food-tour

⚠️ 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 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: food-tour

Overview

Plan culinary travel experiences — local food tours, Michelin restaurants, street food crawls, cooking classes, food markets, and regional specialty tasting routes.

When to Activate

User query contains:

Do NOT activate for: night market → night-market

Prerequisites

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

Parameters

| Parameter | Required | Description | |-----------|----------|-------------| | --query | Yes | Natural language query string |

Core Workflow — Multi-command orchestration

Step 0: Environment Check (mandatory, never skip)

flyai --version

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: Food Tour

Trigger: "food tour in {city}"

flyai search-poi --city-name "{city}" --category "市集"
flyai keyword-search --query "美食 {city}"

Output: Comprehensive food exploration.

Playbook B: Street Food

Trigger: "street food {city}"

flyai search-poi --city-name "{city}" --keyword "小吃街"

Output: Street food hotspots.

Playbook C: Cooking Class

Trigger: "cooking class {city}"

flyai keyword-search --query "烹饪课程 {city}"

Output: Cooking class experiences.

Playbook D: Fine Dining

Trigger: "Michelin {city}"

flyai keyword-search --query "米其林餐厅 {city}"

Output: Top-rated restaurants.

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)

Any NO → re-execute from Step 2.

Usage Examples

flyai search-poi --city-name "Chengdu" --category "市集"
flyai keyword-search --query "美食 成都"

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's food capitals: Chengdu/Chongqing (Sichuan spice), Guangzhou (Cantonese dim sum), Xi'an (Muslim Quarter), Shanghai (xiaolongbao), Lanzhou (hand-pulled noodles), Changsha (Hunan spice). International: Bangkok (street food capital), Tokyo (most Michelin stars worldwide), Istanbul, Mexico City. Food tour tip: go hungry, share dishes, eat where locals eat (not tourist zones).

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 |

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