本文へ移動
cccskills
無料GitHub で公開

image-enhancer

Improve image quality by enhancing resolution, sharpness, and clarity. Perfect for screenshots, documentation, presentations, and social media images.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md8.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Image Enhancer

Use This Skill When

  • Improving screenshot quality for blog posts or documentation
  • Sharpening blurry or low-resolution images
  • Upscaling images to higher resolution
  • Preparing images for presentations or reports
  • Reducing compression artifacts from JPEG images
  • Cleaning up images before social media posting
  • Batch processing multiple images
  • Preparing visual content for printing

When NOT to Use

  • Editing content or composition (different skill)
  • Adding text or watermarks
  • Professional photo retouching or color grading
  • Creating new graphics from scratch
  • Legal/forensic image analysis
  • Images with privacy/sensitive information

Context: Image Quality Enhancement Maturity

Undeveloped: Use images as-is; blurry screenshots posted directly.

Target: Enhance resolution, sharpen details, reduce noise; optimize for use case.

Optimized: Automatic quality detection, ML-based upscaling, batch processing, use-case optimization (web vs. print vs. social).

Core Principle

Image quality reflects professionalism. Poor screenshots, blurry photos, or compressed artifacts undermine content. Enhancement improves clarity, readability, and visual impact with minimal manual effort.

Instructions

Step 1: Analyze Image Quality

Assess current image state:

## Image Analysis: screenshot.png

**Current Specs**:
- Resolution: 1920x1080 (native)
- Format: PNG
- File Size: 3.2 MB
- Color Depth: 24-bit
- Compression: Moderate

**Quality Assessment**:
- ✓ Good resolution for web
- ⚠️ Slight blur in text areas
- ⚠️ Some compression artifacts in gradients
- ✓ Colors well-saturated

**Recommendations**:
1. Sharpen text edges
2. Reduce gradient banding artifacts
3. Upscale to 2560x1440 for retina displays
4. Optimize file size for web delivery

Step 2: Choose Enhancement Strategy

Select based on use case:

Use CaseStrategyOutput ResolutionFormat
Web displaySharpen + optimize1920-2560PNG/WebP
Retina displayUpscale + sharpen2x native (2560x1440)PNG
Print (300dpi)Upscale + detail enhanceHigh DPIPNG/TIFF
Social mediaOptimize + compress1200-1600JPG (optimized)
DocumentationSharpen + artifact reduceNative + 20%PNG

Step 3: Apply Resolution Enhancement

Upscale intelligently:

# Option 1: 1.5x upscaling (minimal quality loss)
upscale --input original.png --output upscaled.png --factor 1.5

# Option 2: 2x upscaling (retina)
upscale --input original.png --output upscaled.png --factor 2

# Using AI/ML upscaling (preserves detail)
# Better than bicubic interpolation

Output comparison:

## Resolution Enhancement

**Original**: 1920x1080 (1 MB)
**After 1.5x Upscale**: 2880x1620 (2.1 MB)
**After 2x Upscale**: 3840x2160 (4K, 4.2 MB)

**Recommendation**: 1.5x for web, 2x for retina/print

Step 4: Enhance Sharpness and Details

Sharpen edges without over-processing:

# Apply unsharp mask (standard sharpening)
sharpen --input upscaled.png --output sharpened.png \
  --radius 0.5 --amount 1.0 --threshold 0

# Adaptive sharpening (preserves smooth areas)
sharpen --input upscaled.png --output sharpened.png \
  --method adaptive --strength medium

Before/after:

## Sharpness Enhancement

**Before**: Blurry text, soft edges
**After Sharpening**: Crisp text, defined boundaries

**Settings Applied**:
- Unsharp mask radius: 0.5
- Strength: 1.0x
- Preservation: Smooth gradients protected

**Result**: Text highly readable, no over-sharpening artifacts

Step 5: Reduce Compression Artifacts

Clean up JPEG compression artifacts:

# Reduce banding and block artifacts
denoise --input original.jpg --output denoised.jpg \
  --method nlm --strength 0.7  # Non-local means
  
# Alternative: bilateral filter (preserves edges)
bilateral --input original.jpg --output cleaned.jpg \
  --sigma-spatial 2 --sigma-intensity 20

Results:

