Video Leaderboard Ai

Turn a batch of 10 short marketing video clips into 1080p ranked video results just by typing what you need. Whether it's comparing and ranking videos by AI-...

Security Scan

The skill's behavior mostly matches a cloud video-rendering service (it asks for a NEMO_TOKEN and calls nemo API endpoints), but there are metadata inconsistencies and provenance gaps (no homepage/source) that warrant caution before installing or providing credentials.

Purpose & Capability

The skill claims to perform cloud video ranking and rendering and its runtime instructions call a nemo video API and require NEMO_TOKEN — this is coherent. However the package has no homepage or source listed (owner is an opaque ID), which reduces trust in provenance. Also the SKILL.md frontmatter declares a required config path (~/.config/nemovideo/) while the registry metadata did not — an inconsistency.

Instruction Scope

SKILL.md instructs only to read/obtain a NEMO_TOKEN, create a session, upload files, read SSE and poll status — all consistent with a cloud video-processing flow. It does not instruct reading unrelated files or other environment variables. It does mention deriving an attribution header from the agent install path (e.g., ~/.clawhub/ or ~/.cursor/skills/) which implies the agent may inspect its install path, but that is limited and expected for attribution.

Install Mechanism

There is no install spec and no code files — instruction-only skills have the lowest disk/write risk. No downloads or package installs are requested.

Credentials

Only one credential is requested (NEMO_TOKEN), which is appropriate for a remote rendering API. The skill will also obtain an anonymous token from the service if none is present. That behavior is plausible but important: providing an existing NEMO_TOKEN gives the skill access to your account on the service, so users should only supply it if they trust the provider. The SKILL.md frontmatter's configPaths (~/.config/nemovideo/) suggests possible filesystem access that is not otherwise referenced in the runtime instructions — this mismatch should be clarified.

Persistence & Privilege

The skill does not request always:true and defaults allow user invocation and autonomous invocation (the platform default). It does not request system-wide configuration changes or other skills' credentials.

What to consider before installing

This skill appears functionally coherent for a cloud video-ranking/rendering service, but exercise caution because the publisher and homepage are missing and the metadata contains a minor inconsistency. Before installing or providing a NEMO_TOKEN: 1) Prefer using the anonymous-token flow instead of pasting your account token unless you trust the provider; 2) Ask the publisher for a homepage, privacy policy, and terms describing how uploaded videos are stored/retained; 3) Ask why the frontmatter lists ~/.config/nemovideo/ (clarify whether the skill will read or write that directory); 4) Do not upload sensitive or private videos until you confirm data handling; 5) If you must test, use throwaway content and a throwaway account/token so exposure is limited.

Getting Started

Send me your video clips and I'll handle the AI performance ranking. Or just describe what you're after.

Try saying:

• "rank a batch of 10 short marketing video clips into a 1080p MP4"
• "rank these videos by engagement score and show me the top performers"
• "comparing and ranking videos by AI-scored performance metrics for 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 Leaderboard AI — Rank and Compare Video Performance

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

A quick example: upload a batch of 10 short marketing video clips, type "rank these videos by engagement score and show me the top performers", and you'll get a 1080p MP4 back in roughly 30-60 seconds. All rendering happens server-side.

Worth noting: upload videos of similar length for more accurate side-by-side comparisons.

Matching Input to Actions

User prompts referencing video leaderboard 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.

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.

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

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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 "rank these videos by engagement score and show me the top performers" — 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.

Common Workflows

Quick edit: Upload → "rank these videos by engagement score and show me the top performers" → 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.