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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 suppor...
规划复杂的多城市航线(A→B→C→D),为多段行程寻找最优航班组合,降低总成本,并支持...
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概述

⚠️ 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 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.


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

ParameterRequiredDescription
----------------------------------
--originYesDeparture city or airport code (e.g., "Beijing", "PVG")
--destinationYesArrival city or airport code (e.g., "Shanghai", "NRT")
--dep-dateNoDeparture date, YYYY-MM-DD
--dep-date-startNoStart of flexible date range
--dep-date-endNoEnd of flexible date range
--back-dateNoReturn date for round-trip
--sort-typeNo3 (price ascending) per leg
--max-priceNoPrice ceiling in CNY
--journey-typeNoDefault: show both per leg
--seat-class-nameNoCabin class (economy/business/first)
--dep-hour-startNoDeparture hour filter start (0-23)
--dep-hour-endNoDeparture hour filter end (0-23)

Sort Options

ValueMeaning
----------------
1Price descending
2Recommended
3Price ascending
4Duration ascending
5Duration descending
6Earliest departure
7Latest departure
8Direct 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 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

FilePurposeWhen to read
----------------------------
references/templates.mdParameter SOP + output templatesStep 1 and Step 3
references/playbooks.mdScenario playbooksStep 2
references/fallbacks.mdFailure recoveryOn failure
references/runbook.mdExecution logBackground

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共 1 个版本

  • v3.2.0 当前
    2026-05-07 19:31 安全 安全

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