Capsule Pod Hotel

Find capsule hotels and pod-style accommodation — ultra-affordable, tech-forward, perfect for solo travelers who just need a clean bed. Also supports: flight...

Security Scan

The skill is internally consistent: it wraps a third‑party CLI (flyai-cli) to fetch real‑time capsule‑hotel data and does not request unrelated credentials, but it requires installing an external npm package and may persist an execution log file — verify the CLI source before installing.

Purpose & Capability

The SKILL.md consistently describes a wrapper around the flyai CLI to search for capsule/pod hotels; required actions (installing and invoking @fly-ai/flyai-cli) match the stated purpose. One minor note: the package/source/homepage for the skill and the CLI are not provided in the registry metadata, so trust in the third‑party CLI must be established separately.

Instruction Scope

Runtime instructions are tightly scoped to running flyai CLI commands and formatting the output. However, the skill mandates strict execution rules (never answer from training data, always produce [Book]({detailUrl}) links, re-run if links missing) which can force repeated CLI calls and retries. The runbook also includes an optional file write (append a JSON log to .flyai-execution-log.json) which is outside the declared config paths and could persist user queries/requests to disk.

Install Mechanism

There is no install spec for the skill itself (instruction-only), which is low risk. The skill requires installing a global npm package (npm i -g @fly-ai/flyai-cli) when the CLI is missing — reasonable for a CLI wrapper, but installing a global package requires trusting that npm package. No URLs or extract/install of arbitrary archives are present.

Credentials

The skill does not request environment variables, credentials, or config paths. All required inputs are user-provided parameters for the CLI. This is proportionate to the stated functionality.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. It does, however, include instructions to append an execution log to .flyai-execution-log.json if filesystem writes are available — this creates persisted logs in the agent's working directory (request/queries and command status), which the registry did not declare as a config path.

Assessment

This skill appears to do what it claims (it wraps a flyai CLI to find capsule hotels) and does not ask for credentials. Before installing or letting an agent run it autonomously: 1) verify the provenance of @fly-ai/flyai-cli on npm (publisher, repository, reviews) because the skill requires installing that package globally; 2) be aware the skill may append execution logs to .flyai-execution-log.json in the working directory (which can contain raw user queries and command outputs) — if you don’t want persisted logs, run the agent in an isolated environment or disable/inspect that step; 3) note the skill enforces re-running the CLI until booking links appear, which can cause repeated network calls — consider running the CLI manually once to confirm results and behavior. If you need higher assurance, request the CLI source (GitHub repo) or run the CLI in a sandbox before granting the skill autonomous invocation.

⚠️ 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.

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Skill: capsule-pod-hotel

Overview

Find capsule hotels and pod-style accommodation — ultra-affordable, tech-forward, perfect for solo travelers who just need a clean bed.

When to Activate

User query contains:

• English: "capsule", "pod hotel", "sleep box", "cheap bed"
• Chinese: "胶囊酒店", "太空舱", "床位"

Do NOT activate for: regular budget → budget-hotel-finder

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 price_asc
--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: Capsule Hotel

Trigger: "capsule hotel", "胶囊酒店"

flyai search-hotel --dest-name "{city}" --key-words "太空舱" --sort price_asc --check-in-date {in} --check-out-date {out}

Output: Pod hotels, cheapest first.

Playbook B: Near Station/Airport

Trigger: "capsule near train station"

flyai search-hotel --dest-name "{city}" --key-words "太空舱 火车站" --sort distance_asc --check-in-date {in} --check-out-date {out}

Output: Capsule hotels near transit hubs.

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-hotel --dest-name "Shanghai" --key-words "太空舱" --sort price_asc --check-in-date 2026-05-01 --check-out-date 2026-05-02

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.

Capsule hotels: typically ¥50-150/night. Private pod with USB charging, light, sometimes TV. Shared bathroom/shower. Popular near train stations and airports. Japan originated the concept but China has many modern versions. Not suitable for couples or families. Luggage storage usually available.

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