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视频尺寸调整
by
fischerlam
· GitHub ↗
· v1.0.12
· MIT-0
316
Downloads
1
Stars
0
Active Installs
5
Versions
Install in OpenClaw
/install video-resizer-zh
Description
面向比例转换和平台适配场景的 Sparki skill 变体，沿用最新版官方 Sparki 安装、API key、上传和命令说明，同时保留 resizer 场景定位。
Usage Guidance
This skill appears to be a genuine Sparki CLI wrapper for video resizing and asks only for the Sparki API key and access to its own config/workspace. Before installing, verify the following: (1) the origin and trustworthiness of the 'uv' binary and what 'uv sync' does — the SKILL.md requests running it but its purpose is not documented here; (2) whether you need to provide SPARKI_UPLOAD_TG_LINK as an environment variable (the code reads it though metadata doesn't declare it); (3) that you are comfortable giving the skill write access to ~/.openclaw and the workspace where videos will be stored; (4) confirm the API key scope/limits on the Sparki side and that network access to agent-api.sparki.io is acceptable. If you cannot confirm the source/behavior of 'uv sync', prefer installing the Python package directly (inspect pyproject.toml & source) or ask the publisher for clarification before running any install commands that fetch or execute external code.
Capability Analysis
Type: OpenClaw Skill
Name: video-resizer-zh
Version: 1.0.12
The video-resizer-zh skill is a legitimate CLI wrapper for the Sparki AI video editing service. The code (cli.py, client.py) implements standard API interactions for uploading videos, managing editing tasks, and downloading results from agent-api.sparki.io. The instructions in SKILL.md are functional directives to ensure the AI agent uses the provided tool instead of local utilities like ffmpeg, and the requested file system and network permissions are consistent with the tool's operation.
Capability Assessment
Purpose & Capability
Name/description, included CLI code, HTTP client, and the declared primary credential (SPARKI_API_KEY) align with a Sparki video-resizing/uploading integration that talks to agent-api.sparki.io. File read/write permissions target the skill's config and workspace, which is expected for a CLI that stores keys, history, and output files.
Instruction Scope
Runtime instructions and CLI commands operate on local video files, upload to the Sparki API, create projects, poll status, and download results — all coherent with the stated purpose. The code reads/writes a local history and config (~/.openclaw) and will use an environment override for SPARKI_API_KEY. One minor mismatch: the code reads SPARKI_UPLOAD_TG_LINK from the environment for the Telegram upload link, but the skill metadata did not declare that env var.
Install Mechanism
Registry metadata indicated 'no install spec', but SKILL.md contains an install entry that runs 'uv sync' and the skill declares the required binary 'uv'. The package also contains a full Python CLI (pyproject.toml) with a sparki console script — so requiring 'uv' to 'sync' is unusual and possibly unnecessary. 'uv sync' could perform network operations or mutate the filesystem; the origin and behavior of the 'uv' binary is not explained here, which is a supply-chain/installation risk.
Credentials
Requesting SPARKI_API_KEY as the primary credential is proportionate to a Sparki client. No other secrets are required. However, the code also checks SPARKI_UPLOAD_TG_LINK in the environment (not declared in requires.env), which is a small inconsistency. There are no unrelated credentials requested (no AWS/GitHub/etc.).
Persistence & Privilege
always:false and normal autonomous invocation are appropriate. The declared filesystem and network permissions are limited to the skill's own config/workspace and the Sparki API domain (agent-api.sparki.io). The skill writes only to its own config and history files; it does not request system-wide or other-skills' settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install video-resizer-zh - After installation, invoke the skill by name or use
/video-resizer-zh - Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Polished the Chinese copy so this scene skill reads more like a native Chinese product page instead of a structural translation, while keeping the official shared Sparki core workflow.
v1.0.10
Refreshed this Chinese 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 Chinese scene skill so its shared setup, API-key, upload, and command guidance now matches the latest official sparki-video-editor skill while preserving scene-specific positioning.
v1.0.8
Published a Chinese-localized scene skill for resizing videos to platform formats, aligned to the official Sparki API domain.
Metadata
Frequently Asked Questions
What is 视频尺寸调整?
面向比例转换和平台适配场景的 Sparki skill 变体，沿用最新版官方 Sparki 安装、API key、上传和命令说明，同时保留 resizer 场景定位。 It is an AI Agent Skill for Claude Code / OpenClaw, with 316 downloads so far.
How do I install 视频尺寸调整?
Run "/install video-resizer-zh" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 视频尺寸调整 free?
Yes, 视频尺寸调整 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 视频尺寸调整 support?
视频尺寸调整 is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux).
Who created 视频尺寸调整?
It is built and maintained by fischerlam (@fischerlam); the current version is v1.0.12.
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