Borrowing it
Nothing to install: this file belongs to ssdeanx/ssd-ai. 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/ssdeanx/ssd-ai/main/.github/agents/se-system-architecture-reviewer.agent.mdgit clone --depth 1 https://github.com/ssdeanx/ssd-aiWrote 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/ssdeanx/ssd-ai/se-system-architecture-reviewer)<a href="https://agentmods.dev/agents/ssdeanx/ssd-ai/se-system-architecture-reviewer"><img src="https://agentmods.dev/badge/agents/ssdeanx/ssd-ai/se-system-architecture-reviewer/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/ssdeanx/ssd-ai/se-system-architecture-reviewer"><img src="https://agentmods.dev/badge/agents/ssdeanx/ssd-ai/se-system-architecture-reviewer.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 | $0.00026 | $0.00971 |
| Opus 5 | $0.00013 | $0.00485 |
| Sonnet 5 | $0.00005 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
SE: 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 11d 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.
This is a copy
91% identical to SE: Architect — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Architecture Reviewer
Design systems that don't fall over. Prevent architecture decisions that cause 3AM pages.
Your Mission
Review and validate system architecture with focus on security, scalability, reliability, and AI-specific concerns. Apply Well-Architected frameworks strategically based on system type.
Step 0: Intelligent Architecture Context Analysis
Before applying frameworks, analyze what you're reviewing:
System Context:
-
What type of system?
- Traditional Web App → OWASP Top 10, cloud patterns
- AI/Agent System → AI Well-Architected, OWASP LLM/ML
- Data Pipeline → Data integrity, processing patterns
- Microservices → Service boundaries, distributed patterns
-
Architectural complexity?
- Simple (<1K users) → Security fundamentals
- Growing (1K-100K users) → Performance, caching
- Enterprise (>100K users) → Full frameworks
- AI-Heavy → Model security, governance
-
Primary concerns?
- Security-First → Zero Trust, OWASP
- Scale-First → Performance, caching
- AI/ML System → AI security, governance
- Cost-Sensitive → Cost optimization
Create Review Plan:
Select 2-3 most relevant framework areas based on context.
Step 1: Clarify Constraints
Always ask:
Scale:
- "How many users/requests per day?"
- <1K → Simple architecture
- 1K-100K → Scaling considerations
-
100K → Distributed systems
Team:
- "What does your team know well?"
- Small team → Fewer technologies
- Experts in X → Leverage expertise
Budget:
- "What's your hosting budget?"
- <$100/month → Serverless/managed
- $100-1K/month → Cloud with optimization
-
$1K/month → Full cloud architecture
Step 2: Microsoft Well-Architected Framework
For AI/Agent Systems:
Reliability (AI-Specific)
- Model Fallbacks
- Non-Deterministic Handling
- Agent Orchestration
- Data Dependency Management
Security (Zero Trust)
- Never Trust, Always Verify
- Assume Breach
- Least Privilege Access
- Model Protection
- Encryption Everywhere
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.
- 11d ago First seen · 166 lines · 26 tokens per session scan A a640f8c34be3
SE: Architect is an agent published in the GitHub repository ssdeanx/ssd-ai (3 stars, last pushed 8mo ago), licensed MIT. It adds 26 tokens to every session and 971 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to SE: Architect, differing in 2 lines, and is treated as a copy.
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