Skip to content
cccskills
FreePublished on GitHub

iac-automation

Terraform, Pulumi, CloudFormation, and infrastructure as code for data platforms

The English summary is being prepared. Showing the original description.

Works withAWSTerraform
See how to install

Files included (6)

  • SKILL.md6.4 KB
  • assets/config.yaml693 B
  • assets/schema.json1.1 KB
  • references/GUIDE.md1.9 KB
  • references/PATTERNS.md1.5 KB
  • scripts/validate.py3.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Infrastructure as Code

Production infrastructure automation with Terraform, Pulumi, and cloud-native IaC patterns.

Quick Start

# Terraform - AWS Data Lake Infrastructure
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
  backend "s3" {
    bucket = "terraform-state-prod"
    key    = "data-lake/terraform.tfstate"
    region = "us-east-1"
  }
}

# Data Lake S3 Bucket
resource "aws_s3_bucket" "data_lake" {
  bucket = "company-data-lake-${var.environment}"

  tags = {
    Environment = var.environment
    ManagedBy   = "terraform"
  }
}

resource "aws_s3_bucket_versioning" "data_lake" {
  bucket = aws_s3_bucket.data_lake.id
  versioning_configuration {
    status = "Enabled"
  }
}

# Glue Catalog Database
resource "aws_glue_catalog_database" "analytics" {
  name = "analytics_${var.environment}"
}

# Output
output "data_lake_bucket" {
  value = aws_s3_bucket.data_lake.bucket
}

Core Concepts

1. Terraform Modules

# modules/data-pipeline/main.tf
variable "pipeline_name" {
  type        = string
  description = "Name of the data pipeline"
}

variable "schedule" {
  type    = string
  default = "cron(0 2 * * ? *)"
}

resource "aws_glue_job" "etl" {
  name     = var.pipeline_name
  role_arn = aws_iam_role.glue.arn

  command {
    script_location = "s3://${var.scripts_bucket}/jobs/${var.pipeline_name}.py"
    python_version  = "3"
  }

  default_arguments = {
    "--job-language"        = "python"
    "--enable-metrics"      = "true"
    "--enable-spark-ui"     = "true"
  }

  glue_version      = "4.0"
  worker_type       = "G.1X"
  number_of_workers = 2
}

resource "aws_glue_trigger" "scheduled" {
  name     = "${var.pipeline_name}-trigger"
  schedule = var.schedule
  type     = "SCHEDULED"

  actions {
    job_name = aws_glue_job.etl.name
  }
}

# Usage
module "customer_pipeline" {
  source        = "./modules/data-pipeline"
  pipeline_name = "customer-etl"
  schedule      = "cron(0 3 * * ? *)"
}

2. State Management

# Remote state configuration
terraform {
  backend "s3" {
    bucket         = "terraform-state"
    key            = "env/prod/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-locks"
  }
}

# State locking with DynamoDB
resource "aws_dynamodb_table" "terraform_locks" {
  name         = "terraform-locks"
  billing_mode = "PAY_PER_REQUEST"
  hash_key     = "LockID"

  attribute {
    name = "LockID"
    type = "S"
  }
}

# Import existing resources
# terraform import aws_s3_bucket.existing bucket-name

# Move resources between states
# terraform state mv module.old.resource module.new.resource

3. Pulumi (Python)

import pulumi
import pulumi_aws as aws

# Configuration
config = pulumi.Config()
environment = config.require("environment")

# S3 Data Lake
data_lake = aws.s3.Bucket(
    "data-lake",
    bucket=f"company-data-lake-{environment}",
    versioning=aws.s3.BucketVersioningArgs(enabled=True),
    tags={"Environment": environment, "ManagedBy": "pulumi"}
)

# Glue Database
analytics_db = aws.glue.CatalogDatabase(
    "analytics",
    name=f"analytics_{environment}"
)

# Lambda for data processing
data_processor = aws.lambda_.Function(
    "data-processor",
    runtime="python3.11",
    handler="handler.main",
    role=lambda_role.arn,
    code=pulumi.FileArchive("./lambda"),
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={"BUCKET": data_lake.bucket}
    )
)

# Export outputs
pulumi.export("bucket_name", data_lake.bucket)
pulumi.export("database_name", analytics_db.name)

4. Environment Management

# environments/prod/main.tf
module "data_platform" {
  source = "../../modules/data-platform"

  environment         = "prod"
  vpc_cidr           = "10.0.0.0/16"
  instance_type      = "r5.2xlarge"
  min_capacity       = 2
  max_capacity       = 10

  tags = {
    Environment = "prod"
    CostCenter  = "data-engineering"
  }
}

# Workspace-based environments
# terraform workspace new prod
# terraform workspace select prod

locals {
  env_config = {
    dev = {
      instance_type = "t3.medium"
      min_nodes     = 1
    }
    prod = {
      instance_type = "r5.xlarge"
      min_nodes     = 3
    }
  }
  config = local.env_config[terraform.workspace]
}

Tools & Technologies

ToolPurposeVersion (2025)
TerraformIaC standard1.7+
PulumiIaC with Python3.100+
CloudFormationAWS nativeLatest
TerragruntTerraform wrapper0.55+
tfsecSecurity scanning1.28+
CheckovPolicy as code3.2+

Troubleshooting Guide

IssueSymptomsRoot CauseFix
State LockCan't applyPrevious run crashedterraform force-unlock
DriftPlan shows changesManual changesImport or recreate
Cycle ErrorDependency cycleCircular referencesRefactor dependencies
Provider ErrorAuth failedWrong credentialsCheck AWS profile

Best Practices

# ✅ DO: Use variables with validation
variable "environment" {
  type = string
  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be dev, staging, or prod."
  }
}

# ✅ DO: Tag all resources
default_tags {
  tags = {
    ManagedBy   = "terraform"
    Environment = var.environment
  }
}

# ✅ DO: Use data sources for existing resources
data "aws_vpc" "existing" {
  id = var.vpc_id
}

# ❌ DON'T: Hard-code values
# ❌ DON'T: Store state locally in production
# ❌ DON'T: Skip plan review before apply

Resources


Skill Certification Checklist:

  • Can write Terraform modules
  • Can manage remote state
  • Can use workspaces for environments
  • Can implement security best practices
  • Can automate with CI/CD

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

See all skills by bouclem

Report a problem with this skill