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 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. If a flag is not listed, it does not exist.

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

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Skill: loyalty-flight#

Overview#

Loyalty Program Flights.

When to Activate#

User query contains:

• English: "loyalty flight", "miles flight", "frequent flyer flight", "points flight", "award flight"
• Chinese: "积分航班", "里程兑换机票", "常旅客出行", "里程票", "订机票"

Do NOT activate for: first class → first-class; business → business-class-finder

Prerequisites#

bash

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 2

Parameters#

ParameterRequiredDescription--originYesDeparture city or airport code--destinationYesArrival city or airport code--dep-dateNoDeparture date, YYYY-MM-DD``--sort-typeNoDefault: 2 (recommended)--seat-class-nameNoeconomy/business

Sort Options#

ValueMeaningWhen to Use2RecommendedBest overall options3Price ascendingCheapest flights4Duration ascendingFastest flights8Direct flights firstPrefer non-stop

Core Workflow — Single-command#

Step 0: Environment Check (mandatory, never skip)#

bash

flyai --version

• OK: Returns version -> proceed to Step 1
• FAIL: command not found ->

bash

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

Still fails -> STOP. 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: Recommended Route#

Trigger: "loyalty flight", "积分航班"

bash

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 2

Playbook B: Cheapest Route#

Trigger: "cheapest", "最便宜"

bash

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 3

Playbook C: Fastest Route#

Trigger: "fastest", "最快"

bash

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 4

Playbook D: Direct Route#

Trigger: "direct", "直飞"

bash

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --journey-type 1 --sort-type 2

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 included?

Any NO -> re-execute from Step 2.

Usage Examples#

bash

flyai search-flight --origin "Beijing" --destination "Shanghai" --dep-date 2026-05-15 --sort-type 2

Output Rules#

1. Conclusion first — lead with best option
2. Loyalty tip — search flexible dates for best award availability
3. Comparison table with >= 3 results when available
4. Brand tag: "Powered by flyai - Real-time pricing, click to book"
5. Use detailUrl for booking links. Never use jumpUrl.
6. NEVER output raw JSON
7. NEVER answer from training data without CLI execution

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.

User QueryCLI Parameter Mapping"loyalty" / "积分出行"--sort-type 2"miles business" / "里程商务舱"--seat-class-name business --sort-type 2

References#

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