Zurück zu Fähigkeiten

provision-infrastructure-terraform

pjt222
Aktualisiert 2 days ago
5 Ansichten
17
2
17
Auf GitHub ansehen
Entwicklungautomation

Über

Diese Fähigkeit ermöglicht es Entwicklern, Cloud-Infrastruktur mit Terraforms deklarativem IaC-Ansatz bereitzustellen und zu verwalten. Sie unterstützt wichtige Funktionen wie HCL-Module, Remote-State-Backends und Plan/Apply-Workflows für Teamzusammenarbeit. Nutzen Sie sie, um manuelle ClickOps zu ersetzen, Multi-Umgebungs-Infrastruktur zu verwalten und Standards durch wiederverwendbare Module durchzusetzen.

Schnellinstallation

Claude Code

Empfohlen
Primär
npx skills add pjt222/agent-almanac -a claude-code
Plugin-BefehlAlternativ
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternativ
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/provision-infrastructure-terraform

Kopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren

Dokumentation

Provision Infrastructure with Terraform

IaC via Terraform → provision, version, manage cloud resources across AWS, Azure, GCP, other providers.

Use When

  • New cloud infra (VPCs, compute, storage, DBs)
  • Migrate ClickOps/CloudFormation → declarative IaC
  • Multi-env infra (dev, staging, prod)
  • Reproducible infra patterns across teams
  • Version infra changes w/ app code
  • Enforce infra standards via reusable modules

In

  • Required: Terraform CLI installed (terraform --version)
  • Required: Cloud provider creds (AWS, Azure, GCP service accounts)
  • Required: Remote state backend config (S3, Azure Storage, Terraform Cloud)
  • Optional: Existing infra to import or migrate
  • Optional: Terraform Cloud/Enterprise for team collab
  • Optional: Pre-commit hooks for validation + formatting

Do

See Extended Examples for complete config files + templates.

Step 1: Init Terraform Project Structure

Organized dir structure w/ backend config + provider setup.

# Create project structure
mkdir -p terraform/{modules,environments/{dev,staging,prod}}
cd terraform

# Create backend configuration
cat > backend.tf <<'EOF'
terraform {
  required_version = ">= 1.6"

  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }

  backend "s3" {
    bucket         = "my-terraform-state"
    key            = "infrastructure/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-lock"

    # Workspace-specific state files
    workspace_key_prefix = "env"
  }
}

provider "aws" {
  region = var.aws_region

  default_tags {
    tags = {
      ManagedBy   = "Terraform"
      Environment = terraform.workspace
      Project     = var.project_name
    }
  }
}
EOF

# Create variables file
cat > variables.tf <<'EOF'
variable "aws_region" {
  description = "AWS region for resources"
  type        = string
  default     = "us-east-1"
}

variable "project_name" {
  description = "Project name for resource naming and tagging"
  type        = string
  validation {
    condition     = length(var.project_name) > 0 && length(var.project_name) <= 32
    error_message = "Project name must be 1-32 characters"
  }
}

variable "environment" {
  description = "Environment name (dev, staging, prod)"
  type        = string
  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be dev, staging, or prod"
  }
}
EOF

# Initialize Terraform
terraform init

→ Terraform inits successfully, downloads provider plugins, configs remote backend. .terraform/ w/ provider binaries. State backend connection verified.

If err: backend init fails → verify S3 bucket exists + IAM perms allow s3:GetObject, s3:PutObject, dynamodb:GetItem, dynamodb:PutItem. Provider download fails → check network + corporate proxy. terraform init -upgrade to update.

Step 2: Create Reusable Infra Modules

Composable modules for VPC, compute, data infra w/ input validation.

# modules/vpc/main.tf
variable "vpc_cidr" {
  description = "CIDR block for VPC"
  type        = string
  default     = "10.0.0.0/16"
}

variable "availability_zones" {
  description = "List of AZs to use"
  type        = list(string)
}

variable "project_name" {
  description = "Project name for resource naming"
  type        = string
}

variable "environment" {
  description = "Environment name"
  type        = string
}

locals {
  common_tags = {
    Project     = var.project_name
    Environment = var.environment
    Module      = "vpc"
  }
}

resource "aws_vpc" "main" {
  cidr_block           = var.vpc_cidr
  enable_dns_hostnames = true
  enable_dns_support   = true

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-vpc"
  })
}

resource "aws_subnet" "public" {
  count             = length(var.availability_zones)
  vpc_id            = aws_vpc.main.id
  cidr_block        = cidrsubnet(var.vpc_cidr, 8, count.index)
  availability_zone = var.availability_zones[count.index]

  map_public_ip_on_launch = true

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-public-${var.availability_zones[count.index]}"
    Type = "public"
  })
}

resource "aws_subnet" "private" {
  count             = length(var.availability_zones)
  vpc_id            = aws_vpc.main.id
  cidr_block        = cidrsubnet(var.vpc_cidr, 8, count.index + 100)
  availability_zone = var.availability_zones[count.index]

