mapbox-search-integration▌
mapbox/mapbox-agent-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Expert guidance for implementing Mapbox search functionality in applications. Covers the complete workflow from asking the right discovery questions, selecting the appropriate search product, to implementing production-ready integrations following best practices from the Mapbox search team.
Mapbox Search Integration Skill
Expert guidance for implementing Mapbox search functionality in applications. Covers the complete workflow from asking the right discovery questions, selecting the appropriate search product, to implementing production-ready integrations following best practices from the Mapbox search team.
Use This Skill When
User says things like:
- "I need to add search to my map"
- "I need a search bar for my mapping app"
- "How do I implement location search?"
- "I want users to search for places/addresses"
- "I need geocoding in my application"
This skill complements mapbox-search-patterns:
mapbox-search-patterns= Tool and parameter selectionmapbox-search-integration= Complete implementation workflow
Discovery Phase: Ask the Right Questions
Before jumping into code, ask these questions to understand requirements:
Question 1: What are users searching for?
Ask: "What do you want users to search for?"
Common answers and implications:
- "Addresses" → Use Search Box API (the default for interactive address search, including geocoding). Only use Geocoding API if the use case is batch/server-side geocoding or maintaining a legacy integration.
- "Points of interest / businesses" → POI search, use Search Box API with category search
- "Both addresses and POIs" → Search Box API
- "Specific types of POIs" (restaurants, hotels, etc.) → Search Box API
- "Countries, cities, postcodes or neighborhoods" → Search Box API for interactive search; Geocoding API only for batch/server-side geocoding
- "Custom locations" (user-created places) → May need custom data + search integration
Follow-up if not stated initially: "Are your users searching for points of interest data? Restaurants, stores, categories of businesses?"
Implications:
- "Yes, POIs are included" → Use the Search Box API
- "No, the user does not need POI search" → Still default to Search Box API for interactive/autocomplete use cases. Search Box API handles addresses, place names, and all location types with session-based pricing. Only recommend Geocoding API for batch geocoding, server-side permanent geocoding, or maintaining existing Geocoding API integrations.
Question 2: What's the geographic scope?
Ask: "Where will users be searching?"
Common answers and implications:
- "Single country" (e.g., "only USA") → Use
countryparameter, better results, lower cost - "Specific region" → Use
bboxparameter for bounding box constraint - "Global" → No country restriction, but may need language parameter
- "Multiple specific countries" → Use
countryarray parameter
Follow-up: "Do you need to limit results to a specific area?" (delivery zone, service area, etc.)
Question 3: What's the search interaction pattern?
Ask: "How will users interact with search?"
Common answers and implications:
- "Search-as-you-type / autocomplete" → Use Search Box API with
auto_complete: trueand session-based pricing (most cost-efficient for autocomplete). Implement debouncing. - "Search button / final query" → Can use either API, no autocomplete needed
- "Both" (autocomplete + refine) → Two-stage search, autocomplete then detailed results
- "Voice input" → Consider speech-to-text integration, handle longer queries
Question 4: What platform?
Ask: "What platform is this for?"
Common answers and implications:
- "Web application" → Mapbox Search JS (easiest), or direct API calls for advanced cases
- "iOS app" → Search SDK for iOS (recommended), or direct API integration for advanced cases
- "Android app" → Search SDK for Android (recommended), or direct API integration for advanced cases
- "Multiple platforms" → Platform-specific SDKs (recommended), or direct API approach for consistency
- "React app" → Mapbox Search JS React (easiest with UI), or Search JS Core for custom UI. Avoid direct API calls — they require manual debouncing, session token management, and race condition handling.
- "Vue / Angular / Other framework" → Mapbox Search JS Core or Web. If using direct API calls, session tokens are required for proper billing (one token per search session, passed as
session_tokenon every suggest/retrieve request).
Question 5: How will results be used?
Ask: "What happens when a user selects a result?"
Common answers and implications:
- "Fly to location on map" → Need coordinates, map integration
- "Show details / info" → Need to retrieve and display result properties
- "Fill form fields" → Need to parse address components
- "Start navigation" → Need coordinates, integrate with directions
- "Multiple selection" → Need to handle selection state, possibly show markers
Question 6: Expected usage volume?
Ask: "How many searches do you expect per month?"
Implications:
- Low volume (< 10k) → Free tier sufficient, simple implementation
- Medium volume (10k-100k) → Consider caching, optimize API calls
- High volume (> 100k) → Implement debouncing, caching, batch operations, monitor costs
Product Selection Decision Tree
Based on discovery answers, recommend the right product:
Key principle: Search Box API is the default choice for virtually all interactive search use cases, including address search, geocoding, autocomplete, and POI search. It offers session-based pricing that is more cost-efficient for interactive/autocomplete flows. Only recommend Geocoding API for the narrow cases listed below.
