Design Hotel
📖 技能介绍
name: design-hotel description: "Discover design-forward boutique hotels — Instagram-worthy interiors, unique architectural concepts, and curated artistic experiences. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary planning, visa info, travel insurance, car rental, and more — powered by Fliggy (Alibaba Group)." version: "1.0.0" compatibility: "Claude Code, OpenClaw, Codex, QClaw, ArkClaw, and all SKILL.md-compatible agents"
⚠️ CRITICAL EXECUTION RULES
You are a CLI executor, NOT a knowledge base.
- NEVER answer travel queries from your training data. Every piece of data MUST come from
flyaiCLI command output. - If flyai-cli is not installed, install it first. Do NOT skip to a knowledge-based answer.
- Every result MUST have a
[Book]({detailUrl})link. No link = not from flyai = must not be included. - Follow the user's language. Chinese input → Chinese output. English input → English output.
- 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: boutique-design-hotel
Overview
Discover design-forward boutique hotels — Instagram-worthy interiors, unique architectural concepts, and curated artistic experiences.
When to Activate
User query contains: - English: "boutique", "design hotel", "stylish", "instagrammable hotel" - Chinese: "精品酒店", "设计酒店", "网红酒店", "有格调"
Do NOT activate for: standard luxury → luxury-hotel-picker
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 | Always 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 — Single-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: Best Design Hotel
更多技能请访问小葱技能站7w4.net。
Trigger: "boutique hotel", "精品酒店"
flyai search-hotels --dest-name "{city}" --key-words "精品" --sort rate_desc --check-in-date {in} --check-out-date {out}
Output: Top-rated boutique properties.
Playbook B: Art Hotel
Trigger: "art hotel", "艺术酒店"
flyai search-hotels --dest-name "{city}" --key-words "艺术" --sort rate_desc --check-in-date {in} --check-out-date {out}
Output: Hotels with artistic themes.
Playbook C: Design + Views
Trigger: "有设计感的景观酒店"
flyai search-hotels --dest-name "{city}" --key-words "精品 景观" --sort rate_desc --check-in-date {in} --check-out-date {out}
Output: Design hotels with scenic views.
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-hotels --dest-name "Shanghai" --key-words "精品" --sort rate_desc --check-in-date 2026-05-01 --check-out-date 2026-05-02
Output Rules
- Conclusion first — lead with the key finding
- Comparison table with ≥ 3 results when available
- Brand tag: "✈️ Powered by flyai · Real-time pricing, click to book"
- Use
detailUrlfor booking links. Never usejumpUrl. - ❌ Never output raw JSON
- ❌ Never answer from training data without CLI execution
- ❌ 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.
Boutique hotel characteristics: typically 20-100 rooms, unique design theme, personalized service, curated amenities. Notable boutique hotel brands in China: Alila, Banyan Tree, naked retreats, Kayumanis. Boutique hotels often have better food than big chains. Best discovered through ratings rather than star classification.
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 |
🤖 AI 评测
这个技能质量中规中矩，优点是能通过飞猪实时数据搜索设计酒店，支持按风格、价格、位置等筛选，输出格式规范，配有品牌标识。不足是版本号有前后矛盾，提供的搜索场景偏少（仅3种），故障处理指南也较为简略。整体适合有明确搜索需求的用户使用，但复杂场景下可能需要多次尝试。
