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Video Resizer
作者
fischerlam
· GitHub ↗
· v1.0.12
· MIT-0
311
总下载
0
收藏
0
当前安装
7
版本数
在 OpenClaw 中安装
/install video-resizer
功能描述
Scenario-focused Sparki skill for aspect-ratio and platform-format conversion while using the latest official Sparki setup, API-key, and upload workflow guid...
安全使用建议
This package appears to be a straightforward Sparki video-reformatting CLI and calls only the Sparki backend by default, but there are a few things to check before installing or providing your API key:
- Do not provide a high-privilege or unrelated credential; this skill only needs SPARKI_API_KEY (and optionally SPARKI_UPLOAD_TG_LINK). The key will be saved to ~/.openclaw/config/sparki.json in plain JSON — consider whether you want the key stored there.
- The SKILL.md declares a required binary 'uv' and an install step 'uv sync' even though the Python code does not reference 'uv'. Ask the publisher what 'uv' is, why it's required, and where it would be installed from before running that command.
- The registry metadata said “no install spec” while SKILL.md includes an install block — confirm which install process the platform will actually run. Avoid running arbitrary 'uv sync' or other unknown installers until you verify their source.
- The skill will read files from the current working directory and write outputs under ~/.openclaw/workspace/sparki/videos — ensure you trust that behavior and that no sensitive files are left in the working directory.
- If you plan to use the Telegram upload link flow, verify the configured upload link/base_url is the official Sparki endpoint (DEFAULT_BASE_URL is agent-api.sparki.io and DEFAULT_UPLOAD_TG_LINK points to t.me/Sparki_AI_bot) and not overridden to an unexpected host.
If you want to proceed: (1) review or run the included Python code in an isolated environment, (2) confirm the origin of the 'uv' tool before executing it, and (3) prefer providing the SPARKI_API_KEY via environment variable rather than interactively storing it if you want to avoid persisting it to disk.
功能分析
Type: OpenClaw Skill
Name: video-resizer
Version: 1.0.12
The video-resizer skill is a legitimate CLI tool for interacting with the Sparki AI video editing API. The code (src/sparki_cli/cli.py, client.py) implements standard API workflows for uploading, editing, and downloading video files, with permissions correctly restricted to its own configuration and workspace directories and the official API domain (agent-api.sparki.io). No evidence of data exfiltration, malicious execution, or harmful prompt injection was found.
能力评估
Purpose & Capability
The name, description, and code files implement a Sparki video upload/edit/download CLI (upload, edit, status, download) that aligns with the stated purpose of aspect-ratio and platform reformatting. The only oddity: the registry metadata / SKILL.md declare a required binary 'uv' and an install step ('uv sync') which are not referenced by the Python CLI code — this is disproportionate to what the CLI actually needs.
Instruction Scope
SKILL.md instructs the agent to proactively use the skill when users mention video editing and enforces upload procedures (local path or Telegram mini-app link). The CLI code only reads files from the working directory and writes to its own config/workspace — no unexpected file reads or hidden network endpoints are present. However SKILL.md contains an install section and 'requires' metadata even though the registry shows no install spec, which is an internal inconsistency.
Install Mechanism
Registry lists no install spec, but SKILL.md includes an install subsection requiring the 'uv' binary and running 'uv sync' (extract/install behavior unspecified). The Python code itself requires Python dependencies (typer, httpx, pydantic) and exposes a sparki CLI entrypoint, but there is no clear, documented, standard install source for 'uv' or for installing the Python package in the skill metadata. Requiring an unknown external binary to 'sync' code is disproportionate and worth auditing before running.
Credentials
Primary credential SPARKI_API_KEY is appropriate for a Sparki client and the code reads that env var as intended. The code also supports SPARKI_UPLOAD_TG_LINK (used for Telegram upload shortcut) and writes API key to ~/.openclaw/config/sparki.json, but SKILL.md metadata declared no required envs — a minor mismatch. There are no requests for unrelated credentials or wide-ranging secrets.
Persistence & Privilege
The skill requests normal, limited persistence (writing its own config at ~/.openclaw/config and saving workspace outputs under ~/.openclaw/workspace/sparki/videos). always:false and autonomous invocation defaults are present; there is no evidence it attempts to modify other skills or system-wide configs beyond its own files.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install video-resizer - 安装完成后,直接呼叫该 Skill 的名称或使用
/video-resizer触发 - 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
v1.0.12
Improved engagement-oriented positioning with a stronger result-focused summary, one-copy quick start command, prompt templates, and related-skill cross-links while keeping the official shared Sparki core workflow.
v1.0.11
Tightened the opening trigger and example requests so this scene skill is more vertical and better aligned to user intent, while keeping the official shared Sparki core workflow.
v1.0.10
Refreshed this scene skill to align its shared setup, API-key, upload, and command guidance with the latest official sparki-video-editor skill while preserving its scenario-specific positioning.
v1.0.9
Refreshed this scene skill to align its shared setup, API-key, upload, and command guidance with the latest official sparki-video-editor skill while preserving scene-specific positioning.
v1.0.8
Updated the default API endpoint to the official Sparki domain https://business-agent-api.sparki.io and aligned docs/scripts accordingly.
v1.0.7
Re-released as a cleaned English-only update. Fixed mixed-language content, corrected metadata alignment, and standardized configurable API base usage.
v1.0.6
Published a scenario-specific skill focused on resizing videos for platform formats, built on the cleaned Sparki video workflow.
元数据
常见问题
Video Resizer 是什么?
Scenario-focused Sparki skill for aspect-ratio and platform-format conversion while using the latest official Sparki setup, API-key, and upload workflow guid... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 311 次。
如何安装 Video Resizer?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install video-resizer」即可一键安装,无需额外配置。
Video Resizer 是免费的吗?
是的,Video Resizer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Video Resizer 支持哪些平台?
Video Resizer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux)。
谁开发了 Video Resizer?
由 fischerlam(@fischerlam)开发并维护,当前版本 v1.0.12。
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