AI Partner Chat

A Claude Skills project that makes AI your personalized conversation partner.

Project Introduction

AI Partner Chat provides a personalized, context-aware conversational experience by integrating user profiles, AI profiles, and vectorized personal notes. This skill enables the AI to remember and reference your previous thoughts, preferences, and knowledge base, creating a more coherent and personalized interaction experience.

Core Features

🎭 Dual Profile System

User Profile: Define your background, expertise, interests, and communication preferences
AI Profile: Customize the AI's role positioning, communication style, and interaction methods

📝 Intelligent Note Retrieval

Automatically index your Markdown notes
Intelligently retrieve relevant historical records based on the conversation content
Naturally reference your past thoughts and notes during conversations

💬 Personalized Conversation

Generate personalized responses based on your profiles and notes
Maintain conversational context coherence
Reference your ideas naturally like a friend, rather than mechanically "according to records"

Use Cases

When you need:

Personalized communication instead of generic replies
Context-aware responses where the AI remembers your background
AI to remember and reference your previous thoughts and notes
A continuous conversational experience rather than starting fresh every time

Installation and Usage

Install the Skill

Copy this project into the .claude/skills/ folder in your working directory:

<你的项目根目录>/
└── .claude/
    └── skills/
        └── ai-partner-chat/    # 本技能包
            ├── assets/
            ├── scripts/
            ├── SKILL.md
            └── README.md

Using Skills

In Claude Code, send the following command to activate this skill:

遵循 ai-partner-chat 对话

The AI agent will automatically:

Read skill configurations and instructions
Create necessary directory structures (notes/, config/, vector_db/, etc.)
Initialize according to your needs

Initialization Process

#### Method 1: Let the AI automatically create and configure

For first-time use, simply tell the AI:

我刚刚在 notes 里放入了对应的笔记，请根据笔记内容，进行向量化；并基于笔记内容，推测并更新 user-persona.md，以及最适合我的 ai-persona.md

The AI agent will:

Analyze the notes in the notes/ directory
Intelligently segment notes based on their format and create a vector database
Infer your background and preferences based on the note content
Automatically generate and update config/user-persona.md
Recommend and create config/ai-persona.md based on your characteristics

#### Method 2: Manual Persona Configuration

If you want to define the persona yourself:

The AI agent will automatically create persona files from templates into the config/ directory
You can manually edit these files to customize the persona
Then instruct the AI to perform vectorization

Start Conversation

After configuration, simply send the following each time you use it:

遵循 ai-partner-chat 对话

AI will:

Read your profile to understand your background
Retrieve relevant historical notes
Generate personalized, context-aware responses

Project Structure

Skill Package Structure (located at `.claude/skills/ai-partner-chat/`)

ai-partner-chat/
├── assets/              # 画像模板
│   ├── user-persona-template.md
│   └── ai-persona-template.md
├── scripts/             # 核心脚本
│   ├── chunk_schema.py
│   ├── vector_indexer.py
│   ├── vector_utils.py
│   └── requirements.txt
├── SKILL.md            # 技能详细文档（AI agent 会读取此文件）
└── README.md           # 本文件

User Data Directory (Located in the Project Root Directory)

The AI agent will create the following structure in your project root directory:

<项目根目录>/
├── notes/              # 你的笔记（由你或 AI agent 创建）
├── config/             # 画像配置（由 AI agent 创建）
│   ├── user-persona.md
│   └── ai-persona.md
├── vector_db/          # 向量数据库（由 AI agent 创建）
└── venv/               # Python 虚拟环境（由 AI agent 创建）

Important: User data and skill packages are separated for easy backup and migration.

Workflow

Load Profiles: Read user profiles and AI profiles to understand the interaction context
Retrieve Notes: Retrieve the most relevant notes from the vector database based on user queries
Build Context: Integrate profile information, relevant notes, and conversation history
Generate Response: Generate personalized and natural responses based on the context

Highlights

🤖 AI Agent Intelligent Chunking

The system analyzes the actual format of each note and dynamically generates the most suitable chunking strategy instead of using preset templates. This means your notes will be optimally processed regardless of their format.

🎯 Natural Citation

The AI naturally incorporates your past information as if recalling, without awkwardly saying "according to the records," seamlessly blending into the conversation.

📦 Data Independence

All your data (notes, profiles, vector databases) are stored in the project root directory, making it easy to back up, migrate, or share across different skills.

Best Practices

Profile Design

Be Specific and Clear: Vague profiles lead to generic replies
Include Examples: Show expected interaction patterns in the AI profile
Update Regularly: Continuously optimize profiles based on conversation quality

Note Management

Flexible Format: The system adapts to any note structure
Rich Content: In-depth notes bring better retrieval results
Timely Updates: Remember to add new notes to the index

Conversation Experience

Natural Citation: Cite notes only when truly relevant
Maintain Flow: Don’t let citations disrupt the natural rhythm of conversation
Focus on Quality: Prioritize meaningful connections over quantity

Maintenance and Updates

Add New Note

After placing the new note into the notes/ directory, inform the AI:

我刚刚在 notes 里添加了新笔记，请更新向量数据库

The AI agent will automatically analyze new notes and update the index.

Update Profile

You can directly edit the profile files in the config/ directory, or tell the AI:

请根据我最近的笔记内容，更新 user-persona.md 和 ai-persona.md

Rebuild Index

When the note structure undergoes significant changes, inform the AI:

请重新初始化向量数据库

AI agent will reanalyze all notes and rebuild the index.

Notes

First Run: When the AI agent creates the vector database for the first time, it will automatically download the embedding model (about 4.3GB), please be patient
Python Environment: The AI agent will automatically create a virtual environment and install the required dependencies
Data Storage: All data (notes, profiles, vector libraries) are stored in the project root directory, not in the skill package directory
Skill Location: Ensure the skill package is located in the .claude/skills/ai-partner-chat/ directory

More Information

For detailed technical documentation and usage instructions, please refer to the SKILL.md file.

Let AI become a conversation partner who truly understands you, not just a tool.

--- Tranlated By Open Ai Tx | Last indexed: 2025-12-18 ---

ai-partner-chat