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).

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "multi-stop" with this command: npx skills add xiejinsong/multi-stop

⚠️ 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.


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]({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

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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

GigaChat (Sber AI) Proxy

Integrate GigaChat (Sber AI) with OpenClaw via gpt2giga proxy

Registry SourceRecently Updated
3600smvlx
General

TencentCloud Video Face Fusion

通过提取两张人脸核心特征并实现自然融合,支持多种风格适配,提升创意互动性和内容传播力,广泛应用于创意营销、娱乐互动和社交分享场景。

Registry SourceRecently Updated
General

TencentCloud Image Face Fusion

图片人脸融合(专业版)为同步接口,支持自定义美颜、人脸增强、牙齿增强、拉脸等参数,最高支持8K分辨率,有多个模型类型供选择。

Registry SourceRecently Updated
General

YoudaoNote News

有道云笔记资讯推送:基于收藏笔记分析关注话题,推送最新相关资讯。支持对话触发与每日定时推送(如早上9点)。触发词:资讯推送、设置资讯推送、生成资讯推送。

Registry SourceRecently Updated
1.5K1lephix