rollinggo-hotel-booking

rollinggo-hotel-booking is a skill for Claude Code, Codex from RollingGo-AI/rollinggo-hotel-skill-cn. It costs 209 tokens per session (3,653 once invoked), scanned A, original, MIT.

A hotel search and booking assistant that finds accommodation by location, price, rating, room details, and travel preferences.

In plain words
What is it for?
Use it to search by city, landmark, airport, or station; filter by budget and amenities; check current room prices and availability; compare hotels; and complete a booking.
Why use it?
It reduces the work of comparing hotels and guides the user from finding suitable rooms through confirming a reservation.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to search by city, landmark, airport, or station; filter by budget and amenities; check current room prices and availability; compare hotels; and complete a booking.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add RollingGo-AI/rollinggo-hotel-skill-cn --skill rollinggo-hotel-booking
Clone the repo
git clone --depth 1 https://github.com/RollingGo-AI/rollinggo-hotel-skill-cn

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for rollinggo-hotel-booking

README.md
[![agentmods](https://agentmods.dev/badge/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking/github.svg)](https://agentmods.dev/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking)
Your own site
<a href="https://agentmods.dev/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking"><img src="https://agentmods.dev/badge/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for rollinggo-hotel-booking

Your own site · 80×15
<a href="https://agentmods.dev/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking"><img src="https://agentmods.dev/badge/skills/rollinggo-ai/rollinggo-hotel-skill-cn/rollinggo-hotel-booking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 209 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,653 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00209 $0.03653
Opus 5.5 $0.00084 $0.01461
Sonnet 5.5 $0.00042 $0.00731
Haiku 4.5 $0.00021 $0.00365

Measured 23d ago against content hash da533e75785f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rollinggo-hotel-booking scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 23d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/install.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

"anyBins": ["rgh", "npx", "node", "python", "python3", "curl", "wget"]
skills/rollinggo-hotel-booking/SKILL.md · 242 lines

How it starts

The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RollingGo 酒店搜索与预订 Skill

安装

如果本地环境未安装 rgh 命令,可以通过以下三种方式之一进行安装:

方式一:通过 npm 安装(推荐) 如果环境中有 Node.js 和 npm,请执行:

npm install -g @rollinggo/hotel@latest

方式二:通过 Python 自动脚本安装(免 Node.js 环境) 如果环境中有 Python,可以直接在 Skill 目录下运行我们提供的自动化安装脚本,它将自动检测您的操作系统并下载最新版的 rgh 独立二进制程序到本地 bin 文件夹:

python scripts/install.py

方式三:手动下载独立可执行文件(免 Node/Python 环境) 如果环境中既没有 npm 也没有 Python,请从以下 Release 页面下载对应的单文件程序(Windows: rgh-win.exe / rgh-windows.exe, Mac: rgh-macos, Linux: rgh-linux): https://github.com/RollingGo-AI/oauth-hotel-cli/releases/latest

下载后将其重命名为 rgh(Windows下为 rgh.exe),赋予执行权限,并放入系统环境变量 PATH 或技能目录的 bin 文件夹中。

⚠️ 自动升级拦截原则: CLI 工具内置了版本检测。当你在执行任何 rgh 命令时,如果控制台输出中出现 📦 有新版本可用! 或 运行以下命令更新 的提示,你必须立即暂停当前操作,先使用上述三种方式之一将工具升级到最新版,升级成功后再重新执行刚才中断的命令。 这样可避免由于服务端接口过期而导致的预订失败。


⚠️ 输出规范:

  1. 严禁向用户展示任何技术细节,包括但不限于:工具名(如 search-hotels、hotel-detail)、JSON 字段名(如 hotelId、ratePlanId、referenceNo)、命令行内容、技术参数。
  2. 只展示用户关心的信息:酒店名称、星级、价格、距离、核心设施、标签、预订链接。
  3. 结果必须格式化展示,每家酒店独占一个卡片,关键信息换行分隔,禁止堆砌在一行。
  4. 价格说明:搜索结果中的价格为参考展示价,实际下单价以价格确认为准,展示时注明"参考价"。
  5. 登录授权:用户通过 Agent 对话时看不到终端输出,执行 rgh login 后必须从输出中提取授权链接并回复给用户,不要展示二维码文本。

When to Use

用户涉及酒店住宿相关的任何意图时均应触发本 Skill,包括但不限于以下场景:

搜索与发现:

  • 按地点找酒店:"帮我找北京三里屯附近酒店"、"三亚有什么好酒店"、"西湖旁边住宿推荐"
  • 按条件筛选:"五星酒店"、"带泳池的酒店"、"含早餐的住宿"、"亲子酒店"、"宠物友好酒店"
  • 按预算筛选:"500块以内的酒店"、"经济实惠的住宿"、"豪华酒店推荐"
  • 按品牌筛选:"希尔顿"、"万豪"、"亚朵"、"全季"

查询与对比:

  • 查房价:"杭州酒店多少钱一晚"、"这个酒店什么价格"
  • 看房型:"有什么房型"、"大床房有没有"、"家庭房推荐"
  • 比较住宿:"帮我对比一下这两家酒店"、"哪个更划算"
  • 了解设施:"有没有泳池"、"离地铁站多远"、"停车方便吗"

预订与订单:

  • 预订酒店:"帮我订这家酒店"、"我要下单"、"预订一间房"
  • 查询订单:"我的订单"、"之前订的酒店"、"订单状态"
  • 取消与退订:"帮我取消这个订单"、"不想住了退订"、"退房退款"、"查一下取消要扣多少钱"

触发词覆盖: 找酒店、订酒店、搜酒店、酒店推荐、酒店查询、附近酒店、五星酒店、民宿、度假村、查房价、看房型、入住、住哪、住宿、出差住宿、旅游住宿、亲子酒店、带泳池的酒店、含早餐酒店、商务酒店、情侣酒店、温泉酒店、海景房、江景房、取消订单、退订酒店、退房退款、取消预订。

When NOT to Use

  • 用户询问机票、火车票、租车、景点门票等非住宿类旅行需求
  • 用户只是闲聊旅游目的地,没有明确住宿意图
  • 用户已明确表示"不用订"、"只是问问"

安全门控

⚠️ 酒店预订与退订属于真实资金与消费操作:

  1. 两步确认:展示房型和价格后,须等用户明确选择方可锁价与下单。
  2. 信息完整性:下单前须确认入住人姓名拼音与邮箱。
  3. 锁价时效:锁定的价格凭证(referenceNo)有效期约 15-30 分钟,超时须重新锁价。
  4. 订单取消高危安全门控(两步确认):取消订单属于不可逆操作,绝对禁止未经二次确认直接取消!必须先执行 pre-cancel 查询取消手续费/违约金与凭证(confirmId 凭证 10 分钟有效),向用户明确汇报扣费金额与预计退款,取得用户明确肯定答复后,方可调用 confirm-cancel。

Read the full file on GitHub · 242 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 23d ago Changed · +1 lines da533e75785f
  2. 24d ago Changed 1a72b4256997
  3. 1mo ago Changed · +36 lines · +27 tokens per session e3fe9875d033
  4. 2mo ago First seen · 205 lines · 182 tokens per session scan A dff1ae1c7ad2

Subscribe to this mod's changes

rollinggo-hotel-booking is a skill published in the GitHub repository RollingGo-AI/rollinggo-hotel-skill-cn (167 stars, last pushed 24d ago), licensed MIT. It adds 209 tokens to every session and 3,653 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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