Image AI Kit
v1.0.0AI图像工具包 - 智能图像处理与增强 | AI Image Kit - Intelligent image processing and enhancement
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byLv Lancer@kaiyuelv
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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high confidencePurpose & Capability
Name/description (image enhancement, OCR, smart-crop) align with included Python modules (image_enhancer, image_processor, ocr_engine, smart_crop), tests, and requirements.txt. Required binaries/env vars are none, which is consistent for a library that operates on local image files.
Instruction Scope
SKILL.md instructs only local operations (pip install -r requirements.txt, import and call library functions). The runtime code opens image files provided by the user and saves outputs; there are no instructions to read unrelated system files, environment variables, or to send data to external endpoints.
Install Mechanism
This is an instruction-only skill with a requirements.txt (pip install). There is no custom install script or remote download. Note: pytesseract requires an external Tesseract binary (system package) which SKILL.md doesn't mention; OpenCV and other libraries are pulled from PyPI (standard).
Credentials
No environment variables, credentials, or config paths are requested. The code accesses image files supplied by the user and OpenCV's local haarcascade data (cv2.data.haarcascades), which is appropriate for the stated functionality.
Persistence & Privilege
always is false and the skill does not request elevated or persistent system presence. It does not modify other skills or global agent settings; autonomous invocation is allowed by default (normal for skills) and not combined with other red flags.
Assessment
This package appears coherent and limited to local image processing. Before installing: 1) Verify you trust the repository/source (homepage is provided); run it in a virtualenv or sandbox. 2) Be aware pytesseract is a Python wrapper and requires the Tesseract binary (install via your OS package manager); SKILL.md omits that step. 3) The code operates on files you provide — avoid processing sensitive images unless you trust the environment. 4) Review requirements.txt and consider pinning versions; run the bundled tests to confirm expected behavior. 5) If you need network-isolated execution, run the package in an environment without outbound access — the code has no network calls, but isolation is good practice.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.