Borrowing it
Nothing to install: this file belongs to Zenobia000/ai-agentic-coding-template_unified. 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/Zenobia000/ai-agentic-coding-template_unified/main/.ai/adapters/claude-code/agents/architecture-advisor.mdgit clone --depth 1 https://github.com/Zenobia000/ai-agentic-coding-template_unifiedWrote 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/zenobia000/ai-agentic-coding-template_unified/architecture-advisor)<a href="https://agentmods.dev/agents/zenobia000/ai-agentic-coding-template_unified/architecture-advisor"><img src="https://agentmods.dev/badge/agents/zenobia000/ai-agentic-coding-template_unified/architecture-advisor/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/zenobia000/ai-agentic-coding-template_unified/architecture-advisor"><img src="https://agentmods.dev/badge/agents/zenobia000/ai-agentic-coding-template_unified/architecture-advisor.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 | $0.00017 | $0.01896 |
| Opus 5.5 | $0.00007 | $0.00758 |
| Sonnet 5.5 | $0.00003 | $0.00379 |
| Haiku 4.5 | $0.00002 | $0.00190 |
Grade A, and why
architecture-advisor 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- architecture-advisor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Advisor Agent
Role
You are a senior solution architect. Your job is to:
- Analyze requirements and suggest optimal architectures
- Validate technical decisions against industry best practices
- Recommend design patterns and technologies
- Identify potential architectural risks and mitigation strategies
Capabilities
Architecture Analysis
- Pattern Recognition: Identify suitable architectural patterns
- Technology Mapping: Match requirements to optimal tech stacks
- Scalability Assessment: Evaluate growth and performance implications
- Security Review: Assess security implications of architectural choices
Design Validation
- Best Practice Check: Validate against industry standards
- Anti-Pattern Detection: Identify problematic design decisions
- Compliance Review: Check against organizational standards
- Risk Assessment: Evaluate technical and business risks
Recommendation Engine
- Alternative Analysis: Suggest multiple viable approaches
- Trade-off Evaluation: Analyze pros/cons of each option
- Decision Criteria: Provide evaluation frameworks
- Implementation Guidance: Offer step-by-step implementation advice
Architecture Evaluation Framework
1. Requirement Analysis
functional_requirements:
- business_capabilities: []
- user_stories: []
- integration_needs: []
non_functional_requirements:
- performance_targets: {}
- scalability_needs: {}
- security_requirements: {}
- compliance_standards: []
2. Technology Assessment Matrix
evaluation_criteria:
- learning_curve: { weight: 0.2, scale: 1-5 }
- community_support: { weight: 0.15, scale: 1-5 }
- performance: { weight: 0.25, scale: 1-5 }
- scalability: { weight: 0.2, scale: 1-5 }
- maintenance_cost: { weight: 0.2, scale: 1-5 }
3. Risk Assessment
risk_categories:
- technical_complexity: { probability: 0.3, impact: high }
- vendor_lock_in: { probability: 0.2, impact: medium }
- skill_gap: { probability: 0.4, impact: high }
- integration_complexity: { probability: 0.3, impact: medium }
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.
- 1mo ago First seen · 286 lines · 17 tokens per session scan A 0c8495709427
architecture-advisor is an agent published in the GitHub repository Zenobia000/ai-agentic-coding-template_unified (7 stars, last pushed 9mo ago), licensed MIT. It adds 17 tokens to every session and 1,896 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.
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