Atlas empowers Polars developers in 2026 to generate precise module documentation by reading live source code, ensuring READMEs reflect current functionality rather than historical intent. It integrates directly with the Polars toolchain, using `lsp` to enumerate APIs, `read` to understand implementations, and `write` to produce documentation. Atlas verifies code samples with `bash` and respects your project's setup, including `pytest (assert_frame_equal)` for testing, `uv` for package management, and `ruff format` for code style.
How Atlas enumerates and understands Polars module APIs
Atlas precisely enumerates a Polars module's public API in 2026 by leveraging the `lsp` tool's `documentSymbol` operation. This ensures that every exported function or class is captured, preventing any omissions or invented interfaces in the generated documentation.
To create accurate documentation for a Polars module, Atlas first uses the `lsp` tool's `documentSymbol` operation. This capability allows Atlas to inspect the live codebase and identify all public API declarations, such as functions, classes, and methods within a Polars project. Unlike relying on comments or outdated specifications, this approach guarantees that the documentation reflects the actual, current public surface of the module. After enumerating the API, Atlas employs the `read` tool to delve into the implementation details of each identified export. This deep dive is crucial for understanding the nuances of Polars' expression API and lazy query optimizer. Atlas can trace `LazyFrame` chains, identify expression contexts, and pinpoint where `.collect()` calls materialize data. Furthermore, the `codebase_search` tool is used to find real-world examples of how these exports are utilized by callers within the codebase. This combination of API enumeration, implementation reading, and usage analysis ensures that the generated README describes the module's behavior as it exists today, not merely as it was conceived a year ago. Atlas operates within your project's `pyproject.toml` context, understanding your Polars version and dependencies managed by `uv`.
Generating and verifying Polars READMEs with real code samples
Atlas generates a new README for your Polars module by first analyzing existing documentation with `grep` to match style and tone. It then uses the `write` tool to produce the README, incorporating real Polars code signatures and file paths, with 100% of code samples verified by `bash` execution.
When generating a README for a Polars module, Atlas prioritizes consistency and accuracy. Before writing new content, Atlas uses the `grep` tool to search the repository for existing `README.md` files or similar documentation. This step allows Atlas to understand and match the established heading structure, tone, and formatting conventions of your project, ensuring the new documentation feels native rather than an alien addition. Once the structural and stylistic context is established, Atlas employs the `write` tool to draft the README. This draft is rich with concrete details, quoting real Polars function signatures, actual file paths, and specific examples of how to use the module's API. A critical aspect of this process is the verification of every code sample. Atlas uses the `bash` tool to execute each code snippet embedded in the documentation. This rigorous verification step ensures that every example, whether it's a `scan_csv` operation followed by a filter or a complex `LazyFrame` transformation, is runnable and produces the expected output. A code sample that has never been executed is a liability, and Atlas eliminates this risk, providing Polars developers with documentation that is not only descriptive but also demonstrably correct in 2026.
Ensuring safety and traceability in Polars documentation edits
Atlas incorporates multiple safety mechanisms to protect your Polars codebase during documentation generation, including permission-gated tool calls and a read-only plan agent. Every proposed edit is presented as a unified diff for approval, ensuring 100% transparency before any changes are written to disk.
Atlas is designed with robust safety features to provide Polars developers with confidence when generating or updating documentation. Every tool call made by Atlas, such as `lsp`, `read`, `write`, or `bash`, is permission-gated. This means you explicitly grant 'allow', 'ask', or 'deny' rules, maintaining full control over Atlas's actions. Before any modifications are made, Atlas drafts a comprehensive plan in a read-only plan agent. This agent outlines the steps Atlas intends to take, allowing you to review and approve the strategy before it switches to a build agent that can make changes. For every file edit, Atlas computes and surfaces a unified diff, clearly showing what will be added, removed, or modified. This diff is presented for your explicit approval, ensuring that you understand and agree with every proposed change to your Polars project's documentation or code. Furthermore, Atlas integrates deeply with Git, reading branches, status, and diffs. It can stage and create commits on your behalf, and it snapshots file changes as Git patches, enabling easy diffing and rolling back of edits if needed. This comprehensive safety framework, combined with the ability to run `pytest (assert_frame_equal)` and format code with `ruff format` behind permission prompts, ensures that documentation updates are both accurate and secure in 2026.
