; there is a minor metadata inconsistency about a local config path but nothing that obviously conflicts with its stated purpose.

Purpose & Capability

Name/description describe a cloud video editing service and the skill only requires a single service token (NEMO_TOKEN) and makes API calls to a nemo video backend—this is proportionate to the stated purpose.

Instruction Scope

SKILL.md is an instruction-only runtime plan that tells the agent to check NEMO_TOKEN, optionally obtain an anonymous token via a POST to the vendor API, create sessions, upload files, stream SSE responses, and poll render status. Those actions are within the expected scope for a remote render service. One minor note: it asks to auto-detect X-Skill-Platform from the install path which may require the agent to inspect its environment/install path; the instructions do not explicitly specify how to detect it or what exact filesystem reads are needed.

Install Mechanism

No install spec or code files—instruction-only. No downloads or installs are requested, which is the lowest-risk install mechanism.

Credentials

Only NEMO_TOKEN is declared as required/primary, which matches the API Authorization usage in the instructions. However, SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while registry metadata recorded no required config paths—this metadata mismatch should be clarified. Otherwise the environment access requested is limited and proportionate.

Persistence & Privilege

always is false and the skill does not request elevated platform privileges. It stores a session_id for operations (expected) and does not ask to modify other skills or system-wide settings.

Assessment

This skill uploads your media and control data to a remote service (mega-api-prod.nemovideo.ai) and uses a single token (NEMO_TOKEN). Before installing: 1) Confirm you trust the nemo/video domain and understand their storage/privacy policy for uploaded videos and generated clips. 2) Note the skill can create an anonymous short-lived token if you don't provide NEMO_TOKEN — that will send requests on your behalf to the vendor. If you have sensitive content, avoid using an anonymous flow or the service. 3) Ask the publisher to clarify the configPaths discrepancy (~/.config/nemovideo/ appears in SKILL.md frontmatter but was not declared in the registry metadata). 4) If you want tighter control, supply your own account token (NEMO_TOKEN) rather than relying on anonymous issuance and verify what data the vendor retains. Overall the skill appears coherent for cloud-based video editing, but verify trust and privacy before uploading private footage.

Getting Started

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

Try saying:

• "convert a 3-minute talking-head recording shot on a phone into a 1080p MP4"
• "cut this into 5 short clips optimized for Instagram Reels and TikTok with captions"
• "repurposing course or tutorial footage into short-form social media videos for online course creators and social media marketers"

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.

Video for Social Media Course — Turn Course Footage Into Social Clips

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

A quick example: upload a 3-minute talking-head recording shot on a phone, type "cut this into 5 short clips optimized for Instagram Reels and TikTok with captions", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: splitting your course video into chapters before uploading helps the AI cut more accurate short clips.

Matching Input to Actions

User prompts referencing video for social media course, 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	video-for-social-media-course
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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

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

SSE Event Handling

Event	Action
Text response	Apply GUI translation (§4), present to user
Tool call/result	Process internally, don't forward
heartbeat / empty data:	Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes	Process 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.

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

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 → "cut this into 5 short clips optimized for Instagram Reels and TikTok with captions" → 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 "cut this into 5 short clips optimized for Instagram Reels and TikTok with captions" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for widest compatibility across Instagram, TikTok, and YouTube Shorts.