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-private-${var.availability_zones[count.index]}"
    Type = "private"
  })
}

resource "aws_internet_gateway" "main" {
  vpc_id = aws_vpc.main.id

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-igw"
  })
}

resource "aws_eip" "nat" {
  count  = length(var.availability_zones)
  domain = "vpc"

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-nat-eip-${var.availability_zones[count.index]}"
  })

  depends_on = [aws_internet_gateway.main]
}

resource "aws_nat_gateway" "main" {
  count         = length(var.availability_zones)
  allocation_id = aws_eip.nat[count.index].id
  subnet_id     = aws_subnet.public[count.index].id

  tags = merge(local.common_tags, {
    Name = "${var.project_name}-${var.environment}-nat-${var.availability_zones[count.index]}"
  })

  depends_on = [aws_internet_gateway.main]
}

# modules/vpc/outputs.tf
output "vpc_id" {
  description = "VPC ID"
  value       = aws_vpc.main.id
}

output "public_subnet_ids" {
  description = "List of public subnet IDs"
  value       = aws_subnet.public[*].id
}

output "private_subnet_ids" {
  description = "List of private subnet IDs"
  value       = aws_subnet.private[*].id
}

output "nat_gateway_ips" {
  description = "List of NAT Gateway public IPs"
  value       = aws_eip.nat[*].public_ip
}

→ Module creates VPC w/ public/private subnets across AZs, IGW, NAT GWs w/ EIPs. Outputs expose resource IDs for downstream modules.

If err: CIDR overlap → adjust cidrsubnet() calc or validate VPC CIDR doesn't conflict. Dependency errors → verify depends_on ensures proper creation order. terraform graph | dot -Tpng > graph.png to viz.

Step 3: Implement Env-Specific Configs

Env workspaces w/ var overrides + data sources.

# environments/prod/main.tf
terraform {
  required_version = ">= 1.6"
}

# Import shared backend and provider config
# ... (see EXAMPLES.md for complete configuration)

→ Env-specific config creates prod-sized infra w/ 3 AZs, larger instance types, prod security. Data sources resolve latest AMI. Templates render w/ env vars.

If err: workspace errors → terraform workspace new prod. Data source fails → verify AWS creds have ec2:DescribeImages perms. Template rendering errors → validate var types match template expectations.

Step 4: Execute Plan + Apply Workflow

Run plan, review changes, apply w/ approval.

# Format code
terraform fmt -recursive

# Validate configuration
terraform validate

# ... (see EXAMPLES.md for complete configuration)

CI/CD integration:

# .github/workflows/terraform.yml
name: Terraform

on:
  pull_request:
    paths:
# ... (see EXAMPLES.md for complete configuration)

→ Plan shows resource additions/changes/deletions. No drift detected. Apply creates/updates w/o errors. Outputs contain expected values. CI workflow comments plan on PRs, auto-applies on main merges.

If err: plan fails → terraform validate for syntax. State lock errors → identify holder via aws dynamodb get-item --table-name terraform-lock --key '{"LockID":{"S":"terraform-state-bucket/key"}}', force-unlock if stale. Apply fails → check CloudWatch for provider errors. terraform show to inspect current state.

Step 5: Manage State + Drift Detection

State locking, backup, automated drift detection.

# Create DynamoDB table for state locking
cat > state-backend.tf <<'EOF'
resource "aws_dynamodb_table" "terraform_lock" {
  name           = "terraform-lock"
  billing_mode   = "PAY_PER_REQUEST"
  hash_key       = "LockID"
# ... (see EXAMPLES.md for complete configuration)

Auto drift detection:

# Create drift detection script
cat > scripts/detect-drift.sh <<'EOF'
#!/bin/bash
set -euo pipefail

cd terraform
# ... (see EXAMPLES.md for complete configuration)

→ State backend w/ versioning + encryption. Drift detection IDs out-of-band changes. State ops (list, show, mv, import) w/o errors. Auto drift checks on schedule + alerts.

If err: state lock timeouts → verify DynamoDB table exists w/ correct key schema. Versioning issues → check S3 versioning via aws s3api get-bucket-versioning --bucket bucket-name. Import fails → verify resource exists + Terraform config matches actual attributes.

Step 6: Module Testing + Documentation

Auto tests w/ Terratest + generate docs.

// test/vpc_test.go
package test

import (
    "testing"

# ... (see EXAMPLES.md for complete configuration)

Generate docs:

# Install terraform-docs
go install github.com/terraform-docs/terraform-docs@latest

# Generate module documentation
terraform-docs markdown table modules/vpc > modules/vpc/README.md

# ... (see EXAMPLES.md for complete configuration)

→ Terratest validates module creates expected resources w/ correct config. Docs auto-gen from var descriptions + outputs. Pre-commit hooks enforce formatting + validation.

