How does Atlas prevent accidental edits in my Polars project during planning?

Atlas's plan agent operates with a strict permission set that denies all edit tools for your Polars codebase. It only allows writing to the designated plan markdown file, ensuring no accidental modifications occur while you research complex `LazyFrame` structures.

Can Atlas help me understand complex Polars LazyFrame query plans?

Yes, Atlas can be asked to print `explain()` on your Polars query plans, showing which projections got pruned or predicates pushed down. This helps in understanding and optimizing `LazyFrame` chains and `expression API` usage before implementation.

What Polars-specific tools does Atlas integrate with for testing and formatting?

Atlas integrates directly with your Polars toolchain. It can run `pytest` for testing, specifically leveraging `assert_frame_equal` for Polars DataFrames, and format code diffs using `ruff format` to maintain consistent style.

How does Atlas ensure my Polars code stays private when using its search features?

Atlas can build its code index with local Ollama embeddings, keeping your Polars code off third-party servers. This ensures your proprietary `LazyFrame` logic and data processing remains private and secure during all research activities.

What happens after I finish planning my multi-file Polars change with Atlas?

After drafting your plan, you call `atlas plan_exit`. Atlas will ask for confirmation to switch to the build agent. Answering 'Yes' transitions you to implementation, where Atlas can then apply the changes, run `pytest (assert_frame_equal)`, and format with `ruff format`.

Can Atlas help me refactor collect() calls in my Polars pipelines?

Yes, Atlas can read your `LazyFrame` chains and expression contexts, identifying where each `.collect()` actually materializes. It can then assist in converting operations like `scan_csv` plus filter into lazy chains, pushing predicates down to the reader for better performance.

How does Atlas handle code review for multi-file Polars changes?

Atlas facilitates review by first requiring a detailed plan in markdown, which can be shared. Once in the build agent, every proposed file edit generates a unified diff for your approval before writing, and changes are snapshotted as git patches for easy review and rollback of Polars code.

Plan a multi-file change before editing in Polars with Atlas in 2026

Atlas empowers Polars developers in 2026 to design multi-file changes and get them reviewed before modifying a single line of code, leveraging its dedicated plan agent. This workflow integrates directly with your existing Polars toolchain, including `pytest (assert_frame_equal)` for testing, `uv` for package management, and `ruff format` for consistent code style.

How does Atlas plan multi-file Polars changes?

Atlas's plan agent provides a read-only environment for Polars developers in 2026, ensuring that research and design cannot accidentally lead to code modifications. This agent operates with a strict permission set, allowing writes only to a designated plan markdown file, typically under `.atlas/plans/*.md`.

The Atlas plan agent is specifically designed to facilitate the design phase of complex Polars refactors. It operates in a read-only mode for your entire codebase, with the sole exception of a markdown file within the `.atlas/plans/` directory. This strict permission gating means you can confidently use Atlas's powerful research tools like `codebase_search`, `grep`, `read`, and `lsp` without any risk of inadvertently altering your Polars `LazyFrame` definitions or `expression API` calls. For a Polars developer, understanding the full data flow and optimization potential across multiple files is crucial before making changes. Atlas's ability to index code by AST declarations using tree-sitter, rather than blind line windows, provides a deeper understanding of your Polars project's structure, making the planning phase more precise and less error-prone.

How to research Polars code with Atlas before editing?

To effectively plan a multi-file change in a Polars project, Atlas provides powerful research tools that remain fully functional within the plan agent. In 2026, you can use `codebase_search` for semantic and keyword retrieval, `grep` for pattern matching, `read` for file content, and `lsp` for language server protocol queries.

Atlas's research capabilities are essential for understanding the scope and impact of a multi-file Polars change. `codebase_search` uses hybrid semantic and keyword retrieval fused by reciprocal rank fusion to help you locate all relevant `LazyFrame` definitions, `expression API` usages, or `collect()` calls across your entire project. For instance, you might search for 'DataFrame schema definition' or 'predicate pushdown logic'. `grep` allows you to find specific patterns, such as `pl.scan_csv` or `pl.col("*")`, across many files. The `read` tool lets you inspect the contents of specific Polars `.py` files or configuration files like `pyproject.toml`. Furthermore, the `lsp` tool provides language server protocol capabilities, allowing you to query type information or find definitions for complex Polars expressions. Atlas can build its code index with local Ollama embeddings, ensuring your proprietary Polars logic remains off third-party servers during this critical research phase.

Where does Atlas store my Polars change plan?

Atlas ensures that your multi-file Polars change plan is meticulously documented within a dedicated markdown file, typically found at `.atlas/plans/my_polars_refactor_plan.md`. This is the *only* location where the plan agent is permitted to write, providing a secure and auditable record of your proposed modifications before any actual code is touched in 2026.

