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fengjianli0721

HANHANLI

作者 FENGJIANLI0721 · GitHub ↗ · v1.4.0
cross-platform ⚠ suspicious
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5
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1
版本数
在 OpenClaw 中安装
/install china-hotel-comparison
功能描述
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。
使用说明 (SKILL.md)

中国酒店比价

核心功能

  1. 多平台比价:携程、去哪儿、美团、飞猪、途牛
  2. 智能推荐:基于用户画像和历史学习
  3. 套餐分析:深度拆解服务计算真实优惠
  4. 多用户管理:家庭共享和协同决策
  5. 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

  1. 收集用户需求
  2. 多平台实时查询
  3. 智能过滤和排序
  4. 提供不少于5个选项
  5. 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

安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install china-hotel-comparison
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /china-hotel-comparison 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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
元数据
Slug china-hotel-comparison
版本 1.4.0
许可证
累计安装 5
当前安装数 5
历史版本数 1
常见问题

HANHANLI 是什么?

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。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 608 次。

如何安装 HANHANLI?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install china-hotel-comparison」即可一键安装,无需额外配置。

HANHANLI 是免费的吗?

是的,HANHANLI 完全免费(开源免费),可自由下载、安装和使用。

HANHANLI 支持哪些平台?

HANHANLI 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 HANHANLI?

由 FENGJIANLI0721(@fengjianli0721)开发并维护,当前版本 v1.4.0。

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