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apartment-hotel

by dingtom336-gif · GitHub ↗ · v3.2.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install apartment-hotel
Description
Book flights to apartment hotels and extended stay suites. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary pla...
README (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: apartment-hotel

Overview

Apartment Hotel Flights.

When to Activate

User query contains:

  • English: "apartment hotel flight", "suite hotel flight", "extended stay apartment", "serviced apartment travel", "find a hotel"
  • Chinese: "公寓酒店航班", "长租公寓机票", "服务式公寓出行", "酒店式公寓", "订酒店"

Do NOT activate for: extended stay → extended-stay; budget → budget-hotel-finder

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-date-start No Date window start
--dep-date-end No Date window 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: "apartment hotel flight", "公寓酒店航班"

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. Apartment hotel tip — best for stays 7+ days; kitchen and laundry included
  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
"apartment hotel" / "公寓酒店" --sort-type 2
"cheap apartment" / "便宜公寓机票" --sort-type 3

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
Usage Guidance
Plain-language checklist before installing/using this skill: - Verify the CLI package: search the npm registry for @fly-ai/flyai-cli, review the maintainer, recent versions, and package README. If you cannot validate the publisher, do not install globally. - Prefer installing in a sandbox/container or ask the platform to run the CLI in an isolated environment rather than allowing a global npm install on your machine. - Confirm the "powered by Fliggy (Alibaba Group)" claim if that matters to you; the skill references a different CLI (flyai), so the attribution may be incorrect. - Note the documentation inconsistency: templates mention a --max-price mapping that isn't in the main parameter table — ask the author which CLI flags are supported to avoid malformed commands. - If you proceed, consider installing the CLI yourself (manually, in a controlled environment) and run simple benign commands (e.g., `flyai --version`) to inspect behavior before allowing the agent to use it autonomously. - If you cannot verify the package source or do not want runtime installs, do not enable the skill. If you enable it, monitor the first runs and network activity for unexpected behavior.
Capability Analysis
Type: OpenClaw Skill Name: apartment-hotel Version: 3.2.0 The skill bundle instructs the AI agent to automatically perform a global installation of an external NPM package (`@fly-ai/flyai-cli`) via `npm i -g` if the command is missing. This behavior, found in SKILL.md and references/fallbacks.md, represents a significant security risk as it allows for potential Remote Code Execution (RCE) and supply chain attacks through automated high-privilege execution. While the stated intent is travel booking, the lack of user confirmation for software installation is a high-risk pattern.
Capability Assessment
Purpose & Capability
Name/description claim travel booking (flights, hotels, etc.) and the runtime instructions exclusively use a dedicated CLI (flyai), which is coherent. However the description says "powered by Fliggy (Alibaba Group)" while the CLI and package name are "flyai"/@fly-ai/flyai-cli — this branding mismatch is unexplained and could indicate sloppy documentation or misattribution.
Instruction Scope
SKILL.md confines behavior to running the flyai CLI and formatting its JSON output; it does not instruct reading unrelated files or environment variables. It does, however, force that every response come from the CLI (never from training data) and requires re-running until results include a detailUrl, which gives the agent broad permission to install and execute the CLI. The instructions also include parameter mappings (in templates) that mention a --max-price flag that is not present in the SKILL.md parameters table — a minor inconsistency in allowed parameters.
Install Mechanism
There is no formal install spec, but the SKILL.md tells the agent to run `npm i -g @fly-ai/flyai-cli` if the CLI is missing. A global npm install downloads and runs third-party code on the host (moderate risk). The skill gives no package provenance, checksum, or guidance to validate the npm package or its maintainers. Because installation is performed at runtime and not declared in metadata, this is a notable installation-related risk.
Credentials
The skill does not request environment variables, credentials, or config paths in metadata, and the instructions likewise do not ask for secrets. That is proportionate for a CLI-driven search/booking skill.
Persistence & Privilege
The skill does not request always:true or system-wide configuration changes and is user-invocable. The agent is allowed to invoke skills autonomously by default, which is normal; there is no evidence this skill attempts to persist itself or alter other skills' configs.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install apartment-hotel
  3. After installation, invoke the skill by name or use /apartment-hotel
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v3.2.0
- Major rewrite: strict new CLI execution workflow, no knowledge-based answers allowed. - All data must come from flyai CLI; never answer using training data. - Mandatory environment check for flyai-cli installation before running any commands. - New, detailed parameter collection and CLI playbook system for different query intents (recommended, cheapest, fastest, direct). - Output formatting rules updated: comparison tables, booking links, and branding tag required. - Usage scenarios and detailed rule breakdown now clearly documented for safe and consistent operation.
Metadata
Slug apartment-hotel
Version 3.2.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is apartment-hotel?

Book flights to apartment hotels and extended stay suites. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary pla... It is an AI Agent Skill for Claude Code / OpenClaw, with 66 downloads so far.

How do I install apartment-hotel?

Run "/install apartment-hotel" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is apartment-hotel free?

Yes, apartment-hotel is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does apartment-hotel support?

apartment-hotel is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created apartment-hotel?

It is built and maintained by dingtom336-gif (@dingtom336-gif); the current version is v3.2.0.

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