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airport-lounge

v3.2.0

Search for flights with airport lounge access and premium terminal options. Also supports: flight booking, hotel reservation, train tickets, attraction ticke...

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Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "airport-lounge" (dingtom336-gif/airport-lounge) from ClawHub.
Skill page: https://clawhub.ai/dingtom336-gif/airport-lounge
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

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openclaw skills install airport-lounge

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npx clawhub@latest install airport-lounge
Security Scan
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medium confidence
Purpose & Capability
The stated purpose (search/booking for lounges and premium travel) aligns with using a CLI search tool. However the SKILL.md repeatedly references a 'flyai' CLI while the description also claims 'powered by Fliggy (Alibaba Group)' — a branding mismatch with no homepage or source to reconcile. The registry metadata does not declare the flyai CLI as a required binary even though runtime rules mandate it, which is an omission.
!
Instruction Scope
Instructions require running and (if missing) installing an external CLI (npm i -g @fly-ai/flyai-cli) and strictly demand all answers come from that CLI output. The files instruct the agent to never invent CLI parameters, yet multiple referenced playbooks and templates use flags that are not listed in the Parameters table (e.g., --journey-type, --max-price) — a direct contradiction that could lead an agent to invent parameters or behave unpredictably. No instructions read unrelated system files or env vars, which is good, but the contradictory parameter rules are a significant operational inconsistency.
!
Install Mechanism
There is no registry install spec, but SKILL.md explicitly tells the agent to run npm i -g @fly-ai/flyai-cli if flyai is missing. Installing a global npm package is a moderate-risk action because it downloads and executes third-party code and writes to the filesystem. The package name is not linked to any homepage/repo in the skill, and the skill author/source is unknown, so the npm install instruction should be treated as potentially risky until the package is audited or its origin verified.
Credentials
The skill does not request environment variables, credentials, or config paths in the registry metadata. The runtime instructions also do not ask for secrets. This is proportionate to a search/booking helper.
Persistence & Privilege
The skill is not always-enabled and does not request special agent-wide privileges. The only privilege-like action is instructing a global npm install, which requires write permissions on the host (may prompt for elevated privileges on some systems). That filesystem write is the main persistence concern.
What to consider before installing
This skill is plausible for searching/bookings, but several red flags mean you should verify before installing: 1) Confirm the upstream project: ask the author for the flyai CLI homepage/repository (or find the @fly-ai/flyai-cli package on npm and inspect its repository and maintainers). 2) Do not run npm i -g unless you trust the package—prefer to audit its source code or run it in a sandbox/container. 3) Ask the author to reconcile branding (Fliggy vs flyai) and to provide a homepage/source and a manifest that declares required binaries. 4) Request clarification about allowed CLI flags (Parameters table should include all flags used in playbooks/templates); the current contradictions could cause the agent to invent parameters. 5) If you must test, run the CLI in an isolated environment (VM or container) and inspect what detailUrl/jumpUrl the CLI returns and where it posts data. If the author can't provide a verifiable package/repository and clear parameter documentation, treat the skill as untrusted.

Like a lobster shell, security has layers — review code before you run it.

latestvk972rvvjxp3gx12qnvwq5q5wsn85exsj
57downloads
0stars
1versions
Updated 4d ago
v3.2.0
MIT-0

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.


Skill: airport-lounge

Overview

Airport Lounge Flights.

When to Activate

User query contains:

  • English: "airport lounge flight", "lounge access flight", "premium terminal flight", "vip flight", "travel booking", "trip search"
  • Chinese: "机场贵宾厅航班", "VIP航班", "头等舱候机", "贵宾通道出行", "机场住宿"

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

Prerequisites

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 Use
2RecommendedBest overall options
3Price ascendingCheapest flights
4Duration ascendingFastest flights
8Direct flights firstPrefer non-stop

Core Workflow — Single-command

Step 0: Environment Check (mandatory, never skip)

flyai --version
  • OK: Returns version -> proceed to Step 1
  • FAIL: command not found ->
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: "airport lounge flight", "机场贵宾厅航班"

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

Playbook B: Cheapest Route

Trigger: "cheapest", "最便宜"

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

Playbook C: Fastest Route

Trigger: "fastest", "最快"

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

Playbook D: Direct Route

Trigger: "direct", "直飞"

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

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. Lounge tip — business class tickets usually include lounge access
  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
"lounge flight" / "贵宾厅航班"--seat-class-name business --sort-type 2
"first class lounge" / "头等舱贵宾"--seat-class-name first --sort-type 2

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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