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apartment-hotel

v3.2.0

Book flights to apartment hotels and extended stay suites. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary pla...

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Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "apartment-hotel" (dingtom336-gif/apartment-hotel) from ClawHub.
Skill page: https://clawhub.ai/dingtom336-gif/apartment-hotel
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 apartment-hotel

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npx clawhub@latest install apartment-hotel
Security Scan
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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.
What to consider before installing
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.

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

latestvk973y9j4dgh5230qk5rvwm9de985fwn3
58downloads
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: 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

ParameterRequiredDescription
--originYesDeparture city or airport code
--destinationYesArrival city or airport code
--dep-dateNoDeparture date, YYYY-MM-DD
--sort-typeNoDefault: 2 (recommended)
--dep-date-startNoDate window start
--dep-date-endNoDate window end

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: "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 QueryCLI Parameter Mapping
"apartment hotel" / "公寓酒店"--sort-type 2
"cheap apartment" / "便宜公寓机票"--sort-type 3

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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