What is the benefit of the three-part prompt structure?

It separates identity, logic, and formatting, which makes prompts significantly easier to debug, maintain, and reuse across different agent versions.

Does this skill help with token optimization?

Yes, the framework encourages using concise action verbs and structured lists, and includes specific guidance on creating token-efficient prompts.

How do I handle agent errors or uncertainty?

The skill provides patterns for 'Background' instructions that tell the agent to acknowledge uncertainty and ask for clarification when a request is ambiguous.

Can I integrate real-time data into these prompts?

Yes, the SystemPromptGenerator supports BaseDynamicContextProvider to inject dynamic information like RAG search results or API data into the final prompt.

Is this skill specific to a certain programming language?

While the examples use Python with the Atomic Agents library, the prompt engineering principles apply to any LLM-based agent development.

Atomic Prompt Design

Name: Atomic Prompt Design
Author: BrainBlend-AI

byBrainBlend-AI

•

5,524

数据科学与机器学习

Generates structured, effective system prompts for AI agents using a modular architecture of background identity, processing steps, and output instructions.

关于

Atomic Prompt Design provides a standardized framework for crafting high-quality system prompts within the Atomic Agents ecosystem. By utilizing the SystemPromptGenerator, developers can separate agent identity (WHO), processing logic (HOW), and output constraints (WHAT) into distinct, manageable components. This modular approach allows for more reliable and predictable agent behavior, making it easier to iterate on complex workflows, integrate dynamic context via providers, and maintain consistency across multi-agent systems.

主要功能

Dynamic context provider integration for RAG and external data sources
Built-in best practices for constraint setting and edge-case handling
Modular prompt generation with Background, Steps, and Output sections
Pre-defined patterns for analysts, researchers, and software engineers
Action-oriented workflow design using specific step-by-step logic
5,524 GitHub stars

使用场景

Building specialized RAG agents with custom document processing workflows
Refining agent reliability through explicit output formatting and quality constraints
Standardizing prompt structures across complex multi-agent architectures

关于

主要功能

Dynamic context provider integration for RAG and external data sources
Built-in best practices for constraint setting and edge-case handling
Modular prompt generation with Background, Steps, and Output sections
Pre-defined patterns for analysts, researchers, and software engineers
Action-oriented workflow design using specific step-by-step logic
5,524 GitHub stars

使用场景

Building specialized RAG agents with custom document processing workflows
Refining agent reliability through explicit output formatting and quality constraints
Standardizing prompt structures across complex multi-agent architectures