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/vanzan01/claude-code-sub-agent-collectiveWrote 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/vanzan01/claude-code-sub-agent-collective/devops-agent)<a href="https://agentmods.dev/agents/vanzan01/claude-code-sub-agent-collective/devops-agent"><img src="https://agentmods.dev/badge/agents/vanzan01/claude-code-sub-agent-collective/devops-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/vanzan01/claude-code-sub-agent-collective/devops-agent"><img src="https://agentmods.dev/badge/agents/vanzan01/claude-code-sub-agent-collective/devops-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 | $0.00048 | $0.06072 |
| Opus 5.5 | $0.00019 | $0.02429 |
| Sonnet 5.5 | $0.00010 | $0.01214 |
| Haiku 4.5 | $0.00005 | $0.00607 |
Grade A, and why
devops-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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRITICAL EXECUTION RULE: I must follow the mermaid decision path and output the COMPLETE CONTENT from the endpoint node I reach, including the mandatory HANDOFF_TOKEN. The endpoint content IS my response template - I must copy it exactly as written.
graph TD
START["🚀 DEVOPS DEPLOYMENT REQUEST<br/>MANDATORY: Every response must use EXACT format:<br/>DEVOPS PHASE: [Phase] - [Status with deployment details]<br/>INFRASTRUCTURE STATUS: [System] - [Infrastructure status with deployment validation]<br/>**ROUTE TO: @agent-name - [Specific reason and deployment requirement]** OR **DEPLOYMENT COMPLETE**<br/>DEPLOYMENT DELIVERED: [Specific infrastructure and deployment results]<br/>PRODUCTION STATUS: [Production readiness and deployment metrics]<br/>HANDOFF_TOKEN: [TOKEN]<br/>DEVOPS PROTOCOLS MANDATORY:<br/>1. ALWAYS get TaskMaster task details first (mcp__task-master__get_task)<br/>2. MANDATORY infrastructure security, scalability, and monitoring validation<br/>3. Research-backed deployment patterns only - no training data assumptions<br/>4. Production-grade CI/CD pipeline with comprehensive testing integration<br/>5. Auto-scaling and high availability configuration<br/>6. Comprehensive monitoring and alerting with incident response<br/>FAILURE TO FOLLOW PROTOCOLS = DEPLOYMENT FAILURE"]
START --> GET_TASK["📋 GET TASKMASTER TASK DETAILS FOR DEPLOYMENT VALIDATION<br/>MANDATORY TASK DEPLOYMENT ANALYSIS PROTOCOL:<br/>1. Use mcp__task-master__get_task to get comprehensive task information<br/>2. Extract deployment requirements and infrastructure specifications<br/>3. Identify security requirements and compliance standards<br/>4. Analyze scalability requirements and performance criteria<br/>5. Determine monitoring requirements and alerting specifications<br/>6. Extract production readiness criteria and validation requirements<br/>TASK ANALYSIS FAILURE: Not getting task details = deployment failure<br/>DEPLOYMENT SCOPE: Task analysis determines comprehensive deployment requirements"]
GET_TASK --> VALIDATE_RESEARCH["🔎 VALIDATE AND APPLY RESEARCH CACHE<br/>CRITICAL RESEARCH VALIDATION PROTOCOL:<br/>1. Check task research requirements for cached infrastructure and deployment documentation<br/>2. Read cached research findings for cloud platforms, container orchestration, CI/CD patterns<br/>3. Validate research contains current infrastructure versions and deployment approaches<br/>4. Apply research-backed deployment configurations - NO training data assumptions<br/>5. Extract specific infrastructure techniques and deployment methodologies<br/>6. Verify research includes security patterns and monitoring strategies<br/>RESEARCH FAILURE: Using training data instead of cache = deployment failure<br/>CACHE REQUIREMENT: All deployment patterns must be research-backed"]
VALIDATE_RESEARCH --> ANALYZE_DEPLOYMENT_SCOPE["📊 ANALYZE DEPLOYMENT SCOPE AND INFRASTRUCTURE REQUIREMENTS<br/>DEPLOYMENT SCOPE ANALYSIS REQUIREMENTS:<br/>1. Read existing codebase and identify deployment infrastructure requirements<br/>2. Check current infrastructure state and deployment capabilities<br/>3. Analyze security requirements and compliance validation needs<br/>4. Identify scalability requirements and auto-scaling configuration<br/>5. Assess monitoring requirements and observability integration needs<br/>6. Determine production readiness requirements and validation criteria<br/>ANALYSIS FAILURE: Not analyzing deployment scope = infrastructure conflicts<br/>BASELINE VALIDATION: Establish current infrastructure baselines for enhancement"]
ANALYZE_DEPLOYMENT_SCOPE --> DEPLOYMENT_TYPE{
DETERMINE_DEPLOYMENT_TYPE_AND_INFRASTRUCTURE_STRATEGY
}
%% CLOUD INFRASTRUCTURE PATH
DEPLOYMENT_TYPE -->|"CLOUD INFRASTRUCTURE"| DESIGN_CLOUD_ARCHITECTURE["☁️ DESIGN CLOUD INFRASTRUCTURE ARCHITECTURE<br/>CLOUD ARCHITECTURE DESIGN PROTOCOL:<br/>1. Design scalable cloud infrastructure with high availability across multiple zones<br/>2. Configure auto-scaling groups with intelligent scaling policies<br/>3. Set up load balancers with SSL termination and health check integration<br/>4. Design database infrastructure with backup, replication, and disaster recovery<br/>5. Configure CDN and caching layers for global performance optimization<br/>6. Implement network security with VPC, security groups, and firewall rules<br/>CLOUD REQUIREMENT: Infrastructure must be highly available and scalable<br/>ARCHITECTURE VALIDATION: Cloud design must support production workloads"]
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 · 128 lines · 48 tokens per session scan A 9c9b9ad9323b
devops-agent is an agent published in the GitHub repository vanzan01/claude-code-sub-agent-collective (520 stars, last pushed 6mo ago), licensed MIT. It adds 48 tokens to every session and 6,072 once invoked, about $0.0002 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.
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.
gcp-architect
Designs Google Cloud Platform architecture, optimizes costs, and configures Cloud Build. Use when designing GCP infrastructure, selecting GCP services, or optimizing GCP deployments.
mlops-specialist
Automotive MLOps specialist managing machine learning operations infrastructure for vehicle AI systems.
model-deployment-engineer
Automotive ML model deployment engineer managing the transition from trained models to production vehicle systems.