image-quality-enhancement — install image-quality-enhancement image-quality-enhancement, pet-design-agent, community, install image-quality-enhancement, ide skills, image-quality-enhancement for AI agents, Real-ESRGAN image enhancement, Laplacian variance image clarity, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Visual Analysis Agents needing advanced image processing capabilities with Real-ESRGAN image-quality-enhancement is a skill that assesses and improves image clarity using Laplacian variance and Real-ESRGAN technology

Features

Evaluates image clarity using Laplacian variance
Enhances image quality with Real-ESRGAN via Replicate API
Supports customizable minimum clarity standards
Outputs enhanced images in accessible formats
Utilizes nightmareai/real-esrgan for super-resolution tasks
Integrates with projects via skills/image-quality-enhancement and scripts directories

# Core Topics

worm-13 worm-13
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The image-quality-enhancement skill by worm-13 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for install image-quality-enhancement, image-quality-enhancement for AI agents, Real-ESRGAN image enhancement.

Ideal Agent Persona

Perfect for Visual Analysis Agents needing advanced image processing capabilities with Real-ESRGAN

Core Value

Empowers agents to enhance image quality using Laplacian variance for clarity assessment and Real-ESRGAN for super-resolution, supporting image file paths or URLs with optional minimum clarity standards

Capabilities Granted for image-quality-enhancement

Enhancing low-quality images for data visualization
Automating image clarity checks for web scraping tasks
Generating high-resolution images from low-resolution inputs using Replicate API

! Prerequisites & Limits

  • Requires Replicate API access for Real-ESRGAN
  • Limited to image files or URLs
  • Dependent on minimum clarity standard for enhancement triggering
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

image-quality-enhancement

Install image-quality-enhancement, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command...

SKILL.md
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实现与脚本:本技能逻辑与脚本未改动,位于项目 skills/image-quality-enhancement/scripts/;此处为 Cursor 约定入口,便于 Agent 发现。

Image Quality Enhancement(图像清晰度提升)

检查图像清晰度,若不足则通过 Real-ESRGAN 提升图像质量并输出增强后的图像。

输入与输出

类型说明
输入图像文件路径(或 URL);可选:最低清晰度标准(默认建议 70)
输出清晰度增强后的图像文件(或可访问的 URL)

工作流程

  1. 评估清晰度:用 Laplacian 方差计算图像清晰度分数(越高越清晰)。
  2. 判断是否需增强:若清晰度分数 低于 用户给定的最低标准(如 70),则进行增强。
  3. 调用 Real-ESRGAN:通过 Replicate API 调用 nightmareai/real-esrgan 对图像做超分/增强。
  4. 保存或返回结果:将增强后的图像保存到本地或返回其 URL。

清晰度检测

使用 OpenCV 的 Laplacian 方差作为清晰度指标:

python
1import cv2 2 3def get_sharpness(image_path): 4 img = cv2.imread(image_path) 5 if img is None: 6 raise ValueError(f"无法读取图像: {image_path}") 7 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 8 return cv2.Laplacian(gray, cv2.CV_64F).var()
  • 分数越高越清晰,越低越模糊。
  • 阈值由用户指定(例如 70);低于该阈值则触发 Real-ESRGAN 增强。

调用 Real-ESRGAN(Replicate)

  • 模型nightmareai/real-esrgan(可用版本如 f121d640 等,以 Replicate 当前为准)。
  • 环境:需设置环境变量 REPLICATE_API_TOKEN
  • 输入
    • image(必填):输入图像,可为 URL 或 base64/data URL。
    • scale(可选):放大倍数,默认 4,最大 10。
    • face_enhance(可选):是否启用人脸增强,默认 False。
  • 输出:返回增强后图像的 URL(或文件流),需下载或保存为本地文件。

使用 Replicate Python 客户端示例:

python
1import replicate 2 3output = replicate.run( 4 "nightmareai/real-esrgan:f121d640bd286e1fdc67f9799164c1d5be36ff74576ee11c803ae5b665dd46aa", 5 input={"image": image_url_or_data, "scale": 4} 6) 7# output 为结果图像 URL,可用 requests 或 replicate 客户端下载并保存

若输入为本地文件,需先上传到可公网访问的 URL,或使用 Replicate 支持的 data URL/base64 等形式(以当前 API 文档为准)。

执行清单

执行时按顺序完成:

  • 读取用户提供的图像路径(或 URL)及可选的最低清晰度标准(如 70)。
  • 计算当前图像的清晰度分数(Laplacian 方差)。
  • 若分数 低于 最低标准,则调用 Real-ESRGAN 进行增强;否则可告知用户当前已达标并询问是否仍要增强。
  • 将 Real-ESRGAN 的输出保存为本地文件或返回可访问的 URL。
  • 向用户说明:原始清晰度分数、是否进行了增强、输出文件路径或 URL。

注意事项

  • 清晰度阈值(如 70)需根据实际图像类型和需求调整;Laplacian 方差受分辨率与场景影响。
  • 调用 Replicate 前请确认已配置 REPLICATE_API_TOKEN
  • 若图像来自本地路径,Replicate 通常需要可公网访问的 URL,需先通过图床或 data URL 等方式提供输入。

更多参考

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is image-quality-enhancement?

Perfect for Visual Analysis Agents needing advanced image processing capabilities with Real-ESRGAN image-quality-enhancement is a skill that assesses and improves image clarity using Laplacian variance and Real-ESRGAN technology

How do I install image-quality-enhancement?

Run the command: npx killer-skills add worm-13/pet-design-agent/image-quality-enhancement. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for image-quality-enhancement?

Key use cases include: Enhancing low-quality images for data visualization, Automating image clarity checks for web scraping tasks, Generating high-resolution images from low-resolution inputs using Replicate API.

Which IDEs are compatible with image-quality-enhancement?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for image-quality-enhancement?

Requires Replicate API access for Real-ESRGAN. Limited to image files or URLs. Dependent on minimum clarity standard for enhancement triggering.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add worm-13/pet-design-agent/image-quality-enhancement. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use image-quality-enhancement immediately in the current project.

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