Confirm successful installation by checking the skill directory location:
.cursor/skills/image-processing
Restart Cursor to activate image-processing. Access via /image-processing in your agent's command palette.
β
Security Notice
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.
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).
Prerequisites
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
Output Format Guide
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
Core Patterns
Save with Format-Specific Quality
Different formats need different save parameters. Always handle RGBA-to-JPG compositing β JPG does not support transparency, so composite onto a white background first.
βΊAccess to product documentation and roadmap tools (Jira, Notion, etc.)
βΊUnderstanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
βΊStakeholder 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
β Not validating competitive researchβverify facts before sharing
β Accepting user stories without involving engineering team
β Over-relying on frameworks without qualitative judgment
β Not customizing outputs to company culture and communication style
β Skipping stakeholder validation of generated requirements
Best Practices
β 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
β Don't
βDon't publish competitive analysis without fact-checking
βDon't finalize user stories without engineering review
βDon't make prioritization decisions solely on AI scoring
βDon't skip customer validation of generated requirements
βDon't ignore company-specific context and culture
π‘ Pro Tips
β Provide context: company goals, constraints, customer feedback
β Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
β Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
β Use skill for 70% generation + 30% customization to company needs
When to Use This
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β 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