| name | trip-map-builder |
| description | End-to-end trip planning: gather user constraints, build a reference itinerary, research locations and dining signals via 大众点评 + 小红书, then generate an interactive mobile-first map page (Leaflet + timeline) and optionally deploy to Vercel. Use when user asks to plan a trip, create an itinerary, research restaurants on 大众点评/小红书, build a trip map page, or says "行程规划", "行程地图", "trip map", "plan my trip", "做个行程". Covers the full pipeline from scattered inputs (screenshots, wishlists) to a deployable reference map with navigation links, 小红书 links, payment info, and reservation buttons.
|
Trip Map Builder
Three-phase pipeline: Plan → Research → Build.
The output is a reference itinerary, not a script the traveler must obey.
During the trip, weather, current location, fatigue, and hunger can override
the original plan.
Shared memory
Before planning or building, read ~/.trip-map-builder/MEMORY.md if it
exists. Use it only for durable traveler context:
- pace preference
- food and drink preferences
- budget habits
- payment and navigation preferences
- previously generated trip outputs
- recurring constraints and unresolved follow-ups
If the file does not exist, continue normally. Do not block on memory setup.
Do not store raw screenshots, passport data, booking codes, full chat logs, or
other sensitive/private material.
After each completed trip plan, research pass, or map build, update
~/.trip-map-builder/MEMORY.md with only durable facts:
# Trip Map Builder Memory
## Traveler Defaults
- Departure city:
- Pace:
- Food preferences:
- Budget habits:
- Payment preference:
- Navigation preference:
- Language preference:
## Past Trips
| Trip | Dates | Destination | Output | Notes |
|------|-------|-------------|--------|-------|
## Reusable Preferences
-
## Open Threads
-
Phase 1: Plan the itinerary
Read references/trip-planning.md for the full methodology.
Core sequence:
- Extract hard constraints — dates, flight times, terminals, hotel location
- Group user's wishlist — city-easy / needs-reservation / far-suburbs / pass-through
- Cut high-risk items first — too far, holiday-crowded, weather-dependent. Say what was cut and why.
- Arrange by area — one main area per day, first day light, last day close to airport
- Fill in meals — daily-area candidates first, 大众点评 + 小红书 signals second, fame last.
- Add tickets & transport — only critical ones (museum tickets, airport transfer)
- Write reference doc — conclusion first, then daily plan, weather-sensitive spots, meal areas, and what was cut
Key principles:
- Not everything the user listed fits. Delete for them.
- One area per day. One reservation-required spot per day max.
- Itineraries should be smooth, not packed.
- The plan gives coordinates for later adjustment; it does not pretend reality will follow the timeline.
- All user-facing questions follow the 4-beat format: Re-ground → Simplify → Recommend → Options. See
references/trip-planning.md § 用户交互 for examples and anti-patterns.
Phase 2: Research via 大众点评 + 小红书
Read references/dianping-research.md for the 大众点评 OpenCLI workflow.
Read references/xhs-research.md for the 小红书 OpenCLI + CDP workflow.
For restaurants, use 大众点评 as the main Chinese dining signal for taste,
queue risk, value, and obvious traps. Use 小红书 to supplement atmosphere,
recent experience, photo-worthiness, and soft warnings. Do not bend a whole
day around a famous restaurant unless it is already on the route.
小红书 core sequence:
- Launch Chrome with
--remote-debugging-port=9223
- Connect via OpenCLI's
CDPBridge
- Navigate to
xiaohongshu.com/search_result?keyword=<encoded> (never simulate input box)
- Intercept
POST /api/sns/web/v1/search/notes response
- Pick top 2-3 notes by relevance, open detail pages
- Extract via DOM:
#detail-title, #detail-desc, .author-container .username
- Compress to one decision-useful sentence per store, write back to local
.md
Filtering rules:
- Keep: specific store name, address, dish, personal experience, repeated keywords
- Drop: generic area roundups, reposts, pure emotion, "氛围很好" x3
- Output: store name + one representative link + 2-3 sentence verdict
Phase 3: Build the map page
- Copy
assets/template.html → index.html
- Fill
HOTEL object and DAYS array with structured data from Phase 1+2
- Each location needs: name, lat/lng, type, time, desc; optional: budget, detail, pay, xhs, reserve, gmap
- Fill
overviewContent() with trip summary, payment warnings
- Apply design system — default template uses Apple style, but can switch to any style from awesome-design-md
Location types: food | spot | drink | hotel | transport
Payment chip values: 1 = confirmed yes (green), 0.5 = maybe (orange), omit = not shown
Design system (optional)
Default template uses Apple design system (SF Pro, light theme, frosted glass).
To use a different style, grab a DESIGN.md from awesome-design-md:
curl -O https://raw.githubusercontent.com/VoltAgent/awesome-design-md/main/design-md/<brand>/DESIGN.md
Then adjust template.html's :root CSS variables (colors, fonts, spacing, border-radius) to match the chosen DESIGN.md tokens.
Deploy (optional)
git init && git add . && git commit -m "trip map"
gh repo create REPO --public --source=. --push
Dependencies
| Tool | Purpose | Install |
|---|
| OpenCLI | 大众点评 adapter + 小红书调研 | npm install -g @jackwener/opencli |
| Chrome/Chromium | 浏览器 + 远程调试 | 已有 |
| Leaflet.js | 地图渲染（CDN 引入，无需安装） | template.html 内置 |
| gh CLI | GitHub 仓库创建（可选） | brew install gh |
Resources
references/trip-planning.md — itinerary planning methodology, input/output templates, selection principles, common pitfallsreferences/dianping-research.md — 大众点评 OpenCLI search/shop workflow, dining decision signals, writeback formatreferences/xhs-research.md — OpenCLI installation, Chrome CDP setup, 小红书 search workflow, API details, filtering criteriaassets/template.html — single-file HTML map template (Leaflet + Apple design system)- awesome-design-md — 60+ brand design systems (Apple, Vercel, Stripe, Linear, etc.) for alternative styling
