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airport-hotel

作者 dingtom336-gif · GitHub ↗ · v3.2.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install airport-hotel
功能描述
Book flights with airport hotel recommendations for layovers and early departures. Also supports: flight booking, hotel reservation, train tickets, attractio...
使用说明 (SKILL.md)

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-hotel

Overview

Airport Hotel Flights.

When to Activate

User query contains:

  • English: "airport hotel flight", "layover hotel flight", "near airport travel", "transit hotel trip", "find a hotel"
  • Chinese: "机场酒店航班", "中转酒店机票", "机场附近出行", "过夜转机", "订酒店"

Do NOT activate for: transit hotel → airport-transit-hotel; hotel → compare-hotels

Prerequisites

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

Parameters

Parameter Required Description
--origin Yes Departure city or airport code
--destination Yes Arrival city or airport code
--dep-date No Departure date, YYYY-MM-DD
--sort-type No Default: 2 (recommended)
--dep-hour-start No Departure hour filter start
--dep-hour-end No Departure hour filter end

Sort Options

Value Meaning When to Use
2 Recommended Best overall options
3 Price ascending Cheapest flights
4 Duration ascending Fastest flights
8 Direct flights first Prefer 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 hotel flight", "机场酒店航班"

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. Airport hotel tip — book hotel + flight together for best combo deals
  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 Query CLI Parameter Mapping
"airport hotel" / "机场酒店" --sort-type 2
"early departure + hotel" / "早班+酒店" --dep-hour-start 5 --dep-hour-end 8 --sort-type 2

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
安全使用建议
This skill behaves like a wrapper around a third‑party CLI (flyai) and requires installing a global npm package at runtime; the publisher and homepage are missing and the manifest also claims it’s “powered by Fliggy,” which doesn’t match the CLI name. Before installing or enabling this skill: 1) Verify the @fly-ai/flyai-cli package on npmjs.com (publisher, downloads, code) and confirm it’s trustworthy; 2) confirm whether the service actually integrates with Fliggy or another provider and ask the skill author for a homepage/source link; 3) be aware that installing a global npm package gives code execution capability on the host (requires Node.js and may require elevated permissions); 4) if you can’t verify the CLI, avoid granting the agent the ability to run installs or execute shell commands, or require manual review/installation of the CLI yourself. If you want higher assurance, ask the author for a signed release URL, repository link, or corporate affiliation that matches the claim of Fliggy/AliBaba.
功能分析
Type: OpenClaw Skill Name: airport-hotel Version: 3.2.0 The skill bundle is classified as suspicious because it explicitly instructs the AI agent to perform a global installation of an external NPM package (`npm i -g @fly-ai/flyai-cli`) if the command is not found (seen in `SKILL.md` and `references/fallbacks.md`). This is a high-risk supply-chain behavior that allows for the execution of remote artifacts on the host system. While the instructions are aligned with the stated travel-booking purpose and lack clear evidence of intentional harmful behavior like data exfiltration, the automated installation of third-party software is a significant security risk.
能力评估
Purpose & Capability
The skill claims to be “powered by Fliggy (Alibaba Group)” but all runtime actions target a third‑party CLI named @fly-ai/flyai-cli (flyai). There is no homepage or source URL to verify the provider. Requesting a CLI to perform searches is plausible for a booking skill, but the mismatch in vendor attribution and lack of provenance is unexplained.
Instruction Scope
The SKILL.md requires the agent to run the flyai CLI for every response and mandates installing a global npm package if the CLI is missing. It forbids answering from training data and forces re-execution until every result contains a booking link. While running a provider CLI is consistent with the skill's purpose, the instructions give the agent capacity to install and execute third‑party code at runtime and provide no fallback except aborting; that broadens the runtime authority beyond simple query formatting.
Install Mechanism
There is no formal install spec, but the runtime instructions instruct: `npm i -g @fly-ai/flyai-cli`. Installing a global npm package at runtime is a moderate risk — it downloads and executes code from the npm registry. The manifest provides no publisher verification, no checksum, and no official upstream URL; this increases risk because the package provenance is unverified.
Credentials
The skill does not request environment variables, secrets, or config paths. All required inputs are user-provided parameters (origin, destination, dates, filters), which are proportionate to the stated purpose.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges or system-wide configuration changes. It does direct the agent to perform installs at runtime, but it does not claim persistent control over other skills or global agent settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install airport-hotel
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /airport-hotel 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v3.2.0
- Enforces strict CLI-only data sourcing; no responses from prior training data. - Adds detailed step-by-step command execution and output formatting instructions. - Introduces robust parameter mapping and scenario playbooks for varied user queries. - Mandates validation: every booking result must include a valid [Book](detailUrl) link from real-time CLI output. - Expands supported features: flight & hotel booking, train/attraction tickets, itinerary planning, visa info, insurance, car rental, and more via Fliggy. - Strengthens language handling and error/installation checks for higher reliability.
元数据
Slug airport-hotel
版本 3.2.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

airport-hotel 是什么?

Book flights with airport hotel recommendations for layovers and early departures. Also supports: flight booking, hotel reservation, train tickets, attractio... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 63 次。

如何安装 airport-hotel?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install airport-hotel」即可一键安装,无需额外配置。

airport-hotel 是免费的吗?

是的,airport-hotel 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

airport-hotel 支持哪些平台?

airport-hotel 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 airport-hotel?

由 dingtom336-gif(@dingtom336-gif)开发并维护,当前版本 v3.2.0。

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