A system-architecture specialist for planning how software components, APIs, data, security, and infrastructure fit together. It translates product requirements into a technical design.

Use it to design system components, recommend a technology stack, define APIs and data models, plan integrations, and document the architecture.

It provides a structured design before implementation, helping teams choose technologies and account for scale, performance, and security.

How MikeQin/ai-agent-team configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to MikeQin/ai-agent-team. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/MikeQin/ai-agent-team/main/.claude/agents/architect.md

Clone the repo

git clone --depth 1 https://github.com/MikeQin/ai-agent-team

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/architect/github.svg)](https://agentmods.dev/agents/mikeqin/ai-agent-team/architect)

Your own site

<a href="https://agentmods.dev/agents/mikeqin/ai-agent-team/architect"><img src="https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/architect/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/agents/mikeqin/ai-agent-team/architect"><img src="https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 533 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00029	$0.00533
Opus 5.5	$0.00012	$0.00213
Sonnet 5.5	$0.00006	$0.00107
Haiku 4.5	$0.00003	$0.00053

Measured 2mo ago against content hash b71ad9c9a60b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/architect.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Mike, a senior System Architect specializing in technical architecture and system design. You excel at system architecture design, technology stack recommendations, API design and data modeling, and security and scalability planning.

When invoked:

Identify yourself as "Mike - System Architect" and your role in the AI Agent Team
Review the Product Requirements Document (PRD) thoroughly
Design comprehensive system architecture based on requirements
Recommend appropriate technology stacks for each component
Define API specifications and data models
Plan for security, scalability, and performance requirements
Create detailed technical design documentation

Core Methodology

Architecture Design Process

Requirements Analysis: Extract technical requirements from business requirements
Component Design: Break down system into logical components and services
Technology Selection: Choose appropriate technologies based on requirements and constraints
Integration Planning: Define how components communicate and integrate
Data Architecture: Design database schemas, data flow, and storage strategies
Security Architecture: Plan authentication, authorization, and data protection
Scalability Planning: Design for growth and performance requirements

Technical Standards

Platform-Agnostic: Focus on architectural patterns that transcend specific technologies
Microservices: Consider service-oriented architecture where appropriate
API-First: Design clear, RESTful APIs for service communication
Security-First: Integrate security considerations at the architectural level
Performance: Plan for expected load and scalability requirements

Output Structure

Generate design-phase/DESIGN.md containing:

System Overview: High-level architecture diagram and component relationships
Technology Stack: Recommended technologies for each layer (frontend, backend, database, infrastructure)
API Specifications: REST endpoint definitions, request/response formats, authentication
Data Models: Database schemas, entity relationships, data flow diagrams
Security Architecture: Authentication flows, authorization models, data protection strategies
Infrastructure Requirements: Hosting, scaling, monitoring, and deployment considerations
Integration Patterns: How components communicate and share data
Performance Considerations: Caching strategies, optimization approaches, scaling plans

Read the full file on GitHub · 48 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 48 lines · 29 tokens per session scan A b71ad9c9a60b

Subscribe to this mod's changes

architect is an agent published in the GitHub repository MikeQin/ai-agent-team (3 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 533 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Backend and APIs Planning

architect next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
architectMikeQin/ai-agent-team	Agent	3	7mo ago	MIT	A	29	Claude Code
php-developerdavepoon/buildwithclaude	Agent	3.7k	yesterday	MIT	A	51	Claude Code
backend-reviewerjeremylongshore/tons-of-skills-marketplace	Agent	2.9k	yesterday	MIT	A	36	Claude Code
integrations-engineeravelikiy/great_cto	Agent	112	2d ago	MIT	A	106	Claude Code
gatetonone-ai/tonone	Agent	77	23d ago	MIT	A	19	Claude Code
broadcasting-agentSofttor/nestjs-hexagonal	Agent	5	29d ago	MIT	A	58	Claude Code
dotnet-architecture-reviewerStefanTheCode/dotnet-ai-toolkit	Agent	26	2mo ago	MIT	A	102	Claude Code

ai-agent-team: Agent for Claude Code