Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/de-monkey-v/marketplaceWrote 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/de-monkey-v/marketplace/infra-architect)<a href="https://agentmods.dev/agents/de-monkey-v/marketplace/infra-architect"><img src="https://agentmods.dev/badge/agents/de-monkey-v/marketplace/infra-architect.svg" alt="Measured on agentmods" 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 | $0.00192 | $0.01114 |
| Opus 5 | $0.00096 | $0.00557 |
| Sonnet 5 | $0.00038 | $0.00223 |
| Haiku 4.5 | $0.00019 | $0.00111 |
Grade A, and why
infra-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 7d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 7d ago First seen · 184 lines · 192 tokens per session scan A b5504a41bbbc
infra-architect is an agent published in the GitHub repository de-monkey-v/marketplace (2 stars, last pushed 5mo ago), with no licence file. It adds 192 tokens to every session and 1,114 once invoked, about $0.0010 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
DevOps Expert
DevOps specialist following the infinity loop principle (Plan → Code → Build → Test → Release → Deploy → Operate → Monitor) with focus on automation, collaboration, and continuous improvement.
aws-cloudformation-devops-expert
Provides expert AWS DevOps engineering capabilities for CloudFormation templates, Infrastructure as Code (IaC), and AWS deployment automation. Manages nested stacks, cross-stack references, custom resources, and CI/CD pipeline integration. Use PROACTIVELY for CloudFormation template creation, IaC best practices, or…
coolify-deployment
Du bist ein Senior Deployment-Ingenieur, Experte fur Coolify-Deployments. Du konfigurierst Git-Integrationen, Build-Strategien, Umgebungsvariablen, Domains, SSL-Zertifikate und Preview-Deployments fur produktionsreife Anwendungen auf Coolify Self-Hosted-PaaS.
terraform-specialist
Write advanced Terraform modules, manage state files, and implement IaC best practices. Handles provider configurations, workspace management, and drift detection. Use PROACTIVELY for Terraform modules, state issues, or IaC automation.
mlops_engineer
MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up…
ai-devops-engineer
Expert in LLM serving infrastructure, GPU orchestration, AI cost optimization, and multi-agent system operations. Use for deploying AI systems to production, managing AI-specific CI/CD, and operating AI workloads at scale.