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Destination Trip Planning

Community
AtlasOmnia
destination-trip-planning

destination-trip-planning — Research a specific destination/attraction, gather pricing/logistics, and build a themed trip itinerary tailored to the traveler's profile.

Overview

PublisherAtlasOmnia
Repositorydonna-starter
Skill namedestination-trip-planning
Stars
131
Forks
9
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AtlasOmnia on GitHub. Read the source before you install it.

Installation

Install the Destination Trip Planning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AtlasOmnia/donna-starter/tree/main/skills/productivity/destination-trip-planning
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AtlasOmnia/donna-starter.git /tmp/donna-starter
mkdir -p .claude/skills
cp -r /tmp/donna-starter/skills/productivity/destination-trip-planning .claude/skills/destination-trip-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Destination Trip Planning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Destination Trip Planning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Destination Trip Planning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Destination Trip Planning

Plan themed trips to specific attractions or destinations with real pricing, logistics, and a tight itinerary focused on the theme — not a generic city guide.

When to Use

  • User wants to plan a trip to a specific attraction, event, or themed destination (e.g., Graceland, a national park, a festival, a resort)
  • User asks for "what to do there" at a given destination
  • User needs a go/no-go recommendation on trip length and budget
  • User wants to know what's nearby that fits the same theme

Core Workflow

Phase 1: Lock the Destination and Constraints

Clarify up front:

  • Exact destination — confirm the city/location (Graceland = Memphis, not Nashville)
  • Traveler profile — age, mobility, energy level, interests
  • Time window — exact check-in and checkout dates, calendar days, and number of nights; if the user corrects the duration, recompute the entire budget
  • Traveler and payment structure — total headcount, ages, likely activity participants, and which person/team pays which share
  • Budget sense — looking for deals vs. full-service package
  • 2–3 day trip vs longer

Phase 1.5: Clarify What "The Attraction" Actually Is

First-time visitors often don't know the attraction's layout. Preempt "is it just a house?" or "the house is the museum, right?" moments by describing the physical campus up front:

  • Is the attraction one building or a campus spread across multiple sites?
  • What's the ticket actually cover? (e.g., Graceland's $85 Elvis Experience = mansion across the street + 200K sq ft exhibit complex + two airplanes + on-site dining/shopping)
  • Are there free areas (gravesite, gates, grounds)?
  • Can the traveler see everything without re-entering or is it split across days?

This matters more for older travelers — they need to know if there's significant walking, shuttles, or stairs between components.

Phase 2: Gather Destination Research

For the primary attraction:

  • Ticket pricing tiers (standard vs VIP vs all-access)
  • How long the full experience takes
  • Peak vs off-peak times
  • Available discounts (senior, AAA, military, promo codes)
  • Parking / shuttle / accessibility info

For on-site hotel (if applicable):

  • Lead with the official property name first when the user asks for "the [attraction] hotel" (for Graceland: The Guest House at Graceland)
  • Separate the hotel from the attraction clearly: answer whether the hotel is required or merely part of the fuller themed experience. State this explicitly when the user asks some version of "do we have to stay there?"
  • Room rates and resort fees
  • Package deals bundling tickets + hotel
  • Current promos (seasonal savings, flash sales)
  • Proximity to the attraction (walkable?)
  • Amenities for an easy stay (no need to leave)
  • If exact stay-date pricing is not directly retrievable, give the best grounded public price signal you can find, label it as approximate, and do not imply it is a verified date-specific rate
  • When useful, frame the decision as experience vs cost efficiency: themed on-site stay vs cheaper off-site hotel vs attraction-only day trip

For travel logistics:

  • Distance from user's home city → drive vs fly
  • Drive time with realistic traffic
  • Nearest airport and transit from airport to attraction
  • If driving: split the trip with overnight stops

Phase 3: Build the Themed Itinerary

CRITICAL RULE: When the trip has a theme, every suggestion must serve the theme.

When the user asks "what would she do there" or "anything else related":

  • List ONLY attractions, restaurants, and activities related to the theme
  • Do NOT mix in generic city recommendations (parks, unrelated museums, shopping districts, tourist attractions)
  • If you're not sure, ask "is she also interested in [city] stuff, or just [theme]?"
  • Label each recommendation with its thematic connection (e.g., "where Elvis recorded his first song")

For each recommendation, give:

  • What it is and why it matters
  • How long it takes
  • Distance from the hotel / attraction hub
  • Walkability for an older traveler

Phase 4: Present the Itinerary

Structure by day:

  1. Day 1 — Arrive, settle in, easy evening
  2. Day 2 — Main attraction day (full, relaxed pace)
  3. Day 3 — Morning theme-option + depart

