← Back to Skills Marketplace
x-jihua

Visual models analyze video to generate reports and highlight frames, provided by the Vidu API.

by Vidu AI · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
186
Downloads
0
Stars
1
Active Installs
1
Versions
Install in OpenClaw
/install vidu-video-analyzer
Description
Extract and analyze keyframes from MP4, MOV, AVI videos to identify themes, generate reports, and provide 3 representative screenshots.
Usage Guidance
Before installing or enabling this skill: - Confirm ffmpeg/ffprobe are installed and from a trusted package (the script depends on these but the metadata does not declare them). - Verify how Feishu integration is handled on your agent: if the skill expects to send messages via Feishu, ensure appropriate credentials/tokens are present and intentional — the skill metadata does not list any Feishu env vars. - Understand that the 'image' vision analysis will send extracted frames to whatever model/backend the agent is configured to use; do not analyze sensitive or private video content unless you trust that backend. - Review and, if desired, run the included extract_keyframes.sh in a safe test environment to confirm it behaves as expected (it appears benign: it validates input, creates an output dir, clears keyframe files in that dir, and invokes ffmpeg). - Consider asking the skill author (or the registry owner) to update metadata to list required binaries (ffmpeg/ffprobe) and to clarify any required platform credentials (Feishu) before trusting it in production.
Capability Analysis
Type: OpenClaw Skill Name: vidu-video-analyzer Version: 1.0.1 The skill bundle provides a legitimate workflow for video analysis using ffmpeg to extract keyframes and vision models for content description. The shell script 'scripts/extract_keyframes.sh' performs standard frame extraction as described, and the instructions in 'SKILL.md' are consistent with the tool's stated purpose without any evidence of malicious intent, data exfiltration, or prompt injection.
Capability Assessment
Purpose & Capability
The SKILL.md and shipped script rely on ffmpeg/ffprobe for extraction and reference a Feishu inbound path and sending output via Feishu, but the skill metadata lists no required binaries, env vars, or config paths. The use of ffmpeg/ffprobe is legitimate for video processing, and Feishu integration can be reasonable, but the metadata omission is an incoherence that could lead to runtime failures or hidden assumptions about platform integrations.
Instruction Scope
The runtime instructions stay within the stated purpose (download video, extract keyframes, analyze images, send report). They reference a specific agent filesystem path (~/.openclaw/media/inbound and ~/.openclaw/media/keyframes) and instruct sending results via Feishu. The instructions do not ask to read unrelated files or export data to unknown network endpoints, but they implicitly rely on platform-level Feishu messaging capabilities and an 'image' vision tool (which will send frames to whatever model backend the agent uses).
Install Mechanism
This is instruction-only with a small helper script — no install spec, which reduces supply-chain risk. However, the skill requires ffmpeg/ffprobe to be present on PATH; that dependency is not declared in metadata. Because extract_keyframes.sh invokes ffmpeg directly, the operator should ensure ffmpeg is installed from a trusted source.
Credentials
The skill declares no required environment variables or credentials, yet the SKILL.md mentions sending output via Feishu. If sending via Feishu requires credentials or tokens on the agent, those are not declared here. Additionally, the analysis step uses an 'image' vision tool — processing keyframes will transmit image data to the configured model backend, which may expose sensitive visual content; this risk is expected for this kind of skill but should be acknowledged and matched to declared policies/credentials.
Persistence & Privilege
The skill does not request always:true, does not modify other skills or system-wide settings, and only writes to its own output directory (~/.openclaw/media/keyframes). The script clears keyframe_*.jpg files in its output directory but does not attempt to alter other config files or credentials.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install vidu-video-analyzer
  3. After installation, invoke the skill by name or use /vidu-video-analyzer
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Improved documentation with detailed step-by-step workflow for video analysis, including keyframe extraction, vision analysis, and report generation. - Added optimization notes on token efficiency using I-frame detection. - Clarified supported video formats and user scenarios. - Included usage instructions for included scripts and Feishu workflows. - Provided token consumption estimates and practical tips for efficient analysis.
Metadata
Slug vidu-video-analyzer
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Visual models analyze video to generate reports and highlight frames, provided by the Vidu API.?

Extract and analyze keyframes from MP4, MOV, AVI videos to identify themes, generate reports, and provide 3 representative screenshots. It is an AI Agent Skill for Claude Code / OpenClaw, with 186 downloads so far.

How do I install Visual models analyze video to generate reports and highlight frames, provided by the Vidu API.?

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

Is Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. free?

Yes, Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. support?

Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Visual models analyze video to generate reports and highlight frames, provided by the Vidu API.?

It is built and maintained by Vidu AI (@x-jihua); the current version is v1.0.1.

💬 Comments