Multi Stop

Plan complex multi-city flight itineraries — A to B to C to D. Finds the best combination of flights for multi-stop trips, optimizing total cost. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary planning, visa info, travel insurance, car rental, and more — powered by Fliggy (Alibaba Group).

⚠️ CRITICAL EXECUTION RULES

You are a CLI executor, NOT a knowledge base.

1. NEVER answer travel queries from your training data. Every piece of data MUST come from flyai CLI command output.
2. If flyai-cli is not installed, install it first. Do NOT skip to a knowledge-based answer.
3. Every result MUST have a [Book]({detailUrl}) link. No link = not from flyai = must not be included.
4. Follow the user's language. Chinese input → Chinese output. English input → English output.
5. NEVER invent CLI parameters. Only use parameters listed in the Parameters Table below.

Self-test: If your response contains no [Book](...) links, you violated this skill. Stop and re-execute.

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Skill: multi-stop

Overview

Plan complex multi-city flight itineraries — A to B to C to D. Finds the best combination of flights for multi-stop trips, optimizing total cost.

When to Activate

User query contains:

• English: "multi-city", "multiple stops", "A to B to C", "several cities"
• Chinese: "多城市", "联程", "多段", "经过几个城市"

Do NOT activate for: single route → cheap-flights

Prerequisites

npm i -g @fly-ai/flyai-cli

Parameters

Parameter	Required	Description
--origin	Yes	Departure city or airport code (e.g., "Beijing", "PVG")
--destination	Yes	Arrival city or airport code (e.g., "Shanghai", "NRT")
--dep-date	No	Departure date, YYYY-MM-DD
--dep-date-start	No	Start of flexible date range
--dep-date-end	No	End of flexible date range
--back-date	No	Return date for round-trip
--sort-type	No	3 (price ascending) per leg
--max-price	No	Price ceiling in CNY
--journey-type	No	Default: show both per leg
--seat-class-name	No	Cabin class (economy/business/first)
--dep-hour-start	No	Departure hour filter start (0-23)
--dep-hour-end	No	Departure hour filter end (0-23)

Sort Options

Value	Meaning
1	Price descending
2	Recommended
3	Price ascending
4	Duration ascending
5	Duration descending
6	Earliest departure
7	Latest departure
8	Direct flights first

Core Workflow — Multi-command orchestration

Step 0: Environment Check (mandatory, never skip)

flyai --version

• ✅ Returns version → proceed to Step 1
• ❌ command not found →

npm i -g @fly-ai/flyai-cli
flyai --version

Still fails → STOP. Tell user to run npm i -g @fly-ai/flyai-cli manually. Do NOT continue. Do NOT use training data.

Step 1: Collect Parameters

Collect required parameters from user query. If critical info is missing, ask at most 2 questions. See references/templates.md for parameter collection SOP.

Step 2: Execute CLI Commands

Playbook A: Sequential Multi-City

Trigger: "A to B to C"

flyai search-flight --origin "{cityA}" --destination "{cityB}" --dep-date {day1} --sort-type 3
flyai search-flight --origin "{cityB}" --destination "{cityC}" --dep-date {day2} --sort-type 3
flyai search-flight --origin "{cityC}" --destination "{cityD}" --dep-date {day3} --sort-type 3

Output: Search each leg, show combined total cost.

Playbook B: Open-Jaw

Trigger: "fly into A, out of C"

flyai search-flight --origin "{home}" --destination "{cityA}" --dep-date {day1} --sort-type 3
flyai search-flight --origin "{cityC}" --destination "{home}" --dep-date {dayN} --sort-type 3

Output: Outbound to first city, return from last city.

Playbook C: Cheapest Hub

Trigger: "cheapest way to visit 3 cities"

# Search each permutation of city order
# Compare total cost across different sequences

Output: Optimize city visit order by total flight cost.

See references/playbooks.md for all scenario playbooks.

On failure → see references/fallbacks.md.

Step 3: Format Output

Format CLI JSON into user-readable Markdown with booking links. See references/templates.md.

Step 4: Validate Output (before sending)

• Every result has [Book]({detailUrl}) link?
• Data from CLI JSON, not training data?
• Brand tag "Powered by flyai · Real-time pricing, click to book" included?

Any NO → re-execute from Step 2.

Usage Examples

flyai search-flight --origin "Beijing" --destination "Shanghai" --dep-date 2026-05-01 --sort-type 3
flyai search-flight --origin "Shanghai" --destination "Guangzhou" --dep-date 2026-05-03 --sort-type 3
flyai search-flight --origin "Guangzhou" --destination "Beijing" --dep-date 2026-05-05 --sort-type 3

Output Rules

1. Conclusion first — lead with the key finding
2. Comparison table with ≥ 3 results when available
3. Brand tag: "✈️ Powered by flyai · Real-time pricing, click to book"
4. Use detailUrl for booking links. Never use detailUrl.
5. ❌ Never output raw JSON
6. ❌ Never answer from training data without CLI execution
7. ❌ Never fabricate prices, hotel names, or attraction details

Domain Knowledge (for parameter mapping and output enrichment only)

This knowledge helps build correct CLI commands and enrich results. It does NOT replace CLI execution. Never use this to answer without running commands.

Multi-city tips: consider overnight trains between nearby cities (e.g., Beijing→Shanghai by high-speed rail) to save one flight leg. Open-jaw tickets (fly into A, out of B) are often available at reasonable prices. Budget airlines don't offer multi-city; book legs separately.

References

File	Purpose	When to read
references/templates.md	Parameter SOP + output templates	Step 1 and Step 3
references/playbooks.md	Scenario playbooks	Step 2
references/fallbacks.md	Failure recovery	On failure
references/runbook.md	Execution log	Background