progressive disclosure를 구현하는 AI Agent를 위한 Framework-agnostic 기술 시스템 - 효율적으로 컨텍스트 관리를위한 강력한 패턴. 이 프로젝트는 독점적 인 AI 시스템 (클래드 스킬과 같은)의 성공적인 패턴을 추출하고 Pydantic AI와 같은 열린 프레임 워크에서 구현하는 방법을 보여줍니다.

키 기능

-Progressive Disclosure: Skills load Instruction and resources on-demand, context window constraints 제거

• Framework Agnostic: 특정 공급업체에 고정되지 않은 AI 프레임워크와 기술 작업
• Type Safe: 전체 Pydantic 모델 및 유형 힌트
• Extensible: 간단한 디렉토리 구조에 따라 새로운 기술을 추가하기 쉬운 -Production Ready: 종합적인 테스트 패턴과 예제 포함

프로그레시브는 무엇입니까?

Progressive disclosure는 AI 컨텍스트를 효율적으로 관리하는 기술입니다. 시스템 프롬프트로 가능한 모든 지시를 적재하는 대신, 기술은 3개의 수준에서 적재됩니다:

이 패턴은 컨텍스트 창을 압도하지 않고 잠재적으로 수백 개의 기술에 액세스 할 수 있습니다.

빠른 시작

# 필수품

• Python 3.11 이상
• UV - 빠른 파이썬 패키지 관리자

Custom Skill-Based Pydantic AI Agent

A framework-agnostic skill system for AI agents that implements progressive disclosure - a powerful pattern for managing context efficiently. This project demonstrates how to extract successful patterns from proprietary AI systems (like Claude Skills) and implement them in open frameworks like Pydantic AI.

Key Features

• Progressive Disclosure: Skills load instructions and resources on-demand, eliminating context window constraints
• Framework Agnostic: Skills work with any AI framework, not locked to a specific vendor
• Type Safe: Full Pydantic models and type hints throughout
• Extensible: Easy to add new skills by following a simple directory structure
• Production Ready: Includes comprehensive testing patterns and examples

What is Progressive Disclosure?

Progressive disclosure is a technique for managing AI context efficiently. Instead of loading all possible instructions into the system prompt, skills are loaded in three levels:

Level 1: Metadata (~100 tokens per skill)
    - Name and brief description in system prompt
    - Agent decides which skill might be relevant

Level 2: Full Instructions (loaded on-demand)
    - Complete SKILL.md with detailed instructions
    - Only loaded when agent chooses to use the skill

Level 3: Resources (loaded on-demand)
    - Reference files, scripts, and other resources
    - Only loaded when instructions reference them

This pattern allows an agent to have access to potentially hundreds of skills without overwhelming the context window.

Quick Start

Prerequisites

• Python 3.11 or higher
• UV - Fast Python package manager
• An API key for an LLM provider (OpenRouter, OpenAI, Anthropic, etc.)

Installation

1. Install UV (if not already installed):

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone the repository:

git clone https://github.com/coliam00/custom-agent-with-skills.git
cd custom-agent-with-skills

3. Sync dependencies:

uv sync

This creates a virtual environment and installs all dependencies automatically.

4. Configure environment variables:

Copy .env.example to .env and set your API key:

cp .env.example .env

Edit .env with your API credentials:

# LLM Provider Configuration
LLM_PROVIDER=openrouter
LLM_API_KEY=sk-or-v1-your-api-key-here
LLM_MODEL=anthropic/claude-haiku-4.5
LLM_BASE_URL=https://openrouter.ai/api/v1

Running the Agent

Start the CLI agent:

uv run python -m src.cli

You’ll see a Rich-based interface where you can interact with the agent. Try asking:

• “What’s the weather in New York?”
• “Find papers about machine learning”
• “What can I make with chicken and garlic?”
• “What time is it in Tokyo?”

Included Skills

Weather Skill