
Infrastructure (Grade A)Security-tested development skill for Claude AI. Grade A. Workflow for provisioning cloud infrastructure using IaC. Use when the user needs to create or modify cloud resources.
Overview
Key features
- IaC template creation
- Security group and VPC configuration
- Encryption at rest and in transit
- Least privilege IAM setup
- Monitoring and alerting
Pricing
- Model
- Free
- Category
- Skills
- Rating
- No reviews yet
Use cases
Cloud Resource Creation
Use Infrastructure (Grade A) to create new cloud resources, such as virtual machines, databases, and storage buckets, for a development environment.
Infrastructure Updates
Use Infrastructure (Grade A) to modify existing cloud resources, such as updating security groups or scaling compute resources, for a production environment.
Pros & Cons
Pros
- Security-tested and validated
- Streamlined workflow for cloud infrastructure provisioning
- Uses Infrastructure as Code (IaC) for versioning and reproducibility
Cons
- Complex setup and configuration
- Requires expertise in IaC tools like Terraform or CloudFormation
Reviews
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Q&A
Why You Should Use This?
| Audience | Key Benefits | | -------------------- | ------------------------------------------------------------------------------------------------- | | Individual | 30%+ token savings, stop repeating standards, security guardrails, instant scaffolding | | Teams | Consistent AI behavior, day-one onboarding, codified patterns, compounding cost savings | | Organizations | Always-on compliance, same standards across 1,000 projects, central governance, measurable ROI |
Asked by Ximenez Alvarado · Oct 18, 2025
What You Can Customize?
| Section | What It Controls | Example | | ------------- | -------------------------- | ---------------------------------------------- | | project | Project identity | Name, description, repo URL | | techStack | Language, framework, tools | TypeScript + Express or Python + FastAPI | | paths | Directory structure | Where handlers, services, and common code live | | domain | Business entities | Order, Product, Customer + lifecycle states | | patterns | Code patterns | 7-step handler flow, error handling strategy | | testing | Quality gates | 90% coverage, test/lint/type-check commands | | database | DB conventions | Soft delete field, timestamp columns, naming | | packages | Internal packages | @your-org scope, registry URL | | conventions | Git and workflow | Branch prefixes, commit format, PR templates |
Asked by Yuki Kobayashi · Oct 9, 2025
What it detects?
| Category | Signals | | ------------------ | ------------------------------------------------------------------------------------------ | | Language | tsconfig.json, go.mod, Cargo.toml, requirements.txt, pom.xml, file extensions | | Framework | Dependencies in package.json / requirements.txt (React, Express, Django, Spring, etc.) | | Database | ORM configs (prisma/, sequelize, typeorm), .sql files, migration folders | | Testing | jest.config., vitest, pytest, cypress/, playwright.config. | | Infrastructure | Dockerfile, terraform/, cdk.json, serverless.yml, cloud SDK deps | | CI/CD | .github/workflows/, .gitlab-ci.yml, Jenkinsfile |
Asked by Adaeze Uche · Sep 4, 2025
What's Inside?
| Layer | Count | What It Does | How It's Triggered | |-------------|-----|-------------------------------------------------|-----------------------------------| | Rules | 47 | Enforces coding standards on every AI interaction | Automatically — always on | | Agents | 62 | Specialized assistants for complex tasks | On demand — /agent-name | | Skills | 50 | Step-by-step guided workflows with checklists | Contextually — when patterns match | | Commands | 37 | Lightweight, token-efficient quick actions | On demand — /command | | Hooks | 12 | Automation scripts in the AI loop | Event-driven — before/after actions | | Templates | 9 | Scaffolding for handlers, components, tests, etc. | Referenced by skills and agents | ---
Asked by Tatiana Popescu · Aug 19, 2025
How It Works?
Layer 1 — Pre-Processing: Hooks inject project context and block dangerous commands before your prompt reaches the AI. Layer 2 — Rules Engine: 47 always-on rules enforce token efficiency, security, architecture, code standards, database conventions, and testing thresholds. Layer 3 — Specialized Processing: The right component activates — an agent, skill, or command — based on your prompt. Layer 4 — Post-Processing: Hooks validate output, auto-format, scan for secrets, and verify coverage.
Asked by Xavier Costa · Aug 8, 2025
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