display-ads▌
kostja94/marketing-skills · updated Apr 8, 2026
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Guides display advertising: ad networks, banner ads, and programmatic buying. Use when placing ads on publisher sites (websites, apps) for brand awareness or retargeting.
Paid Ads: Display / Banner
Guides display advertising: ad networks, banner ads, and programmatic buying. Use when placing ads on publisher sites (websites, apps) for brand awareness or retargeting.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
What Is Display / Banner
- Ad networks: Aggregate inventory from many publishers; buy placements programmatically or via direct deals
- Banner ads: IAB standard sizes (300×250, 728×90, 160×600, 320×50 mobile); static or animated
- Programmatic: Automated buying via DSPs; real-time bidding (RTB); audience targeting
Formats
| Format | Use |
|---|---|
| Display banner | IAB sizes; CPM or CPC; brand, retargeting |
| Native | Blends with page content; higher engagement |
| Video pre-roll | Pre-roll on publisher video; see ctv-ads for streaming |
| Rich media | HTML5; expandable, interactive |
| Mobile interstitial | Full-screen between content |
Buying Options
| Option | Use |
|---|---|
| Google Display | Part of Google Ads; automated placements; retargeting |
| Programmatic DSP | The Trade Desk, Magnite, etc.; audience-based; scale |
| Direct publisher | Deal with specific site; guaranteed placement |
| Ad network | Network aggregates inventory; simpler than full programmatic |
Metrics
| Metric | Use |
|---|---|
| CPM | Cost per thousand impressions |
| CPC | Cost per click |
| CTR | Click-through rate; typically low for banners (0.1–0.5%) |
| Viewability | % of impressions actually seen |
| Completion rate | For video; % who watch full ad |
Creative
- IAB sizes: 300×250 (medium rectangle), 728×90 (leaderboard), 160×600 (skyscraper), 320×50 (mobile)
- File types: Static image, animated GIF, HTML5
- Message: Clear CTA; minimal text; brand visible in 3 seconds
UTM
Use utm_medium=display or cpc with utm_source (publisher or network name) for attribution. See traffic-analysis for GA4 alignment.
Pre-Launch Checklist
- Creative in required sizes
- Landing page aligned with ad message
- UTM parameters set
- Retargeting audience defined (if applicable)
- Viewability target set
Related Skills
- paid-ads-strategy: Ad formats by medium; when to use display
- google-ads: Google Display Network; retargeting campaigns
- traffic-analysis: UTM for display; attribution
- analytics-tracking: Conversion tracking; viewability
How to use display-ads on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add display-ads
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches display-ads from GitHub repository kostja94/marketing-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate display-ads. Access the skill through slash commands (e.g., /display-ads) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★65 reviews- ★★★★★Chaitanya Patil· Dec 28, 2024
Solid pick for teams standardizing on skills: display-ads is focused, and the summary matches what you get after install.
- ★★★★★Liam Perez· Dec 28, 2024
display-ads has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Nikhil Abebe· Dec 24, 2024
I recommend display-ads for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sofia Mensah· Dec 16, 2024
Solid pick for teams standardizing on skills: display-ads is focused, and the summary matches what you get after install.
- ★★★★★Mateo Srinivasan· Dec 12, 2024
display-ads has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Tandon· Dec 12, 2024
Keeps context tight: display-ads is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Camila Sanchez· Dec 8, 2024
Useful defaults in display-ads — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mateo Harris· Dec 4, 2024
display-ads fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Min Li· Nov 23, 2024
Useful defaults in display-ads — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Jin Yang· Nov 23, 2024
display-ads has been reliable in day-to-day use. Documentation quality is above average for community skills.
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