Does this skill provide actual code implementation?

Yes, it provides boilerplate code for core primitives like Message and State, as well as implementation templates for the agent loop using Pydantic and Dataclasses.

What kind of output artifacts are created?

It produces a structured directory containing architecture documentation, type definitions, interface specs, Mermaid diagrams, and a multi-week implementation roadmap.

What is the main purpose of Architecture Synthesis?

It helps developers design and specify the structural components, data types, and execution logic required to build a custom AI agent framework.

Can it generate visual diagrams?

Yes, it generates Mermaid-based architecture diagrams to visualize the interaction between the Core, Protocol, Execution, and Integration layers.

Architecture Synthesis

Name: Architecture Synthesis
Author: Dowwie

byDowwie

•

Data Science & ML

Generates comprehensive reference architecture specifications and implementation roadmaps for custom AI agent frameworks.

About

Architecture Synthesis is a specialized skill designed for developers building the next generation of AI agent systems. It streamlines the transition from research to implementation by generating detailed architectural specifications, including immutable core primitives (Message, State, Result), standardized interface protocols for LLMs and tools, and robust execution loop templates. By synthesizing industry best practices and common patterns, it provides a structured foundation for building modular, scalable, and predictable agentic frameworks complete with Mermaid diagrams and phased development roadmaps.

Key Features

Phased implementation roadmaps from core setup to production readiness
Standardized interface protocols for LLM, Tool, and Memory components
Definition of immutable core primitives like Message and AgentState types
Automated generation of Mermaid architecture and flow diagrams
Execution loop design with algorithm pseudocode and Python templates
1 GitHub stars

Use Cases

Specifying strict interface protocols to ensure modularity between LLM providers and tools
Designing a new custom agent framework based on comparative industry analysis
Creating production-grade execution loops with built-in state management and step limits

About

Key Features

Phased implementation roadmaps from core setup to production readiness
Standardized interface protocols for LLM, Tool, and Memory components
Definition of immutable core primitives like Message and AgentState types
Automated generation of Mermaid architecture and flow diagrams
Execution loop design with algorithm pseudocode and Python templates
1 GitHub stars

Use Cases

Specifying strict interface protocols to ensure modularity between LLM providers and tools
Designing a new custom agent framework based on comparative industry analysis
Creating production-grade execution loops with built-in state management and step limits