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

作者 Vidu AI · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
186
总下载
0
收藏
1
当前安装
1
版本数
在 OpenClaw 中安装
/install vidu-video-analyzer
功能描述
Extract and analyze keyframes from MP4, MOV, AVI videos to identify themes, generate reports, and provide 3 representative screenshots.
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install vidu-video-analyzer
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /vidu-video-analyzer 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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.
元数据
Slug vidu-video-analyzer
版本 1.0.1
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

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

Extract and analyze keyframes from MP4, MOV, AVI videos to identify themes, generate reports, and provide 3 representative screenshots. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 186 次。

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

在 OpenClaw 或 Claude Code 对话框中运行命令「/install vidu-video-analyzer」即可一键安装,无需额外配置。

Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. 是免费的吗?

是的,Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. 支持哪些平台?

Visual models analyze video to generate reports and highlight frames, provided by the Vidu API. 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

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

由 Vidu AI(@x-jihua)开发并维护,当前版本 v1.0.1。

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