Atlas automates GitHub issue and pull request triage for Polars projects by integrating directly into your GitHub workflows, ensuring safe and permission-gated responses. It leverages your existing Polars toolchain, including `uv` for package management, `ruff format` for code style, and `pytest (assert_frame_equal)` for robust testing, to propose and apply changes with full transparency and approval.
How to set up Atlas for GitHub triage in a Polars project?
Setting up Atlas for GitHub triage in a Polars project involves wiring the `atlas github` command into a GitHub Actions workflow, ensuring the `MODEL` and `PROMPT` inputs are correctly configured. This process, streamlined for 2026, requires a `pyproject.toml` file that pins your Polars dependencies.
To enable Atlas for automated triage in your Polars repository, you must create a GitHub Actions workflow file, typically located at `.github/workflows/atlas-triage.yml`. Within this workflow, you will invoke the `atlas github` command. Atlas is designed to read its inputs directly from the Actions environment, requiring the `MODEL` input in `provider/model` form (e.g., `ollama/llama3`) and the `PROMPT` input for specific event types that need one. If these are incorrect or missing, Atlas will refuse to run, providing clear error messages like 'PROMPT input is required for <event> events.' This ensures that Atlas operates only under explicitly defined conditions, preventing unintended actions in your Polars codebase. Your project's `pyproject.toml` file, which pins your Polars version and other dependencies, is crucial for Atlas to understand the project context and manage packages using `uv`.
How Atlas ensures safe automated responses in Polars repositories?
Atlas ensures safe automated responses in Polars repositories through a multi-layered security model, starting with strict permission checks that require the triggering actor to have admin or write access. This critical first step prevents unauthorized automation, with Atlas refusing to run for 0-permission users.
Safety is paramount when automating changes to a Polars codebase. Atlas implements robust security measures to prevent unauthorized or erroneous actions. First, it rigorously checks the triggering actor's collaborator permission, refusing to proceed if the user lacks `admin` or `write` access to the repository. This ensures that only trusted contributors can initiate automated triage. Additionally, Atlas enforces a mention requirement, meaning a stray comment cannot accidentally start a run; the handler requires comments to explicitly mention the configured trigger (e.g., `@atlas triage`). Before any changes are made, Atlas drafts a plan in a read-only plan agent, which you can review. Only after explicit approval does it switch to a build agent. Every file edit generates a unified diff, which Atlas surfaces for your approval before writing, and it snapshots file changes as git patches, allowing edits to be easily diffed and rolled back. This meticulous process ensures that any proposed modifications to your Polars `LazyFrame` chains or data processing logic are thoroughly vetted and approved.
Automating Polars code review with Atlas and GitHub Actions
Atlas can automate Polars code review tasks within GitHub Actions, leveraging its `bash`, `read`, `grep`, and `edit` tools to interact with your codebase. It can, for instance, run `ruff format` to standardize code style or execute `pytest (assert_frame_equal)` for testing, all behind a permission prompt, ensuring 100% control.
Integrating Atlas into your GitHub Actions workflow allows for powerful automation of Polars code review. Atlas is equipped with fundamental tools like `bash`, `read`, `grep`, and `edit`, enabling it to perform a wide range of tasks. For example, Atlas can be configured to automatically run `uv install` to manage dependencies, then execute `ruff format` on any modified Polars files to ensure consistent code style. After formatting, it can run your test suite using `pytest (assert_frame_equal)`, providing immediate feedback on potential regressions in your data transformations. All these operations are permission-gated; Atlas will ask for approval before executing commands that modify files or run tests. This allows Atlas to suggest concrete improvements, such as converting a `scan_csv` operation followed by a `filter` into a lazy chain, ensuring predicate pushdown reaches the reader for optimal performance. It can even print `explain()` on a Polars query plan, showing which projections were pruned before and after an optimization, helping you understand and approve its proposed changes.
