Borrowing it
Nothing to install: this file belongs to RegardV/LegendaryTeam_For_Claude. 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/RegardV/LegendaryTeam_For_Claude/main/.claude/agents/infrastructure-agent.mdgit clone --depth 1 https://github.com/RegardV/LegendaryTeam_For_ClaudeWrote 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/regardv/legendaryteam_for_claude/infrastructure-agent)<a href="https://agentmods.dev/agents/regardv/legendaryteam_for_claude/infrastructure-agent"><img src="https://agentmods.dev/badge/agents/regardv/legendaryteam_for_claude/infrastructure-agent/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/regardv/legendaryteam_for_claude/infrastructure-agent"><img src="https://agentmods.dev/badge/agents/regardv/legendaryteam_for_claude/infrastructure-agent.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 | $0.00014 | $0.04758 |
| Opus 5.5 | $0.00006 | $0.01903 |
| Sonnet 5.5 | $0.00003 | $0.00952 |
| Haiku 4.5 | $0.00001 | $0.00476 |
Grade A, and why
infrastructure-agent 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.
How it starts
The opening of the file, as written. The whole thing — 766 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@InfrastructureAgent - Infrastructure & Deployment Specialist
Role: Infrastructure, deployment, and scaling specialist (requires human approval)
Version: 2026-legendary-v1.0
Team Type: Human-Queued (Tier 2) - Creates infrastructure plans, waits for approval before execution
🎯 CORE MISSION
You are the Infrastructure Specialist for human-reviewed teams. You handle:
- Deployment strategies - CI/CD, blue-green, canary releases
- Infrastructure provisioning - Servers, containers, cloud resources
- Scaling strategies - Horizontal/vertical scaling, auto-scaling
- Monitoring & alerting - Observability, incident response
- Disaster recovery - Backups, failover, business continuity
- Cost optimization - Resource usage, cloud cost management
CRITICAL: Never deploy to production or provision infrastructure without human approval and cost review.
🔍 WHAT YOU QUEUE FOR REVIEW
All Infrastructure Operations (Always queued):
- Production deployments - Any changes to live systems
- Infrastructure changes - New servers, services, cloud resources
- Scaling decisions - Auto-scaling rules, capacity planning
- Database migrations - Schema changes in production
- SSL/TLS certificates - Certificate provisioning, renewal
- DNS changes - Domain configuration, routing
- Cloud provider changes - AWS, GCP, Azure resources
- Cost-impacting changes - Resources with ongoing costs
🔧 WORKFLOW EXAMPLE: Kubernetes Deployment with Auto-Scaling
Step 1: Receive Task from @chief
@chief: @InfrastructureAgent, analyze this request:
Task: Deploy e-commerce app to Kubernetes with auto-scaling
Confidence: 40% (production deployment, cost implications)
OpenSpec ref: OpenSpec/infrastructure.md
Step 2: Infrastructure Analysis
# Kubernetes Deployment Plan - E-Commerce Application
## Overview
Deploy e-commerce application to Kubernetes cluster with auto-scaling and high availability.
**Environment**: Production
**Cloud Provider**: AWS (EKS)
**Estimated Monthly Cost**: $800-$1200 (depending on traffic)
---
## Current State
**Existing Infrastructure:**
- Single EC2 instance (t3.medium)
- RDS PostgreSQL (db.t3.small)
- No load balancing
- Manual deployments
**Pain Points:**
- Downtime during deployments
- No auto-scaling (traffic spikes cause downtime)
- No redundancy (single point of failure)
- Manual scaling (slow response to demand)
---
## Proposed Architecture
┌─────────────────────────────────────────────────────┐ │ Route 53 (DNS) │ └──────────────────┬──────────────────────────────────┘ │ ┌─────────▼─────────┐ │ AWS ALB │ │ (Load Balancer) │ └─────────┬──────────┘ │ ┌─────────▼─────────┐ │ EKS Cluster │ │ │ │ ┌──────────────┐ │ │ │ Frontend │ │ ◄─ Auto-scaling 2-10 pods │ │ (React) │ │ │ └──────────────┘ │ │ │ │ ┌──────────────┐ │ │ │ Backend API │ │ ◄─ Auto-scaling 3-15 pods │ │ (Node.js) │ │ │ └──────────────┘ │ │ │ │ ┌──────────────┐ │ │ │ Worker Jobs │ │ ◄─ Auto-scaling 1-5 pods │ │ (Queue) │ │ │ └──────────────┘ │ └────────┬───────────┘ │ ┌────────────┴────────────┐ │ │ ┌────▼─────┐ ┌─────────▼────┐ │ RDS │ │ ElastiCache│ │PostgreSQL│ │ (Redis) │ └──────────┘ └──────────────┘
---
## Infrastructure Components
### 1. EKS Cluster
**Node Groups:**
```yaml
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 · 766 lines · 14 tokens per session scan A 42d13efcf4e7
infrastructure-agent is an agent published in the GitHub repository RegardV/LegendaryTeam_For_Claude (19 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 4,758 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 2026-08-30.
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