The plan markdown file serves as your blueprint for the multi-file Polars change. Within this file, you will detail the proposed modifications to your `LazyFrame` chains, `expression API` usage, or the strategic placement of `collect()` calls. This documentation is crucial for review, allowing team members to understand the design rationale and potential impact before any implementation begins. The plan agent's strict permission set guarantees that all your research and design efforts are channeled into this single, designated document, preventing any accidental writes to your actual Polars source code. Once the plan is complete and reviewed, it becomes the guiding document for the build agent, ensuring a structured and controlled implementation process.

How does Atlas ensure safe Polars multi-file changes?

Atlas prioritizes safety and review for Polars multi-file changes through its permission-gated tool calls and distinct plan and build agents. The plan agent, active in 2026, strictly disallows all edit tools, preventing accidental modifications. Every Atlas tool call is permission-gated, requiring explicit allow, ask, or deny rules before execution, ensuring control over your Polars codebase.

Safety is paramount when dealing with multi-file changes in a Polars codebase, especially when refactoring complex `LazyFrame` pipelines. Atlas's design inherently promotes a safe workflow. The plan agent's read-only nature for source code is the first line of defense. Beyond this, every Atlas tool call is permission-gated, meaning you have explicit control over what actions Atlas can take. When your plan is finalized, the `plan_exit` tool explicitly asks for your confirmation to switch from the read-only plan agent to the build agent. This critical step ensures that the plan has been thoroughly reviewed and approved before any implementation begins. Once in the build agent, Atlas computes a unified diff for every proposed file edit and surfaces it for your approval before writing, and it snapshots file changes as git patches, allowing for easy diffing and rollback if needed. This layered approach provides robust safeguards for your Polars project.

Step by step

01Start Atlas in your Polars project, ensuring your `pyproject.toml` pins Polars.
02Switch to the plan agent by typing `atlas plan` to enter the read-only design environment.
03Use `atlas codebase_search "LazyFrame definition"` to locate relevant Polars data structures across your project.
04Employ `atlas grep "pl.scan_csv"` to identify all data ingestion points that might need modification.
05Examine specific Polars files with `atlas read src/data_processing/pipeline.py` to understand `LazyFrame` chains and `expression API` usage.
06Draft your multi-file change plan in `.atlas/plans/my_polars_refactor.md`, detailing proposed `expression API` adjustments and `collect()` placements.
07Call `atlas plan_exit` to signal the plan is complete and request to switch to the build agent.
08Review the plan and confirm 'Yes' to proceed to implementation, where Atlas will use `pytest (assert_frame_equal)` for validation and `ruff format` for style.

Frequently asked questions

How does Atlas prevent accidental edits in my Polars project during planning?: Atlas's plan agent operates with a strict permission set that denies all edit tools for your Polars codebase. It only allows writing to the designated plan markdown file, ensuring no accidental modifications occur while you research complex `LazyFrame` structures.
Can Atlas help me understand complex Polars LazyFrame query plans?: Yes, Atlas can be asked to print `explain()` on your Polars query plans, showing which projections got pruned or predicates pushed down. This helps in understanding and optimizing `LazyFrame` chains and `expression API` usage before implementation.
What Polars-specific tools does Atlas integrate with for testing and formatting?: Atlas integrates directly with your Polars toolchain. It can run `pytest` for testing, specifically leveraging `assert_frame_equal` for Polars DataFrames, and format code diffs using `ruff format` to maintain consistent style.
How does Atlas ensure my Polars code stays private when using its search features?: Atlas can build its code index with local Ollama embeddings, keeping your Polars code off third-party servers. This ensures your proprietary `LazyFrame` logic and data processing remains private and secure during all research activities.
What happens after I finish planning my multi-file Polars change with Atlas?: After drafting your plan, you call `atlas plan_exit`. Atlas will ask for confirmation to switch to the build agent. Answering 'Yes' transitions you to implementation, where Atlas can then apply the changes, run `pytest (assert_frame_equal)`, and format with `ruff format`.
Can Atlas help me refactor collect() calls in my Polars pipelines?: Yes, Atlas can read your `LazyFrame` chains and expression contexts, identifying where each `.collect()` actually materializes. It can then assist in converting operations like `scan_csv` plus filter into lazy chains, pushing predicates down to the reader for better performance.
How does Atlas handle code review for multi-file Polars changes?: Atlas facilitates review by first requiring a detailed plan in markdown, which can be shared. Once in the build agent, every proposed file edit generates a unified diff for your approval before writing, and changes are snapshotted as git patches for easy review and rollback of Polars code.

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