Handling large Polars codebases and context overflow with Atlas
Atlas is designed to effectively manage large Polars codebases, employing AST-based indexing and hybrid semantic/keyword retrieval to navigate complex projects. It explicitly handles context overflow, catching `ContextOverflowError` and re-throwing it as a prompt-too-large message, listing the offending files for immediate attention, preventing 1 common failure mode.
Working with extensive Polars projects often involves numerous files and complex data pipelines, which can challenge traditional AI agents with context window limitations. Atlas addresses this by indexing code using AST declarations via tree-sitter, rather than relying on blind line windows. This allows for more precise and relevant code retrieval, fused by reciprocal rank fusion for hybrid semantic and keyword search. When dealing with large issues or pull requests that exceed the model's context window, Atlas doesn't silently fail. Instead, it explicitly catches a `ContextOverflowError` by name and re-throws it as a user-friendly 'prompt-too-large' message. This message clearly lists the offending files that contributed to the overflow, enabling Polars developers to quickly identify and address the source of the context issue. This proactive handling ensures that even in large-scale data processing projects, Atlas remains a reliable and transparent assistant, helping you maintain efficient `LazyFrame` operations and understand query plans without being hindered by context limitations.
Step by step
- 01Create a GitHub Actions workflow file, for example, `.github/workflows/atlas-triage.yml`, to define the automation trigger for your Polars project.
- 02Configure the `atlas github` command within the workflow, ensuring the `MODEL` input is set in `provider/model` format (e.g., `ollama/llama3`) and the `PROMPT` input is provided for relevant event types.
- 03Define the workflow to trigger on specific GitHub events like `issue_comment` or `pull_request_target`, ensuring Atlas can respond to relevant Polars-related interactions.
- 04Implement Atlas's built-in permission checks by ensuring the triggering actor has `admin` or `write` permissions on the Polars repository before any actions are taken.
- 05Enforce the mention requirement in your workflow configuration, so Atlas only runs when explicitly mentioned in a comment (e.g., `@atlas triage`), preventing accidental runs on your Polars code.
- 06Allow Atlas to propose code style fixes by running `ruff format` on Polars files, with the changes presented as a unified diff for your approval.
- 07Enable Atlas to validate proposed changes by running `pytest (assert_frame_equal)` on your Polars test suite, ensuring data integrity and correct `LazyFrame` behavior.
- 08Review Atlas's proposed changes, which are presented as git patches, and approve them to be staged and committed to your Polars project.
Frequently asked questions
- How does Atlas ensure safety when proposing changes to Polars code?
- Atlas ensures safety through permission gating, requiring `admin` or `write` access. It drafts plans in a read-only agent, presents unified diffs for approval, and snapshots changes as git patches, giving you full control over any modifications to your Polars code.
- Can Atlas automatically format Polars code using `ruff format`?
- Yes, Atlas can be configured to run `ruff format` on Polars files within a GitHub workflow. It will present the formatted changes as a diff for your review and approval before writing them to the repository.
- How does Atlas handle test failures in a Polars project?
- Atlas can execute your Polars test suite using `pytest (assert_frame_equal)` behind a permission prompt. If tests fail, Atlas can analyze the output and propose fixes, which you can then review and approve as unified diffs.
- What Polars-specific optimizations can Atlas suggest?
- Atlas can suggest Polars-specific optimizations, such as converting a `scan_csv` followed by a `filter` into a lazy chain to enable predicate pushdown. It can also print `explain()` on query plans to show projection pruning.
- How does Atlas manage dependencies in a Polars project?
- Atlas integrates with your project's `pyproject.toml` and can use `uv` to install and manage dependencies. This ensures that any automated tasks or code modifications are performed within the correct Polars environment.
- What happens if a Polars issue or PR is too large for Atlas's context window?
- Atlas explicitly catches `ContextOverflowError` and re-throws it as a 'prompt-too-large' message, listing the specific Polars files that caused the overflow. This allows you to address the context issue directly.
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