Image resizing, format conversion, optimization, and OG card generation using Pillow.
Works with
Handles resize, crop, whitespace trimming, format conversion (PNG/WebP/JPG), compression, thumbnail generation, and Open Graph card creation
Generates Python scripts adapted to your environment; falls back to sips (macOS), sharp (Node.js), or ffmpeg if Pillow unavailable
Includes RGBA-to-JPG compositing, cross-platform font discovery, and format-specific quality settings (WebP 85, JPG 90, PNG optimi
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimage-processingExecute the skills CLI command in your project's root directory to begin installation:
Fetches image-processing from jezweb/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate image-processing. Access via /image-processing in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Use img-process (shipped in bin/) for common operations. For complex or custom workflows, generate a Pillow script adapted to the user's environment.
img-process resize hero.png --width 1920
img-process convert logo.png --format webp
img-process trim logo-raw.jpg -o logo-clean.png --padding 10
img-process thumbnail photo.jpg --size 200
img-process optimise hero.jpg --quality 85 --max-width 1920
img-process og-card -o og.png --title "My App" --subtitle "Built for speed"
img-process batch ./images --action convert --format webp -o ./optimised
Use img-process when: the operation is standard (resize, convert, trim, thumbnail, optimise, OG card, batch). This is faster and avoids generating a script each time.
Generate a custom script when: the operation needs logic img-process doesn't cover (compositing multiple images, watermarks, complex text layouts, conditional processing).
Pillow is required for both img-process and custom scripts:
pip install Pillow
If Pillow is unavailable, use alternatives:
| Alternative | Platform | Install | Best for |
|---|---|---|---|
sips |
macOS (built-in) | None | Resize, convert (no trim/OG) |
sharp |
Node.js | npm install sharp |
Full feature set, high performance |
ffmpeg |
Cross-platform | brew install ffmpeg |
Resize, convert |
| Use case | Format | Why |
|---|---|---|
| Photos, hero images | WebP | Best compression, wide browser support |
| Logos, icons (need transparency) | PNG | Lossless, supports alpha |
| Fallback for older browsers | JPG | Universal support |
| Thumbnails | WebP or JPG | Small file size priority |
| OG cards | PNG | Social platforms handle PNG best |
Different formats need different save parameters. Always handle RGBA-to-JPG compositing — JPG does not support transparency, so composite onto a white background first.
from PIL import Image
import os
def save_image(img, output_path, quality=None):
os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
kwargs = {}
ext = output_path.lower().rsplit(".", 1)[-1]
if ext == "webp":
kwargs = {"quality": quality or 85, "method": 6}
elif ext in ("jpg", "jpeg"):
kwargs = {"quality": quality or 90, "optimize": True}
# RGBA → RGB: composite onto white background
if img.mode == "RGBA":
bg = Image.new("RGB", img.size, (255, 255, 255))
bg.paste(img, mask=img.split()[3])
img = bg
elif ext == "png":
kwargs = {"optimize": True}
img.save(output_path, **kwargs)
When only width or height is given, calculate the other from aspect ratio. Use Image.LANCZOS for high-quality downscaling.
def resize_image(img, width=None, height=None):
if width and height:
return img.resize((width, height), Image.LANCZOS)
elif width:
ratio = width / img.width
return img.resize((width, int(img.height * ratio)), Image.LANCZOS)
elif height:
ratio = height / img.height
return img.resize((int(img.width * ratio), height), Image.LANCZOS)
return img
Remove surrounding whitespace from logos and icons. Convert to RGBA first, then use getbbox() to find content bounds.
img = Image.open(input_path)
if img.mode != "RGBA":
img = img.convert("RGBA")
bbox = img.getbbox() # Bounding box of non-zero pixels
if bbox:
img = img.crop(bbox)
Fit within max dimensions while maintaining aspect ratio:
img.thumbnail((size, size), Image.LANCZOS)
Resize + compress in one step. Convert to WebP for best compression. Typical settings: width 1920, quality 85.
System font paths differ by OS. Try multiple paths, fall back to Pillow's default. On Linux, fc-list can discover fonts dynamically.
from PIL import ImageFont
def get_font(size):
font_paths = [
# macOS
"/System/Library/Fonts/Helvetica.ttc",
"/System/Library/Fonts/SFNSText.ttf",
# Linux
"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
"/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf",
# Windows
"C:/Windows/Fonts/arial.ttf",
]
for path in font_paths:
if os.path.exists(path):
try:
return ImageFont.truetype(path, size)
except Exception:
continue
return ImageFont.load_default()
Composite text on a background image or solid colour. Apply semi-transparent overlay for text readability. Centre text horizontally.
from PIL import Image, ImageDraw, ImageFont
width, height = 1200, 630
# Background: image or solid colour
if background_path:
img = Image.open(background_path).resize((width, height), Image.LANCZOS)
else:
img = Image.new("RGB", (width, height), bg_color or "#1a1a2e")
# Semi-transparent overlay for text readability
overlay = Image.new("RGBA", (width, height), (0, 0, 0, 128))
img = img.convert("RGBA")
img = Image.alpha_composite(img, overlay)
draw = ImageDraw.Draw(img)
font_title = get_font(48)
font_sub = get_font(24)
# Centre title
if title:
bbox = draw.textbbox((0, 0), title, font=font_title)
tw = bbox[2✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.8★★★★★31 reviews- KKabir White★★★★★Dec 28, 2024
Solid pick for teams standardizing on skills: image-processing is focused, and the summary matches what you get after install.
- XXiao White★★★★★Dec 16, 2024
Registry listing for image-processing matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDev Garcia★★★★★Nov 19, 2024
We added image-processing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAlexander Park★★★★★Nov 7, 2024
Useful defaults in image-processing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- HHana Gill★★★★★Oct 26, 2024
I recommend image-processing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- JJin Robinson★★★★★Oct 10, 2024
image-processing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- SSakshi Patil★★★★★Sep 17, 2024
Registry listing for image-processing matched our evaluation — installs cleanly and behaves as described in the markdown.
- HHana Rao★★★★★Sep 17, 2024
Solid pick for teams standardizing on skills: image-processing is focused, and the summary matches what you get after install.
- HHana Flores★★★★★Sep 13, 2024
Keeps context tight: image-processing is the kind of skill you can hand to a new teammate without a long onboarding doc.
- EEmma Zhang★★★★★Sep 1, 2024
image-processing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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