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checkov

Expert guidance for Checkov, the static analysis tool for infrastructure-as-code that scans Terraform, CloudFormation, Kubernetes, Helm, Dockerfile, and ARM templates for security misconfigurations and compliance violations. Helps developers integrate Checkov into CI/CD pipelines and write custom policies.

#iac-security#terraform#kubernetes#compliance#scanning
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed checkov v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Deploy the latest build to the staging environment and run smoke tests"
  • "Check the CI pipeline status and summarize any recent failures"

Documentation

Overview

Checkov, the static analysis tool for infrastructure-as-code that scans Terraform, CloudFormation, Kubernetes, Helm, Dockerfile, and ARM templates for security misconfigurations and compliance violations. Helps developers integrate Checkov into CI/CD pipelines and write custom policies.

Instructions

Scanning

bash
# Install
pip install checkov

# Scan Terraform files
checkov -d ./terraform/

# Scan Kubernetes manifests
checkov -d ./k8s/ --framework kubernetes

# Scan Dockerfiles
checkov -f Dockerfile --framework dockerfile

# Scan with specific checks
checkov -d . --check CKV_AWS_18,CKV_AWS_21   # Only specific checks

# Skip specific checks
checkov -d . --skip-check CKV_AWS_18          # Skip S3 logging check

# Output formats
checkov -d . -o json                           # JSON for CI/CD
checkov -d . -o sarif                          # SARIF for GitHub Security tab
checkov -d . -o junitxml                       # JUnit for test reports

What Checkov Catches

hcl
# Terraform — Checkov flags these misconfigurations:

# ❌ CKV_AWS_18: S3 bucket without access logging
resource "aws_s3_bucket" "data" {
  bucket = "my-data-bucket"
  # Missing: logging { target_bucket = "..." }
}

# ❌ CKV_AWS_145: RDS without encryption
resource "aws_db_instance" "main" {
  engine         = "postgres"
  instance_class = "db.t3.medium"
  # Missing: storage_encrypted = true
}

# ❌ CKV_AWS_24: Security group with 0.0.0.0/0 on SSH
resource "aws_security_group_rule" "ssh" {
  type        = "ingress"
  from_port   = 22
  to_port     = 22
  cidr_blocks = ["0.0.0.0/0"]    # Open SSH to the world
}

# ❌ CKV_AWS_79: EC2 without metadata service v2
resource "aws_instance" "web" {
  ami           = "ami-12345"
  instance_type = "t3.micro"
  # Missing: metadata_options { http_tokens = "required" }
}
yaml
# Kubernetes — Checkov flags these:

# ❌ CKV_K8S_1: Container running as root
# ❌ CKV_K8S_8: No liveness probe
# ❌ CKV_K8S_9: No readiness probe
# ❌ CKV_K8S_12: No memory limit
# ❌ CKV_K8S_13: No memory request
# ❌ CKV_K8S_20: Privileged container
# ❌ CKV_K8S_28: No CPU limit
# ❌ CKV_K8S_37: No capabilities drop
apiVersion: apps/v1
kind: Deployment
spec:
  template:
    spec:
      containers:
        - name: app
          image: myapp:latest      # ❌ CKV_K8S_14: Using 'latest' tag
          # Missing: all security context, probes, and resource limits

Custom Policies

python
# custom_checks/s3_naming.py — Custom Checkov policy in Python
from checkov.terraform.checks.resource.base_resource_check import BaseResourceCheck
from checkov.common.models.enums import CheckResult, CheckCategories

class S3BucketNamingConvention(BaseResourceCheck):
    def __init__(self):
        name = "S3 bucket name must start with company prefix"
        id = "CKV_CUSTOM_1"
        supported_resources = ["aws_s3_bucket"]
        categories = [CheckCategories.CONVENTION]
        super().__init__(name=name, id=id, categories=categories,
                         supported_resources=supported_resources)

    def scan_resource_conf(self, conf):
        bucket_name = conf.get("bucket", [""])[0]
        if bucket_name.startswith("mycompany-"):
            return CheckResult.PASSED
        return CheckResult.FAILED

check = S3BucketNamingConvention()
yaml
# custom_checks/require_tags.yaml — Custom policy in YAML (simpler)
metadata:
  id: "CKV_CUSTOM_2"
  name: "All resources must have 'team' and 'environment' tags"
  category: "CONVENTION"
definition:
  cond_type: "attribute"
  resource_types:
    - "aws_instance"
    - "aws_s3_bucket"
    - "aws_rds_cluster"
  attribute: "tags.team"
  operator: "exists"

CI/CD Integration

yaml
# .github/workflows/security.yml
- name: Checkov IaC Scan
  uses: bridgecrewio/checkov-action@v12
  with:
    directory: terraform/
    framework: terraform
    output_format: sarif
    output_file_path: checkov.sarif
    soft_fail: false                    # Fail the pipeline on findings
    skip_check: CKV_AWS_18             # Skip known exceptions

- name: Upload SARIF
  uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: checkov.sarif

Installation

bash
pip install checkov

# Or via Docker
docker run -v $(pwd):/tf bridgecrew/checkov -d /tf

# Or via Homebrew
brew install checkov

Examples

Example 1: Setting up Checkov for a microservices project

User request:

I have a Node.js API and a React frontend running in Docker. Set up Checkov for monitoring/deployment.

The agent creates the necessary configuration files based on patterns like # Install, sets up the integration with the existing Docker setup, configures appropriate defaults for a Node.js + React stack, and provides verification commands to confirm everything is working.

Example 2: Troubleshooting what checkov catches issues

User request:

Checkov is showing errors in our what checkov catches. Here are the logs: [error output]

The agent analyzes the error output, identifies the root cause by cross-referencing with common Checkov issues, applies the fix (updating configuration, adjusting resource limits, or correcting syntax), and verifies the resolution with appropriate health checks.

Guidelines

  1. Scan in CI/CD — Run Checkov on every PR; catch misconfigurations before they reach production
  2. Start permissive, tighten gradually — Begin with --soft-fail to see findings without blocking; gradually enable hard-fail as you fix issues
  3. Skip with justification — When skipping checks, add inline comments explaining why: #checkov:skip=CKV_AWS_18:Logging handled by org-level trail
  4. Custom policies for your org — Write policies for naming conventions, tagging requirements, and organizational standards
  5. SARIF for GitHub — Output SARIF and upload to GitHub Security tab; findings appear inline on pull requests
  6. Baseline file — Use --baseline to establish a baseline of existing findings; only flag new issues in PRs
  7. Multiple frameworks — Scan Terraform, Kubernetes, Dockerfiles, and Helm charts in the same pipeline
  8. Bridgecrew platform — Use the Bridgecrew platform for centralized policy management and drift detection across teams

Information

Version
1.0.0
Author
terminal-skills
Category
DevOps
License
Apache-2.0