## Artifact Reduction

**Common Artifacts Found**:
- JPEG blocking (8x8 block boundaries)
- Color banding in gradients
- Chroma subsampling halos

**After Denoising**:
✓ Blocks smoothed
✓ Banding reduced
✓ Colors blended naturally
✗ Minimal detail loss

Step 6: Optimize for Delivery

Optimize file size and format:

For Web:

# PNG for lossless (screenshots, text)
convert input.png -strip -interlace Plane \
  -quality 95 output.png

# WebP for better compression
cwebp -q 80 input.png -o output.webp

For Print:

# TIFF with 300 DPI
convert input.png -density 300x300 -format tiff output.tif

For Social Media:

# Optimize JPG for fast loading
jpegoptim --max=85 --strip-all input.jpg
# Result: 1200-1600px width, <200KB

Delivery optimization:

## Format Comparison

| Format | Size | Quality | Use |
|--------|------|---------|-----|
| PNG (native) | 3.2 MB | Lossless | Web, documentation |
| PNG (optimized) | 1.8 MB | Lossless | Balance of size/quality |
| WebP | 1.2 MB | 80% quality | Modern web (95% browser support) |
| JPG (80%) | 0.9 MB | 80% quality | Social media |
| TIFF (300dpi) | 8.1 MB | Lossless | Print |

**Recommended**: WebP for web (best compression)
**Fallback**: PNG for universal compatibility

Step 7: Batch Process Multiple Images

Enhance entire folder:

#!/bin/bash
# enhance-images.sh

INPUT_DIR="${1:-.}"
OUTPUT_DIR="${INPUT_DIR}/enhanced"
SCALE_FACTOR="${2:-1.5}"

mkdir -p "$OUTPUT_DIR"

for img in "$INPUT_DIR"/*.{png,jpg,jpeg}; do
  if [ -f "$img" ]; then
    filename=$(basename "$img")
    echo "Processing $filename..."
    
    # Upscale + sharpen + optimize
    convert "$img" \
      -scale $(($(identify -format %w "$img") * SCALE_FACTOR / 100))x \
      \( +clone -blur 0x0.5 \) \
      -compose Difference -composite \
      +compose \
      -compose Multiply -composite \
      "$OUTPUT_DIR/enhanced-$filename"
      
    echo "✓ Saved to $OUTPUT_DIR/enhanced-$filename"
  fi
done

Run:

chmod +x enhance-images.sh
./enhance-images.sh ~/screenshots 150  # 1.5x upscale

Step 8: Quality Assurance

Verify enhancements:

## QA Checklist

**Visual Quality**:
- [ ] Text is crisp and readable
- [ ] No excessive sharpening artifacts
- [ ] Colors accurate (not oversaturated)
- [ ] Smooth gradients (no banding)
- [ ] Edges clean (no halos)

**File Size**:
- [ ] Web images <2MB
- [ ] Social media <300KB
- [ ] Print-ready at 300 DPI

**Format Appropriateness**:
- [ ] PNG for graphics/screenshots
- [ ] JPG for photographs
- [ ] WebP for modern web
- [ ] TIFF for print

**Before/After**:
- [ ] Original preserved with -original suffix
- [ ] Enhanced version shows clear improvement
- [ ] No significant quality loss compared to original

Common Enhancements

IssueSolution
Blurry textSharpen with unsharp mask, increase radius
Low resolutionUpscale 1.5-2x with AI/ML method
Compression artifactsDenoise with NLM or bilateral filter
Color bandingApply noise dithering or gradient smoothing
Large file sizeConvert to WebP, reduce DPI to 72 for web
Pixelated edgesGentle blur + sharpen combination

Output

Deliver:

  1. Quality Analysis (current state assessment)
  2. Enhancement Strategy (recommended approach for use case)
  3. Before/After Comparison (visual improvement documented)
  4. Optimized Files
    • Enhanced version with clear naming
    • Original preserved with -original suffix
    • File size reduction percentage
  5. Format Recommendations (PNG/JPG/WebP based on context)
  6. Batch Processing Results (if multiple images)

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

Automate Ably tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

Automate Abuselpdb tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

Automate Abyssale tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

manu14357/zskills162026年7月24日 更新

manu14357 のスキルをすべて見る

このスキルの問題を報告する