image-filter-lab

Q: How do I install image-filter-lab?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill image-filter-lab in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/image-filter-lab/ (personal) or .claude/skills/image-filter-lab/ (project).

by dkyazzentwatwaMedia & Creative

Apply artistic filters to images including vintage, sepia, B&W, blur, sharpen, vignette, and color adjustments. Create custom filter presets.

Installs: 69
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill image-filter-lab

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads image-filter-lab automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Image Filter Lab

Apply professional filters and effects to images.

Features

Color Filters: Sepia, B&W, color tint, saturation
Blur Effects: Gaussian, motion, radial blur
Artistic Filters: Vintage, film grain, vignette
Enhancements: Sharpen, contrast, brightness
Custom Presets: Save and apply filter combinations
Batch Processing: Apply filters to multiple images

Quick Start

from image_filter import ImageFilterLab

lab = ImageFilterLab()
lab.load("photo.jpg")

# Apply vintage filter
lab.vintage()
lab.save("photo_vintage.jpg")

# Chain multiple effects
lab.load("photo.jpg")
lab.brightness(1.2).contrast(1.1).saturation(0.8).vignette()
lab.save("photo_edited.jpg")

CLI Usage

# Apply single filter
python image_filter.py --input photo.jpg --filter vintage --output result.jpg

# Apply multiple filters
python image_filter.py -i photo.jpg --sepia --vignette --sharpen -o result.jpg

# Adjust parameters
python image_filter.py -i photo.jpg --brightness 1.2 --contrast 1.1 -o result.jpg

# Batch process
python image_filter.py --batch photos/ --filter vintage --output-dir filtered/

API Reference

ImageFilterLab Class

class ImageFilterLab:
    def __init__(self)

    # Loading
    def load(self, filepath: str) -> 'ImageFilterLab'

    # Color Filters
    def grayscale(self) -> 'ImageFilterLab'
    def sepia(self, intensity: float = 1.0) -> 'ImageFilterLab'
    def negative(self) -> 'ImageFilterLab'
    def tint(self, color: Tuple, intensity: float = 0.3) -> 'ImageFilterLab'

    # Adjustments
    def brightness(self, factor: float) -> 'ImageFilterLab'
    def contrast(self, factor: float) -> 'ImageFilterLab'
    def saturation(self, factor: float) -> 'ImageFilterLab'
    def hue(self, shift: int) -> 'ImageFilterLab'
    def temperature(self, value: int) -> 'ImageFilterLab'

    # Blur Effects
    def blur(self, radius: int = 5) -> 'ImageFilterLab'
    def motion_blur(self, size: int = 15, angle: int = 0) -> 'ImageFilterLab'
    def radial_blur(self, amount: int = 10) -> 'ImageFilterLab'

    # Sharpen
    def sharpen(self, factor: float = 1.0) -> 'ImageFilterLab'
    def unsharp_mask(self, radius: int = 2, percent: int = 150) -> 'ImageFilterLab'

    # Artistic
    def vintage(self) -> 'ImageFilterLab'
    def film_grain(self, amount: int = 25) -> 'ImageFilterLab'
    def vignette(self, radius: float = 0.8, intensity: float = 0.5) -> 'ImageFilterLab'
    def posterize(self, levels: int = 4) -> 'ImageFilterLab'
    def solarize(self, threshold: int = 128) -> 'ImageFilterLab'

    # Presets
    def apply_preset(self, preset: str) -> 'ImageFilterLab'
    def save_preset(self, name: str, operations: List) -> None

    # Output
    def save(self, filepath: str, quality: int = 95) -> str
    def reset(self) -> 'ImageFilterLab'

    # Batch
    def batch_process(self, input_dir: str, output_dir: str,
                     filter_func: callable) -> List[str]

Built-in Presets

vintage: Sepia tint, reduced saturation, vignette
film: Slight desaturation, grain, crushed blacks
instagram: High contrast, warm tint, vignette
noir: High contrast B&W, strong vignette
warm: Warm color temperature, increased saturation
cool: Cool color temperature, slightly desaturated
dramatic: High contrast, deep shadows
dreamy: Soft blur, bright highlights, low contrast

Dependencies

pillow>=10.0.0
opencv-python>=4.8.0
numpy>=1.24.0

FAQ

What does the image-filter-lab skill do?

Apply artistic filters to images including vintage, sepia, B&W, blur, sharpen, vignette, and color adjustments. Create custom filter presets.

How do I install image-filter-lab?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill image-filter-lab in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/image-filter-lab/ (personal) or .claude/skills/image-filter-lab/ (project).

Which AI agents can use image-filter-lab?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is image-filter-lab safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains image-filter-lab?

It is published by dkyazzentwatwa in dkyazzentwatwa/chatgpt-skills. It has 69 recorded installs.

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image-filter-lab

Install

Install

Describe the task

Review the result

SKILL.md

Image Filter Lab

Features

Quick Start

CLI Usage

API Reference

ImageFilterLab Class

Built-in Presets

Dependencies

FAQ

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