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vcarolxhberger

Video Low Vram

by vcarolxhberger · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
45
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0
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0
Active Installs
1
Versions
Install in OpenClaw
/install video-low-vram
Description
Turn a 2-minute 1080p MP4 clip into 1080p rendered MP4 videos just by typing what you need. Whether it's rendering and editing videos on low-end or memory-li...
Usage Guidance
This skill appears to do what it says (remote video processing) but has a few things to check before you install or use it: 1) Source and provenance: there is no homepage or known owner — prefer skills backed by a known project or vendor. 2) Tokens and storage: the skill will accept or generate a NEMO_TOKEN and persist session IDs; confirm where the agent stores these and that you are comfortable granting them to the nemo API. 3) Filesystem probing: the instructions ask the agent to derive a header by inspecting install paths (~/.clawhub, ~/.cursor/skills/) and reference a config path in the SKILL.md frontmatter — ask the author why filesystem access is needed and ensure the agent will ask you before reading any paths beyond the video files you explicitly upload. 4) Uploads to external API: videos will be sent to https://mega-api-prod.nemovideo.ai; do not upload sensitive material unless you trust that service. 5) Metadata mismatch: the registry shows no required config paths but the SKILL.md does — request clarification. If you proceed, test with non-sensitive sample videos first and require explicit user confirmation before any file reads or uploads.
Capability Analysis
Type: OpenClaw Skill Name: video-low-vram Version: 1.0.0 The skill is a functional integration for a cloud-based video processing service (nemovideo.ai). It provides the AI agent with specific instructions for managing authentication (via NEMO_TOKEN), session state, file uploads, and rendering tasks using a REST API and Server-Sent Events (SSE). The instructions include security best practices, such as preventing the leakage of raw tokens in chat logs, and the behavior is entirely consistent with the stated purpose of offloading video rendering to remote GPUs. No indicators of data exfiltration, malicious execution, or persistence were found.
Capability Assessment
Purpose & Capability
The declared purpose (remote low‑VRAM video processing) matches the runtime actions (create session, upload video, render, download). However the SKILL.md frontmatter includes a required config path (~/.config/nemovideo/) that is not reflected in the registry metadata, and the runtime requires constructing X-Skill-Platform by inspecting install paths (~/.clawhub, ~/.cursor/skills/). Those inconsistencies are unexpected and worth verifying with the author.
Instruction Scope
The instructions instruct the agent to: (a) generate an anonymous token via POST and use it as NEMO_TOKEN, (b) create and persist session_id, (c) upload files via multipart using local file paths (files=@/path), and (d) derive attribution headers by reading this file's YAML frontmatter and probing install paths. Probing filesystem paths to compute headers and uploading local files are normal for a video upload skill, but the document does not require explicit user confirmation before reading arbitrary local paths and does not clearly limit which files may be uploaded — this increases the risk of accidental disclosure of unrelated files.
Install Mechanism
Instruction-only skill (no install spec, no code files). This is low risk from an install perspective because nothing is being written to disk by an installer.
Credentials
The only declared credential is NEMO_TOKEN (primaryEnv), which is appropriate for a third‑party API. The SKILL.md also describes creating and storing an anonymous token if none exists. The mismatch between registry metadata (no config paths) and SKILL.md frontmatter (configPaths: ~/.config/nemovideo/) is a proportionality/consistency concern to clarify. Also the skill requires inclusion of custom headers (X-Skill-Source/Version/Platform) — non‑secret but the Platform header requires filesystem inspection.
Persistence & Privilege
always:false and normal agent invocation. The skill asks the agent to save session_id and reuse tokens, which is expected for session-based APIs. It does not request global or persistent privileges beyond its own session state.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install video-low-vram
  3. After installation, invoke the skill by name or use /video-low-vram
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release — enables efficient video processing and rendering via cloud for low-VRAM GPUs. - Upload and process 1080p MP4 videos by describing desired edits; no timeline or export settings needed. - Automatic setup with simple NEMO_TOKEN or anonymous token flow. - Cloud-based GPU rendering handles uploads, edits, and exports (MP4, MOV, AVI, WebM, and more). - Includes session management, credit checking, and live status feedback. - User prompts smartly routed to actions: upload, edit, export, balance, or state. - All communication handled via secure API with minimal local requirements.
Metadata
Slug video-low-vram
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Video Low Vram?

Turn a 2-minute 1080p MP4 clip into 1080p rendered MP4 videos just by typing what you need. Whether it's rendering and editing videos on low-end or memory-li... It is an AI Agent Skill for Claude Code / OpenClaw, with 45 downloads so far.

How do I install Video Low Vram?

Run "/install video-low-vram" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Video Low Vram free?

Yes, Video Low Vram is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Video Low Vram support?

Video Low Vram is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Video Low Vram?

It is built and maintained by vcarolxhberger (@vcarolxhberger); the current version is v1.0.0.

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