If err: Terratest fails → check AWS creds + quotas. Long tests → parallel via t.Parallel(). Doc gen errors → verify all vars have description. Pre-commit fails → manually terraform fmt + fix validation.

Check

  • Backend w/ encryption, versioning, state locking
  • All modules have input validation + outputs
  • Workspaces isolate env-specific state
  • terraform plan shows no unexpected changes after apply
  • Drift detection auto runs + alerts
  • Modules tested w/ Terratest or similar
  • Docs auto-gen + up-to-date
  • Secrets via AWS Secrets Manager, not hardcoded
  • Cost estimation integrated (Infracost or similar)
  • Blast radius min w/ separate state per env

Traps

  • Hardcoded values: Avoid AMI IDs, AZs, account-specific. Use data sources + vars.
  • Missing lifecycle blocks: Resources recreate unexpectedly. Add lifecycle { create_before_destroy = true } → prevent downtime during updates.
  • No state locking: Concurrent applies corrupt state. Always DynamoDB for locking w/ S3 backend.
  • Overly permissive IAM: Terraform service account full admin. Implement least-privilege scoped to managed resources.
  • No version constraints: Provider updates break infra. Pin via version = "~> 5.0" constraints.
  • Secrets in state: Sensitive values plaintext in state. Use sensitive = true on outputs, store in AWS Secrets Manager, ref via data sources.
  • No backup strategy: State file lost/corrupted, no recovery plan. Enable S3 versioning, regular backups, test recovery.
  • Monolithic config: Single state file manages everything. Split into logical boundaries (networking, compute, data) → reduce blast radius.

  • configure-git-repository — version control for Terraform code
  • build-ci-cd-pipeline — automated Terraform workflows w/ GitHub Actions
  • implement-gitops-workflow — ArgoCD/Flux integration w/ Terraform
  • manage-kubernetes-secrets — secrets mgmt in Terraform-provisioned clusters
  • deploy-to-kubernetes — Terraform Kubernetes provider usage

GitHub Repository

pjt222/agent-almanac
Pfad: i18n/caveman-ultra/skills/provision-infrastructure-terraform
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Verwandte Skills

qmd

Entwicklung

qmd ist ein lokales Such- und Indexierungs-CLI-Tool, das Entwicklern ermöglicht, lokale Dateien mittels Hybridsuche zu indexieren und zu durchsuchen, die BM25, Vektoreinbettungen und Neuordnung kombiniert. Es unterstützt sowohl die Kommandozeilennutzung als auch den MCP-Modus (Model Context Protocol) zur Integration mit Claude. Das Tool verwendet Ollama für Einbettungen und speichert Indizes lokal, was es ideal für die direkte Suche in Dokumentationen oder Codebasen vom Terminal aus macht.

Skill ansehen

subagent-driven-development

Entwicklung

Diese Fähigkeit führt Implementierungspläne aus, indem für jede unabhängige Aufgabe ein neuer Subagent bereitgestellt wird, mit Code-Review zwischen den Aufgaben. Sie ermöglicht schnelle Iterationen, während Qualitätssicherungsschritte durch diesen Review-Prozess gewahrt bleiben. Nutzen Sie sie, wenn Sie überwiegend unabhängige Aufgaben innerhalb derselben Sitzung bearbeiten, um kontinuierlichen Fortschritt mit integrierten Qualitätsprüfungen zu gewährleisten.

Skill ansehen

mcporter

Entwicklung

Die mcporter-Skill ermöglicht es Entwicklern, Model Context Protocol (MCP)-Server direkt aus Claude heraus zu verwalten und aufzurufen. Sie bietet Befehle, um verfügbare Server aufzulisten, deren Tools mit Argumenten aufzurufen sowie Authentifizierung und Daemon-Lebenszyklus zu handhaben. Nutzen Sie diese Skill, um MCP-Server-Funktionalität in Ihren Entwicklungs-Workflow zu integrieren und zu testen.

Skill ansehen

adk-deployment-specialist

Entwicklung

Diese Fähigkeit stellt Vertex AI ADK-Agenten über das A2A-Protokoll bereit und orchestriert sie, verwaltet die AgentCard-Erkennung, Aufgabenübermittlung und unterstützende Tools wie die Code Execution Sandbox und Memory Bank. Sie ermöglicht den Aufbau von Multi-Agenten-Systemen mit sequenziellen, parallelen oder Schleifen-Orchestrierungsmustern in Python, Java oder Go. Verwenden Sie sie, wenn Sie aufgefordert werden, ADK-Agenten bereitzustellen oder Agenten-Workflows auf Google Cloud zu orchestrieren.

Skill ansehen