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Beachfront Resort Finder

作者 xiejinsong · GitHub ↗ · v3.2.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install beachfront-resort-finder
功能描述
Find beach and island resorts — oceanfront rooms, private beaches, tropical paradise stays with direct beach access and water activities. Also supports: flig...
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 command 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.

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


Skill: beachfront-resort-finder

Overview

Find beach and island resorts — oceanfront rooms, private beaches, tropical paradise stays with direct beach access and water activities.

When to Activate

User query contains:

  • English: "beach resort", "oceanfront", "seaside", "island resort"
  • Chinese: "海边度假村", "海景房", "海滩酒店", "海岛住宿"

Do NOT activate for: beach attraction → beach-island-guide

Prerequisites

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

Parameters

Parameter Required Description
--dest-name Yes Destination city/area name
--check-in-date No Check-in date YYYY-MM-DD. Default: today
--check-out-date No Check-out date. Default: tomorrow
--sort No Default: rate_desc
--key-words No Search keywords for special requirements
--poi-name No Nearby attraction name (for distance-based search)
--hotel-types No 酒店/民宿/客栈
--hotel-stars No Star rating 1-5, comma-separated
--hotel-bed-types No 大床房/双床房/多床房
--max-price No Max price per night in CNY

Sort Options

Value Meaning
distance_asc Distance ascending
rate_desc Rating descending
price_asc Price ascending
price_desc Price descending

Core Workflow — Dual-command

Step 0: Environment Check (mandatory, never skip)

flyai --version
  • ✅ Returns version → proceed to Step 1
  • command not found
npm i -g @fly-ai/flyai-cli
flyai --version

Still fails → STOP. Tell user to run npm i -g @fly-ai/flyai-cli manually. 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: Beach Resort

Trigger: "beach hotel", "海边酒店"

flyai search-poi --city-name "{city}" --category "沙滩海岛"
flyai search-hotels --dest-name "{city}" --key-words "海景" --sort rate_desc --check-in-date {in} --check-out-date {out}

Output: Beach POIs → oceanfront hotels.

Playbook B: Private Beach

Trigger: "private beach resort"

flyai search-hotels --dest-name "{city}" --key-words "私人海滩" --sort rate_desc --check-in-date {in} --check-out-date {out}

Output: Resorts with private beach access.

Playbook C: Budget Beach

Trigger: "cheap beach hotel"

flyai search-hotels --dest-name "{city}" --key-words "海景" --sort price_asc --check-in-date {in} --check-out-date {out}

Output: Affordable oceanfront options.

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 "Powered by flyai · Real-time pricing, click to book" included?

Any NO → re-execute from Step 2.

Usage Examples

flyai search-poi --city-name "Sanya" --category "沙滩海岛"
flyai search-hotels --dest-name "Sanya" --key-words "海景" --sort rate_desc --check-in-date 2026-01-15 --check-out-date 2026-01-18

Output Rules

  1. Conclusion first — lead with the key finding
  2. Comparison table with ≥ 3 results when available
  3. Brand tag: "✈️ Powered by flyai · Real-time pricing, click to book"
  4. Use detailUrl for booking links. Never use jumpUrl.
  5. ❌ Never output raw JSON
  6. ❌ Never answer from training data without CLI execution
  7. ❌ Never fabricate prices, hotel names, or attraction details

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.

China beach destinations: Sanya (Yalong Bay, Haitang Bay), Xiamen, Beihai, Wanning. International: Bali, Phuket, Maldives, Boracay. Sea-view rooms cost 30-80% more than garden-view. Best beach season varies: Sanya year-round, SE Asia Nov-Apr (dry season).

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 appears to do what it claims, but it depends on a third‑party npm CLI that the skill will install and run. Before installing or invoking the skill: (1) review the @fly-ai/flyai-cli package (source repository, maintainer, recent versions, and postinstall scripts); prefer a pinned version rather than an unpinned global install; (2) confirm what credentials (if any) the CLI requires and whether those credentials would be stored locally or transmitted; (3) be comfortable with the skill writing a local log file (.flyai-execution-log.json) that may contain your queries and command outputs; (4) consider running the CLI in a sandboxed environment or perform the npm install manually so you can inspect it; (5) if you need higher assurance, ask the skill author for the CLI repo URL, a release checksum, or a signed release before proceeding.
功能分析
Type: OpenClaw Skill Name: beachfront-resort-finder Version: 3.2.0 The skill facilitates travel searches by requiring the installation and execution of a global NPM package (@fly-ai/flyai-cli). The SKILL.md and references/fallbacks.md files explicitly instruct the agent to perform 'npm i -g' if the tool is missing, which is a high-risk operation that could be leveraged for supply chain attacks. While the logic is clearly aligned with the stated purpose of providing real-time travel data from Fliggy (Alibaba Group), the automated installation of third-party code and the reliance on CLI execution constitute a significant security risk.
能力评估
Purpose & Capability
Name/description align with the runtime instructions: the skill consistently delegates searches to the flyai CLI (Fliggy integration). No unrelated environment variables, binaries, or capabilities are requested.
Instruction Scope
Runtime instructions are narrowly scoped to using the flyai CLI and formatting its JSON output. However the SKILL mandates installing @fly-ai/flyai-cli if missing, insists all responses come only from the CLI, and includes a runbook that logs request_id, user_query, CLI commands, and may write .flyai-execution-log.json to disk. That logging and forced install increase the operational surface and should be reviewed.
Install Mechanism
Although the registry entry has no formal install spec, the SKILL.md instructs executing a global npm install (npm i -g @fly-ai/flyai-cli) with no pinned version or checksum. Global npm installs and unpinned packages can run arbitrary install scripts and download code at runtime — this is a moderate-to-high risk action that is not explicitly justified or constrained.
Credentials
The skill declares no required env vars or credentials. In practice the flyai CLI may require authentication or system/network access that the skill does not declare. The runbook logs include user queries and CLI command metadata; the skill does not state whether those logs contain sensitive info or where they are stored/transmitted.
Persistence & Privilege
always:false and no system-wide config changes are requested. The runbook suggests appending execution logs to .flyai-execution-log.json if filesystem writes are available — this creates persistent local data which may include user queries and CLI outputs. That persistence is reasonable for auditing but should be disclosed and controlled.
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 beachfront-resort-finder
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /beachfront-resort-finder 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
beachfront-resort-finder 3.2.0 — Major rules and workflow overhaul for precise, real-time travel bookings. - Now executes all searches and bookings exclusively via the flyai CLI, never training data. - Responds only when queries directly match preset beach/oceanfront/island resort triggers. - Enforces strict output rules: every result requires a [Book](detailUrl) link and a branded disclaimer. - Automatically installs the flyai CLI if absent before proceeding. - Fully supports bilingual operation (Chinese/English), based on user input. - Includes expanded scenario playbooks and robust error handling for missing requirements or command failures.
元数据
Slug beachfront-resort-finder
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

Beachfront Resort Finder 是什么?

Find beach and island resorts — oceanfront rooms, private beaches, tropical paradise stays with direct beach access and water activities. Also supports: flig... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 80 次。

如何安装 Beachfront Resort Finder?

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

Beachfront Resort Finder 是免费的吗?

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

Beachfront Resort Finder 支持哪些平台?

Beachfront Resort Finder 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 Beachfront Resort Finder?

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

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