by patrickchugh
Professional cloud architecture diagrams with official AWS, Azure and GCP icons, from Terraform code or a plain JSON graph. MCP server + agent skill.
# Add to your Claude Code skills
git clone https://github.com/patrickchugh/terravisionLast scanned: 9/25/2026
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"status": "PASSED",
"scannedAt": "2026-09-25T09:24:33.631Z",
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}terravision is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by patrickchugh. Professional cloud architecture diagrams with official AWS, Azure and GCP icons, from Terraform code or a plain JSON graph. MCP server + agent skill. It has 1,638 GitHub stars.
Yes. terravision passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/patrickchugh/terravision" and add it to your Claude Code skills directory (see the Installation section above).
terravision is primarily written in Python. It is open-source under patrickchugh on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh terravision against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Turn Terraform code into professional cloud architecture diagrams that stay in sync with your infrastructure — automatic, secure, living documents
TerraVision automatically converts your Terraform code into professional-grade cloud architecture diagrams using the official AWS, GCP, and Azure icon sets. Your diagrams stay in sync with your infrastructure — no more outdated Visio, draw.io or Lucidchart files.
| Provider | Status | Resources |
|---|---|---|
| AWS | ✅ Full support | 200+ services |
| Google Cloud | 🔄 Partial support | Core services |
| Azure | 🔄 Partial support | Core services |
pipx install terravision # or: pip install terravision if in a virtual env
You also need Python 3.10+, Terraform 1.x, Graphviz, and Git. See the Installation Guide for platform-specific instructions, Docker, and Nix.
git clone https://github.com/patrickchugh/terravision.git
cd terravision
# EKS cluster example
terravision draw --source tests/fixtures/aws_terraform/eks_automode --show
# Azure VM scale set
terravision draw --source tests/fixtures/azure_terraform/test_vm_vmss --show
# From a public Git repo (note the // for subfolder)
terravision draw --source https://github.com/patrickchugh/terraform-examples.git//aws/wordpress_fargate --show
That's it — your diagram is saved as architecture.png and opens automatically.
terravision visualise --source ./path-to-your-terraform --show
Click any resource to see its Terraform metadata, search resources, pan/zoom, and watch animated data flow on edges. The HTML is a single self-contained file that works fully offline.
Click any of these to see the interactive HTML output TerraVision produces:
# From a local directory
terravision draw --source ./path-to-your-terraform
# From a Git repository
terravision draw --source https://github.com/user/repo.git
# Custom format and filename
terravision draw --source ./path-to-your-terraform --format svg --outfile my-architecture
# Editable draw.io file
terravision draw --source ./path-to-your-terraform --format drawio --outfile my-architecture
# Step 1: in your Terraform environment
terraform plan -out=tfplan.bin
terraform show -json tfplan.bin > plan.json
terraform graph > graph.dot
# Step 2: diagram generation, no Terraform or cloud access required
terravision draw --planfile plan.json --graphfile graph.dot --source ./path-to-your-terraform
terravision draw --source ./path-to-your-terraform --ai-annotate ollama # local LLM (no data leaves your machine)
terravision draw --source ./path-to-your-terraform --ai-annotate bedrock # AWS Bedrock via boto3 (uses your AWS credentials)
terravision draw --source ./path-to-your-terraform --ai-annotate restapi # any OpenAI-compatible endpoint (OpenAI, LiteLLM, vLLM, ...)
Only metadata and the summary graph are sent to the LLM — never your .tf source. The bedrock backend authenticates via the standard AWS credential chain (no infrastructure to deploy); restapi is configured via TV_RESTAPI_URL, TV_RESTAPI_KEY, and TV_RESTAPI_MODEL. See the Annotations Guide and AI-Powered Annotations for the full configuration.
terravision draw --source ./path-to-your-terraform --simplified
Strips VPCs, subnets, and networking plumbing. Great for executive presentations.
terravision --help shows full help text details.
| Option | Description | Example |
|---|---|---|
--source |
Terraform directory or Git URL | ./path-to-your-terraform |
--format |
Output format: png, svg, pdf, drawio, and more |
svg |
--outfile |
Output filename | my-architecture |
--workspace |
Terraform workspace | production |
--varfile |
Variable file (repeatable) | prod.tfvars |
--planfile |
Pre-generated plan JSON | plan.json |
--graphfile |
Pre-generated graph DOT | graph.dot |
--ai-annotate |
AI annotation backend | ollama, bedrock, restapi |
--simplified |
High-level view (no networking) | (flag) |
--show |
Open after generation | (flag) |
The complete documentation lives at patrickchugh.github.io/terravision.
For users:
For contributors:
Common questions — cloud credentials, LLM data privacy, offline use, Terragrunt, output formats, and more — are answered in the FAQ on the documentation site.
Contributions are very welcome. See CONTRIBUTING.md for development setup, coding standards, and the PR process.
See LICENSE.