No "must see everything" pressure. For older travelers, prioritize:

  • Short distances between stops
  • Seating available
  • Minimal walking / stairs
  • Easy Uber or shuttle access
  • Afternoon rest time

Output Style

  • Lead with "here's what she'd actually do" — concrete, not conceptual
  • Use tables for pricing comparisons
  • Use bullet lists for attraction lineups
  • Clearly separate the primary attraction from optional extras
  • Before moving from research to pricing, confirm the itinerary with the user
  • For unstable group decisions, do not wait for perfect commitment before helping: present a provisional primary-trip budget plus a clearly separated fallback for the smaller subgroup, with assumptions, optional activity add-ons, and the decision deadline that matters for peak inventory
  • When the party includes older travelers or children, favor resort-accessible lodging and simple transport over an isolated property, even if the isolated cabin appears cheaper

Pitfalls

  • Do not assume the destination city is the attraction city. Graceland = Memphis, not Nashville. Confirm geography early.
  • Do not suggest generic city attractions on a themed trip. When someone asks for Elvis stuff, give them Elvis stuff — not Beale Street blues clubs or the Civil Rights Museum unless they ask.
  • Travel/attraction websites frequently block web_extract and even browser_navigate. They use Cloudflare or JS-heavy rendering. Fallback chain: search-result snippets → press releases on the official site → third-party aggregator articles. For JS-heavy blog-type sites that web_extract times out on, try curl -sL -H "User-Agent: Mozilla/5.0 ..." <url> to pull raw HTML — the content is often inline despite the SPA shell. Do not keep retrying web_extract, and don't re-navigate a Cloudflare-blocked page.
  • Do not overbuild. A 70-year-old who wants to see Elvis doesn't need a 12-stop scavenger hunt. 2-3 meaningful things plus Graceland is plenty.
  • Duration and payer drift. When the user changes a trip from one number of days/nights to another, recompute flights, lodging, meals, transport, activities, and all subgroup totals. When teams pay separately, do not report only a combined household total.
  • Airport handoff pattern: When you suggest an airport and the user asks about a different one, research it quickly and neutrally — don't argue for your original suggestion. After reporting the data (connecting, more expensive), the user may pick the regional option anyway — accept without re-explaining the comparison.
  • Confirm each phase before proceeding. User wants to confirm: research findings → itinerary → day count → themed extras → THEN pricing. When they reply with a short affirmative like "y" or "k," that's their signal to move to the next phase. Do not jump ahead.
  • Do not bury the practical info under blog-style prose. Give ticket prices, hours, and how-to-book in the first pass.
  • When the user asks "anything else [theme]-related?" do NOT re-list what you already told them. Give new specific themed options only. If you already covered them all, say so and offer a themed restaurant, historic site, or offbeat spot — not generic city stuff unless they explicitly ask for it.
  • Do not assume the user knows what the ticket price includes. When presenting pricing, add one clear line about what each tier actually covers (e.g., "$85 standard ticket = mansion tour + exhibit complex + airplanes, ~3.5-4 hrs"). This prevents the "the house is the museum right?" confusion.

Verification

Before presenting:

  • Primary attraction pricing confirmed from at least one source
  • Hotel package deal checked against bundle vs separate pricing
  • Themed attractions only — no generic city filler
  • Realistic pace for the traveler's profile

Total Trip Estimate

After pricing is finalized, deliver a one-table budget estimate:

CategoryEstimated Cost
✈️ Flights (round trip)$xxx
🏨 Hotel (N nights)$xxx
🎫 Attraction tickets$xxx
🚕 Local transport (Uber/shuttle)$xxx
🍔 Meals (N days)$xxx
Total (approx)~$xxx
  • Use real fares and rates where available, rounded estimates where not
  • For groups paying separately, show per-person cost and each paying subgroup/team total, with an explicit split rule for shared lodging and transportation
  • State the exact dates and nights in the budget heading; do not reuse an earlier duration after the user corrects it
  • Label which numbers are verified vs. estimates
  • Include a suggested date range (midweek preferred, event dates to avoid)

Frequently asked questions

What does the Destination Trip Planning AI skill do?

destination-trip-planning — Research a specific destination/attraction, gather pricing/logistics, and build a themed trip itinerary tailored to the traveler's profile.

Why use Destination Trip Planning on TypingMind?

Because you install it once and use it with any model. Destination Trip Planning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Destination Trip Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AtlasOmnia/donna-starter/tree/main/skills/productivity/destination-trip-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Destination Trip Planning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Destination Trip Planning?

As many as you like. As long as a model supports skills, you can use Destination Trip Planning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Destination Trip Planning AI skill free?

Yes. It is published on GitHub by AtlasOmnia under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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