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在 OpenClaw 中安装
/install chat-search
功能描述
Search and find relevant chat messages from Feishu or Telegram using semantic search with similarity scores and message source labels.
使用说明 (SKILL.md)
Chat Search Skill
飞书/Telegram 聊天记录语义搜索
功能
- 语义搜索聊天记录
- 支持中文分词
- 显示相似度分数
- 区分消息来源（飞书/Telegram）
- 使用 Qdrant 向量数据库
- 使用 FastEmbed 生成中文向量
触发方式
当用户说以下内容时自动触发：
- "搜索XXX" / "搜素XXX" / "查找XXX"
- "帮我搜一下XXX"
- "找一下关于XXX的对话"
依赖
- Qdrant 向量数据库 (localhost:6333)
- Python FastEmbed (BGE 中文模型)
- Node.js
使用示例
用户：帮我搜索 Docker 相关的对话
小T：→ 执行搜索
🔍 搜索 "Docker" 的结果：
1. 🐦 [feishu] 小T (2026-03-20 09:17)
Docker 安装成功了
相似度: 58.2%
安装
需要先安装依赖：
# Qdrant
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant
# Python FastEmbed
pip install fastembed
安全使用建议
This skill claims it can search your Feishu and Telegram chats but the instructions do not say how it will get those messages (no API tokens, connectors, or upload steps). Before installing or using it, ask the author: (1) how are chat messages ingested and indexed — do you need to provide exports or API credentials? (2) does embedding and search run entirely locally (Qdrant + FastEmbed) or will anything be sent to remote services? (3) what exact commands or code the agent will run when triggered? Also be cautious about giving any chat platform tokens or uploading private chat history until you confirm the data flow and where embeddings/search indexes are stored. The install commands shown (docker run qdrant, pip install fastembed) are standard, but because this is an instruction-only skill with no code reviewed, get more detail about runtime behavior and data access before proceeding.
功能分析
Type: OpenClaw Skill
Name: chat-search
Version: 1.0.1
The skill bundle contains only metadata and documentation (SKILL.md) for a semantic chat search tool using Qdrant and FastEmbed. No executable code was provided, and the instructions are consistent with the stated purpose of searching Feishu and Telegram history without any signs of malicious intent or prompt injection.
能力评估
Purpose & Capability
The skill's name/description say it searches Feishu and Telegram chat history, but the instructions do not declare or request any means of accessing those platforms (no API tokens, export/upload steps, or connectors). Searching remote chat systems normally requires credentials or a connector — their absence is an incoherence between claimed purpose and required inputs.
Instruction Scope
SKILL.md instructs use of a local Qdrant instance and FastEmbed for embeddings and lists triggers, but it does not describe how chat data is obtained, indexed, or whether the agent will request uploads or credentials. The instructions also tell the operator to run docker and pip commands, but are otherwise vague about the runtime data flows, which grants the agent broad unspecified discretion.
Install Mechanism
No formal install spec in the registry, but SKILL.md recommends running 'docker run qdrant/qdrant' and 'pip install fastembed' and requires Node.js. These are standard/traceable sources (Docker Hub, pip) and not an unusual install pattern for a vector-search skill.
Credentials
The registry metadata lists no required env vars or credentials, yet the skill's function (searching Feishu/Telegram) normally requires platform tokens or access to exported chat data. The omission is disproportionate: either the skill expects the agent to ask the user for credentials or uploads at runtime, or the author left out critical details.
Persistence & Privilege
always is false and there is no install-time code or persistent config declared in the registry. The skill does not request elevated or always-on privileges in the manifest.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install chat-search - 安装完成后，直接呼叫该 Skill 的名称或使用
/chat-search触发 - 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
v1.0.1
- Added detailed trigger phrases and activation instructions in the documentation.
- Expanded feature list to specify use of Qdrant and FastEmbed for vector search.
- Added installation instructions for required dependencies (Qdrant, FastEmbed).
- Simplified and removed three implementation and metadata files (_meta.json, chat-search.js, index.js).
v1.0.0
Initial release of chat-search skill.
- Enables semantic search of chat history from Feishu or Telegram.
- Supports Chinese word segmentation.
- Displays similarity scores for search results.
- Differentiates between message sources (Feishu/Telegram).
- Requires Qdrant vector database and Python FastEmbed (BGE Chinese model).
元数据
常见问题
Chat Search 是什么？
Search and find relevant chat messages from Feishu or Telegram using semantic search with similarity scores and message source labels. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 126 次。
如何安装 Chat Search？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install chat-search」即可一键安装，无需额外配置。
Chat Search 是免费的吗？
是的，Chat Search 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Chat Search 支持哪些平台？
Chat Search 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 Chat Search？
由 fhqoopp（@fhqoopp）开发并维护，当前版本 v1.0.1。
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