Search Box API (DEFAULT)
Use when (any of these):
- User needs interactive address search or autocomplete (this IS geocoding — Search Box API handles it)
- User needs POI / category search
- User needs any end-user-facing search UI
- User wants session-based pricing (more cost-efficient for autocomplete/interactive use)
- User is building a web, iOS, or Android app with a search bar
Prefer SDKs over direct API calls for web integration:
- Mapbox Search JS (SDK) - Recommended for web integration, with three components:
- Search JS React - Easy search integration via React library with UI
- Search JS Web - Easy search integration via Web Components with UI
- Search JS Core - JavaScript (node or web) wrapper for API, build your own UI
- Search Box API (REST) - Direct API integration, for advanced/custom cases
- Search SDK for iOS - Native iOS integration
- Search SDK for Android - Native Android integration
Geocoding API (SPECIALIZED)
Use ONLY when:
- Batch geocoding large lists of addresses (server-side)
- Permanent/stored geocoding results (server-side, where results are persisted)
- Maintaining an existing Geocoding API integration (migration not justified)
- No interactive/user-facing search needed
Do NOT recommend Geocoding API when:
- The user wants a search bar, autocomplete, or interactive address lookup — use Search Box API instead
- The user says "geocoding" but describes an interactive search flow — use Search Box API instead
Reference Files
Load the relevant reference based on the user's platform and needs:
-
Web (Search JS React / Web / Core / Direct API) → Load
references/web-search-js.md- When: User is building a web app (vanilla JS, any framework except React-specific patterns)
-
React Integration → Load
references/react-search.md- When: User is building a React app specifically
-
iOS → Load
references/ios-search.md- When: User is building an iOS app (Swift/UIKit/SwiftUI)
-
Android → Load
references/android-search.md- When: User is building an Android app (Kotlin/Java)
-
Node.js → Load
references/nodejs-search.md- When: User needs server-side search (Express, serverless, backend API)
-
Best Practices → Load
references/best-practices.md- When: Implementing search for the first time, or optimizing an existing implementation
- Covers: debouncing, session tokens, geographic filtering, error handling, accessibility, caching, token security
-
Common Pitfalls → Load
references/pitfalls.md- When: Debugging issues, reviewing code, or during code review
- Covers: no debouncing, missing session tokens, no geo context, poor mobile UX, race conditions
-
Framework Hooks → Load
references/framework-hooks.md- When: Building custom hooks (React) or composables (Vue) around Search JS Core
-
Testing and Monitoring → Load
references/testing-monitoring.md- When: Writing tests or setting up production monitoring/analytics
Checklist: Production-Ready Search
Before launching, verify:
Configuration:
- Token properly scoped (search:read only)
- URL restrictions configured
- Geographic filtering set (country, proximity, or bbox)
- Types parameter set based on use case
- Language parameter set if needed
Implementation:
- Debouncing implemented (300ms recommended)
- Session tokens used correctly
- Error handling for all failure cases
- Loading states shown
- Empty results handled gracefully
- Race conditions prevented
UX:
- Touch targets at least 44pt/48dp
- Results show enough context (name + address)
- Keyboard navigation works
- Accessibility attributes set
- Mobile keyboard handled properly
Performance:
- Caching implemented (if high volume)
- Request timeout set
- Minimal data fetched
- Bundle size optimized
Testing:
- Unit tests for core logic
- Integration tests with real API
- Tested on slow networks
- Tested with various query types
- Mobile device testing
Monitoring:
- Analytics tracking set up
- Error logging configured
- Usage monitoring in place
- Budget alerts configured
Integration with Other Skills
Works with:
- mapbox-search-patterns: Parameter selection and optimization
- mapbox-web-integration-patterns: Framework-specific patterns
- mapbox-token-security: Token management and security
- mapbox-web-performance-patterns: Optimizing search performance
Resources
- Search Box API Documentation
- Geocoding API Documentation
- Mapbox Search JS
- Search SDK for iOS
- Search SDK for Android
- Location Helper Tool - Calculate bounding boxes
Quick Decision Guide
User says: "I need location search"
- Ask discovery questions (Questions 1-6 above)
- Recommend product:
- Search Box API (default for all interactive/user-facing search, including address geocoding)
- Geocoding API only for batch/server-side/permanent geocoding
- Platform SDK preferred (Search JS for web, native SDKs for mobile)
- Implement with:
- ✅ Debouncing
- ✅ Session tokens
- ✅ Geographic filtering
- ✅ Error handling
- ✅ Good UX
- Test thoroughly
- Monitor in production
Remember: The best search implementation asks the right questions first, then builds exactly what the user needs - no more, no less.
How to use mapbox-search-integration 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 mapbox-search-integration
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mapbox-search-integration from GitHub repository mapbox/mapbox-agent-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 mapbox-search-integration. Access the skill through slash commands (e.g., /mapbox-search-integration) 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
4.8★★★★★70 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Useful defaults in mapbox-search-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Luis Anderson· Dec 28, 2024
mapbox-search-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Harper Okafor· Dec 24, 2024
Registry listing for mapbox-search-integration matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Chawla· Dec 20, 2024
We added mapbox-search-integration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Luis Brown· Dec 20, 2024
Keeps context tight: mapbox-search-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kiara Perez· Dec 16, 2024
Useful defaults in mapbox-search-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★William Ghosh· Dec 12, 2024
I recommend mapbox-search-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arjun Taylor· Dec 4, 2024
mapbox-search-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Oshnikdeep· Nov 19, 2024
mapbox-search-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Benjamin Diallo· Nov 19, 2024
mapbox-search-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 70