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HANHANLI
by
FENGJIANLI0721
· GitHub ↗
· v1.4.0
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Install in OpenClaw
/install china-hotel-comparison
Description
中国酒店比价 - 专门针对美团、去哪儿、携程、飞猪、途牛等中国本土平台的酒店搜索、价格比较、套餐分析和个性化推荐。
README (SKILL.md)
中国酒店比价
核心功能
- 多平台比价:携程、去哪儿、美团、飞猪、途牛
- 智能推荐:基于用户画像和历史学习
- 套餐分析:深度拆解服务计算真实优惠
- 多用户管理:家庭共享和协同决策
- 强制5选项:确保选择多样性
使用场景
- 中国境内酒店搜索和比价
- 家庭旅行协同规划
- 套餐价值深度分析
- 个性化推荐和筛选
工作流程
- 收集用户需求
- 多平台实时查询
- 智能过滤和排序
- 提供不少于5个选项
- 详细分析和建议
文件结构
- search-strategy.md:搜索策略
- price-calculation.md:价格计算
- package-value-analysis.md:套餐分析
- multi-user-management.md:多用户管理
- user-profiles.md:用户画像
- user-history-learning.md:历史学习
- personalized-recommendation.md:推荐引擎
- user-preferences.md:用户偏好
- scripts/hotel-search-example.sh:示例脚本
版本: 1.4.0
更新: 2026-02-25
特色: 多用户管理 + 套餐分析 + 历史学习
Usage Guidance
What to check before installing or enabling this skill:
1) Inspect scripts/hotel-search-example.sh and the two omitted files for network destinations and shell commands. Ensure they only call well-known public endpoints (platforms or official hotel sites) and don't POST data to unknown servers.
2) Search all files for hidden/unprintable/unicode control characters and for the DSML-like blocks. Confirm any web_fetch or tool-invoke calls only use trusted domains (no personal servers, IP addresses, pastebins, or URL shorteners).
3) Clarify persistence: the docs describe storing user and family data and device/session recognition but the skill metadata does not request filesystem or config-path access. If you plan to allow history/profile persistence, require transparency about where data will be stored, encryption, and deletion workflows.
4) Verify that no credentials (payment, platform API keys) are being collected or required. If the skill needs APIs for deeper integration, demand explicit declared env vars and a privacy/security rationale.
5) Run the skill in a sandboxed environment first (or with network monitoring) to confirm it only fetches public pages for price checks and does not exfiltrate data.
If you are not comfortable with hidden characters, undeclared filesystem usage, or unreviewed shell scripts, do not install or enable the skill until the maintainer provides cleared source (human-readable script contents), an explanation of the DSML/web_fetch invocations, and an explicit description of data persistence behavior and storage locations.
Capability Analysis
Type: OpenClaw Skill
Name: china-hotel-comparison
Version: 1.4.0
The skill bundle is largely benign, consisting of documentation and illustrative shell scripts that simulate hotel search and price calculation without performing actual external actions. However, the `multi-user-management.md` file contains a direct `<|DSML|invoke name="web_fetch">` instruction to the AI agent, instructing it to make an HTTP request to `https://www.shanghaidisneyresort.com/tickets/`. While this specific URL is relevant to the skill's stated purpose (e.g., for package value analysis), the explicit use of the `web_fetch` tool in a markdown file represents a risky capability. This capability, if exploited through prompt injection or dynamic URL generation, could lead to unauthorized data exfiltration or remote code execution, classifying the skill as suspicious due to this potential vulnerability.
Capability Assessment
Purpose & Capability
The skill's name/description (China hotel comparison) aligns with the files and algorithms included (search strategies, price calculation, recommendation engine). Requesting no credentials and no binaries is reasonable for an instruction-only skill that uses web fetching. However, the docs also describe device/environment recognition, session IDs, and local storage paths (/users/{user_id}/private/, /families/{family_id}/shared/) which imply filesystem access and persistent storage that are not declared in the metadata; this mismatch is noteworthy.
Instruction Scope
SKILL.md and the included docs instruct the agent to perform multi‑platform real-time queries and to collect and persist user profiles, history, and device/session signals. They reference reading implicit signals (dialog style, device/environment) and storing per-user and per-family data in filesystem-like paths. The package-value-analysis file also contains an embedded DSML/web_fetch invocation to a public Disney URL (expected), but the presence of unicode-control-chars and DSML tags suggests the runtime instructions may include hidden or non-obvious tooling directives. The skill does not declare that it will read or write local paths or access system identifiers—this is a scope mismatch and could enable broader data access than the metadata suggests.
Install Mechanism
There is no install spec; this is instruction-only plus documentation and one example script. No packages or external archives are downloaded by the skill metadata, which reduces installation risk. The only potential install/runtime risk is the included script file (scripts/hotel-search-example.sh) whose contents were not provided in the evaluation text; that file could perform network or system operations at runtime and should be inspected before use.
Credentials
The skill declares no required environment variables or credentials (appropriate for a read-only comparison tool). However, the documentation explicitly discusses storing personal data, family-shared data, and device/environment recognition—operations that could require filesystem access, device identifiers, or additional permissions. Because those capabilities are not reflected in requires.env or required config paths, the requested/declared environment is under-specified relative to the behavior described.
Persistence & Privilege
always:false and normal autonomous invocation are in place (no elevated persistent privilege declared). The documentation does describe persistent storage locations and a learning/feedback loop (history learning, profiles, shared family storage). That behavior implies the skill expects to persist user data but the metadata does not declare any config paths or permissions; this mismatch should be resolved. There is no explicit claim that the skill will modify other skills or system-wide settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install china-hotel-comparison - After installation, invoke the skill by name or use
/china-hotel-comparison - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.4.0
Initial release of china-hotel-comparison.
- Supports price comparison across major Chinese hotel platforms: Meituan, Qunar, Ctrip, Fliggy, Tuniu
- Provides real-time price comparisons, package analysis, and personalized recommendations
- Integrates intelligent recommendation algorithms based on price, rating, location, and service
- Includes user profiling, historical analysis, and multi-dimensional personalized filtering
- Offers detailed recommendation templates and cost calculation guides for efficient booking decisions
Metadata
Frequently Asked Questions
What is HANHANLI?
中国酒店比价 - 专门针对美团、去哪儿、携程、飞猪、途牛等中国本土平台的酒店搜索、价格比较、套餐分析和个性化推荐。 It is an AI Agent Skill for Claude Code / OpenClaw, with 608 downloads so far.
How do I install HANHANLI?
Run "/install china-hotel-comparison" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is HANHANLI free?
Yes, HANHANLI is completely free (open-source). You can download, install and use it at no cost.
Which platforms does HANHANLI support?
HANHANLI is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created HANHANLI?
It is built and maintained by FENGJIANLI0721 (@fengjianli0721); the current version is v1.4.0.
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