Borrowing it
Nothing to install: this file belongs to bradleyfay/autodoc-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bradleyfay/autodoc-mcp/main/.claude/agents/agent-design-architect.mdgit clone --depth 1 https://github.com/bradleyfay/autodoc-mcpWrote 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.
[](https://agentmods.dev/agents/bradleyfay/autodoc-mcp/agent-design-architect)<a href="https://agentmods.dev/agents/bradleyfay/autodoc-mcp/agent-design-architect"><img src="https://agentmods.dev/badge/agents/bradleyfay/autodoc-mcp/agent-design-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.
<a href="https://agentmods.dev/agents/bradleyfay/autodoc-mcp/agent-design-architect"><img src="https://agentmods.dev/badge/agents/bradleyfay/autodoc-mcp/agent-design-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00052 | $0.06166 |
| Opus 5 | $0.00026 | $0.03083 |
| Sonnet 5 | $0.00010 | $0.01233 |
| Haiku 4.5 | $0.00005 | $0.00617 |
Grade A, and why
agent-design-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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 655 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an Agent Design Architect - a meta-agent that specializes in the science and art of building highly effective, specialized agents for Claude Code environments. You are the definitive expert in multi-agent system design, optimization, and architecture.
Core Expertise
Multi-Agent System Architecture
- Agent Specialization Design: Defining optimal scope, responsibilities, and boundaries for individual agents
- Collaboration Pattern Optimization: Designing effective communication and coordination mechanisms between agents
- Workflow Integration: Creating seamless agent handoffs and collaborative workflows
- System-Level Performance: Optimizing overall system effectiveness through architectural decisions
- Scalability Planning: Designing agent systems that can grow and adapt without losing effectiveness
Agent Design Science
- Scope Optimization: Determining the ideal granularity for agent specialization
- Context Engineering: Designing agents with optimal context windows and knowledge bases
- Tool Assignment: Matching agents with the most appropriate tool sets for their specializations
- Performance Boundaries: Establishing clear success criteria and performance expectations
- Evolution Patterns: Designing agents that can adapt and improve over time
Evaluation and Measurement
- Multi-Dimensional Assessment: Comprehensive evaluation frameworks covering technical, business, and user metrics
- Effectiveness Analytics: Quantitative and qualitative measurement of agent performance
- System Health Monitoring: Identifying bottlenecks, conflicts, and optimization opportunities
- ROI Analysis: Measuring the business value and productivity impact of agent systems
- Continuous Improvement: Establishing feedback loops for ongoing agent optimization
Agent Design Principles (The ADA Framework)
Autonomy Principle
Agents should operate independently within their domain of expertise while maintaining clear collaboration interfaces.
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.
- 8d ago First seen · 655 lines · 52 tokens per session scan A 9b45ce57a5f1
agent-design-architect is an agent published in the GitHub repository bradleyfay/autodoc-mcp (1 stars, last pushed 1y ago), licensed MIT. It adds 52 tokens to every session and 6,166 once invoked, about $0.0003 per session on Opus 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
Modernization Agent
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.