How does Atlas find relevant Terraform HCL code without knowing filenames?

Atlas uses hybrid semantic and keyword retrieval, indexing Terraform HCL code by AST declarations using tree-sitter, not blind line windows, to find relevant `.tf` files and snippets based on meaning.

Can Atlas help me understand Terraform HCL module dependencies?

Yes, Atlas can read your `terraform init (module registry)` dependencies and use the `lsp` tool's `goToDefinition` operation to navigate module calls and resource references within your `.tf` files, providing a clear dependency map.

Is it safe to let Atlas explore my Terraform HCL repository?

Yes, the `explore` subagent is permissioned `deny-by-default`, allowing only safe read-only operations like `grep`, `glob`, `read`, `bash`, `webfetch`, and `websearch` on your Terraform HCL code, ensuring no accidental changes.

How does Atlas handle Terraform HCL formatting and testing?

Atlas integrates directly with the Terraform HCL toolchain, allowing it to run `terraform fmt -recursive` for consistent formatting and add `terraform test` cases in your `tests/` directory, which can then be executed with `terraform test`.

Does Atlas keep my Terraform HCL code off third-party servers?

Yes, Atlas can build its code index with local Ollama embeddings, ensuring your Terraform HCL code remains entirely on your local machine, off third-party servers, maintaining data privacy and security.

How does Atlas help me review potential Terraform HCL changes?

Atlas drafts a plan in a read-only plan agent, computes a unified diff for every file edit, and surfaces it for your approval before writing. It also runs `terraform validate` and `terraform plan` behind a permission prompt, allowing you to review the plan diff before any Terraform HCL is applied.

What Terraform HCL files and constructs does Atlas understand?

Atlas understands `.tf` files, including resources, variables, outputs, and the provider versions pinned in your `.terraform.lock.hcl` file, providing a comprehensive view of your Terraform HCL root module and its components.

Onboard to an Unfamiliar Terraform HCL Codebase in 2026 with Atlas

To onboard to an unfamiliar Terraform HCL codebase in 2026, Atlas helps you build a working mental model by starting from meaning, not filenames, leveraging tools like `terraform init (module registry)` for dependencies, `terraform fmt` for consistent styling, and `terraform test` for validation, all while keeping your code local and secure through its terminal-native AI agent.

How does Atlas help me understand a Terraform HCL codebase?

Atlas helps you build a working mental model of an unfamiliar Terraform HCL codebase in 2026 by starting from meaning, not just filenames. It uses hybrid semantic and keyword retrieval, indexing your `.tf` files by AST declarations with tree-sitter, not blind line windows, to find relevant code snippets in under 1 second.

When you encounter a new Terraform HCL repository, Atlas begins by indexing your code using AST declarations via tree-sitter. This allows `codebase_search` to query a semantic index for concepts you care about, such as 'how requests are authenticated' or 'where are AWS S3 buckets configured'. Instead of guessing file names, Atlas returns ranked snippets with precise file paths from your `.tf` files. This approach ensures that you focus on the most relevant parts of the infrastructure definition, avoiding the need to read every single file. Furthermore, Atlas can build its code index with local Ollama embeddings, ensuring your sensitive Terraform HCL configurations remain entirely off third-party servers, maintaining your security posture.

How do I explore Terraform HCL modules and resources safely with Atlas?

Exploring an unfamiliar Terraform HCL codebase safely is crucial, and Atlas facilitates this with its `explore` subagent. This subagent operates with a `deny-by-default` permission set, allowing only 6 specific read-only operations like `grep` and `read`, ensuring no accidental changes occur while you investigate the infrastructure in 2026.

The `explore` subagent is designed for wide sweeps and deep dives into your Terraform HCL repository without any risk of modification. When you delegate a task to `explore` using the `task` tool, it is strictly permissioned with a `deny-by-default` rule set. This means it can only execute `grep`, `glob`, `read`, `bash`, `webfetch`, and `websearch`. This controlled environment allows you to ask the subagent to 'find all `aws_instance` resources' or 'list all variables defined in `modules/vpc`' and receive comprehensive answers without concern for unintended side effects. It's an invaluable tool for understanding how different Terraform HCL modules interact, how resources are configured, and how variables are passed throughout your infrastructure, all within a secure, read-only context.

How does Atlas help with Terraform HCL module dependencies and file structure?

Atlas helps you quickly grasp the structure and dependencies of a Terraform HCL codebase in 2026 by leveraging `glob` for directory layout and `lsp` for following module calls. It understands the significance of `terraform init (module registry)` for managing external and internal module dependencies, providing a holistic view.

Understanding the layout and dependencies is fundamental to onboarding to any Terraform HCL project. Atlas uses the `glob` tool to map the top-level directories, giving you an immediate sense of the package layout and naming conventions, such as `modules/`, `environments/`, or `providers/`. After identifying key `.tf` files with `codebase_search` and `read`, you can use the `lsp` tool's `goToDefinition` operation to follow module calls, resource references, or variable definitions. Atlas is also aware of your `terraform init (module registry)` dependencies, allowing it to understand how external modules are integrated and where their source code resides. This capability is crucial for tracing the flow of configuration and understanding the full dependency graph of your infrastructure as code.