Optimizing Polars query plans and verifying documentation
Atlas helps Polars developers optimize query plans by reading `LazyFrame` chains and showing how predicate pushdown impacts performance. It can print `explain()` on query plans, revealing projections pruned before and after, ensuring documentation reflects efficient Polars idioms in 2026.
Beyond just documenting, Atlas assists Polars developers in understanding and optimizing their code, which in turn leads to more accurate and insightful documentation. Atlas is capable of reading your `LazyFrame` chains and expression contexts, understanding where each `.collect()` operation actually materializes data. This deep understanding allows Atlas to suggest or demonstrate optimizations, such as converting a `scan_csv` operation followed by a filter into a lazy chain, ensuring that predicate pushdown reaches the reader for maximum efficiency. Atlas can also print `explain()` on the query plan, providing a visual representation of how Polars processes data. This feature is invaluable for documentation, as it allows Atlas to show concrete examples of which projections got pruned before and after optimizations, illustrating the benefits of Polars' lazy evaluation. By integrating this analytical capability, the generated READMEs can include not just 'what' the code does, but also 'how' it does it efficiently, reflecting best practices for Polars in 2026. This ensures that the documentation is not just a static description but a living guide to high-performance Polars development.
Step by step
- 01Run Atlas in your Polars project, ensuring a `pyproject.toml` pins Polars and dependencies are managed by `uv`.
- 02Ask Atlas to enumerate the module's public API using the `lsp` tool's `documentSymbol` operation to capture all exports.
- 03Instruct Atlas to `read` the implementation of each Polars export and use `codebase_search` to find how callers actually use it in practice, especially observing `LazyFrame` chains and `.collect()` calls.
- 04Have Atlas `grep` the repository for an existing `README.md` to match heading structure and tone for the new documentation.
- 05Ask Atlas to `write` the README with the `write` tool, quoting real Polars signatures, file paths, and examples of `scan_csv` or `LazyFrame` operations.
- 06Verify every Polars code sample in the generated documentation by having Atlas run it with the `bash` tool, ensuring 100% executability.
- 07Review Atlas's proposed changes as a unified diff, ensuring all Polars-specific details and code samples are accurate.
- 08Approve the changes, allowing Atlas to stage and create a commit on your behalf, optionally running `pytest (assert_frame_equal)` and formatting with `ruff format`.
Frequently asked questions
- How does Atlas ensure Polars documentation is always up-to-date?
- Atlas ensures Polars documentation is current by reading the live source code using the `lsp` tool's `documentSymbol` operation and the `read` tool, rather than relying on outdated comments or memory. This guarantees the README reflects the code's actual behavior in 2026.
- Can Atlas verify Polars code examples in the documentation?
- Yes, Atlas rigorously verifies Polars code examples. It uses the `bash` tool to execute every code sample embedded in the documentation, ensuring that `LazyFrame` chains, `scan_csv` operations, and other Polars snippets are fully runnable and correct.
- What Polars-specific tools does Atlas integrate with?
- Atlas integrates direct with the Polars ecosystem. It uses `pytest (assert_frame_equal)` for testing, `uv` for package management, and `ruff format` for code formatting, ensuring a familiar and efficient workflow for Polars developers.
- How does Atlas handle Polars' lazy execution and query optimization?
- Atlas deeply understands Polars' lazy execution. It reads `LazyFrame` chains, expression contexts, and `collect()` calls. Atlas can also print `explain()` on query plans to show predicate pushdown and projection pruning, helping document optimized Polars code.
- Is my Polars codebase safe when Atlas generates documentation?
- Your Polars codebase is safe with Atlas. All tool calls are permission-gated, a read-only plan agent drafts changes, and every file edit is presented as a unified diff for your explicit approval. Atlas also uses Git for snapshots and rollbacks.
- How does Atlas determine the style and tone for a new Polars README?
- Atlas determines the style and tone for a new Polars README by using the `grep` tool to search your repository for existing `README.md` files. It analyzes these files to match the established heading structure, formatting, and overall tone, ensuring consistency.
- Can Atlas help optimize Polars queries while documenting?
- Yes, Atlas can assist in optimizing Polars queries. It can convert `scan_csv` plus filter into lazy chains for predicate pushdown and print `explain()` on query plans to illustrate how projections are pruned, helping document efficient Polars practices.
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