How does Atlas ensure safety and review for Terraform HCL changes?

Atlas prioritizes safety and review for any potential Terraform HCL changes in 2026 through a multi-layered permission system. Every Atlas tool call is permission-gated, and it drafts a plan in a read-only agent before switching to a build agent, ensuring you have 100% control over modifications.

When you move from exploration to making changes in a Terraform HCL codebase, Atlas implements stringent safety protocols. Every Atlas tool call is permission-gated against `allow`, `ask`, and `deny` rules, giving you explicit control. Before any modification, Atlas drafts a plan in a read-only plan agent and asks for your approval before switching to a build agent. For Terraform HCL specifically, Atlas can run `terraform validate` and `terraform plan` behind a permission prompt, allowing you to review the proposed infrastructure changes. It then computes a unified diff for every file edit and surfaces it for your approval before writing. This ensures that you can meticulously review the `terraform plan` diff and all file changes, preventing unintended infrastructure deployments. Atlas also snapshots file changes as git patches, so edits can be easily diffed and rolled back if needed.

How do I document my learning and track open questions in Terraform HCL?

As you build a mental model of a new Terraform HCL codebase in 2026, documenting your findings and open questions is essential for continuity. Atlas provides the `todowrite` tool to record what you learned as a persistent list, ensuring your insights survive into the next turn and beyond.

Onboarding to a complex Terraform HCL codebase involves accumulating a lot of information and identifying areas that require further investigation. The `todowrite` tool in Atlas is designed to help you capture these insights effectively. You can use it to record key learnings about module responsibilities, resource configurations, or provider usage. Crucially, `todowrite` also allows you to list open questions or areas for future exploration, ensuring that these items are not lost between sessions. This structured approach to documentation helps you systematically build a comprehensive mental model of the Terraform HCL repository, making your onboarding process more efficient and your knowledge more durable.

Step by step

01Ask `codebase_search` a plain-language question about the Terraform HCL codebase, for example, 'how are AWS S3 buckets configured?' to get ranked `.tf` snippets.
02Run `glob` on the top-level directories to see the Terraform HCL module layout and naming conventions, such as `modules/` or `environments/`.
03Read the two or three `.tf` files `codebase_search` ranked highest, then follow module calls or resource references with the `lsp` tool's `goToDefinition` operation.
04Delegate wider sweeps to the `explore` subagent through the `task` tool, for example, `task explore 'find all aws_vpc resources and their associated subnets'`.
05Record what you learned about the Terraform HCL configuration as a `todowrite` list so open questions survive into the next turn.
06If making changes, let Atlas run `terraform validate` and `terraform plan` behind a permission prompt to review the proposed infrastructure changes.
07Review the `terraform plan` diff with Atlas, ensuring all changes to your Terraform HCL configuration are understood and approved.
08Have Atlas add `terraform test` cases in your `tests/` directory and finish with `terraform fmt -recursive` for consistent styling.

Frequently asked questions

How does Atlas find relevant Terraform HCL code without knowing filenames?: Atlas uses hybrid semantic and keyword retrieval, indexing Terraform HCL code by AST declarations using tree-sitter, not blind line windows, to find relevant `.tf` files and snippets based on meaning.
Can Atlas help me understand Terraform HCL module dependencies?: Yes, Atlas can read your `terraform init (module registry)` dependencies and use the `lsp` tool's `goToDefinition` operation to navigate module calls and resource references within your `.tf` files, providing a clear dependency map.
Is it safe to let Atlas explore my Terraform HCL repository?: Yes, the `explore` subagent is permissioned `deny-by-default`, allowing only safe read-only operations like `grep`, `glob`, `read`, `bash`, `webfetch`, and `websearch` on your Terraform HCL code, ensuring no accidental changes.
How does Atlas handle Terraform HCL formatting and testing?: Atlas integrates directly with the Terraform HCL toolchain, allowing it to run `terraform fmt -recursive` for consistent formatting and add `terraform test` cases in your `tests/` directory, which can then be executed with `terraform test`.
Does Atlas keep my Terraform HCL code off third-party servers?: Yes, Atlas can build its code index with local Ollama embeddings, ensuring your Terraform HCL code remains entirely on your local machine, off third-party servers, maintaining data privacy and security.
How does Atlas help me review potential Terraform HCL changes?: Atlas drafts a plan in a read-only plan agent, computes a unified diff for every file edit, and surfaces it for your approval before writing. It also runs `terraform validate` and `terraform plan` behind a permission prompt, allowing you to review the plan diff before any Terraform HCL is applied.
What Terraform HCL files and constructs does Atlas understand?: Atlas understands `.tf` files, including resources, variables, outputs, and the provider versions pinned in your `.terraform.lock.hcl` file, providing a comprehensive view of your Terraform HCL root module and its components.

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