How does Atlas handle large scikit-learn codebases?

Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and uses hybrid semantic and keyword retrieval. This allows it to efficiently search and retrieve relevant scikit-learn code snippets, even in projects with thousands of files, focusing on meaning rather than just text.

Can Atlas help me understand custom scikit-learn estimators?

Yes, Atlas can read your custom estimator definitions, including `fit`, `transform`, and `predict` methods. You can use `codebase_search` to find them and `lsp goToDefinition` to work through their implementations, helping you understand how `get_params` and `set_params` are correctly implemented to pass `check_estimator`.

How does Atlas integrate with my existing scikit-learn development tools?

Atlas is designed to integrate direct. It recognizes `pyproject.toml` for pinning scikit-learn versions and allows you to run your existing `pytest` tests and apply `ruff format` to diffs directly through its `bash` tool, ensuring your familiar scikit-learn toolchain remains central.

Can Atlas help me identify common scikit-learn bugs like data leakage?

Yes. Atlas can be asked to identify and even fix classic leakage bugs, such as a scaler that was fit on the full dataset outside a `Pipeline`. It will propose moving the scaler inside the `Pipeline`, presenting a diff for your review and approval.

What if I need to explore a scikit-learn repository without making any changes?

You can delegate wide sweeps to the `explore` subagent using the `task` tool. This subagent operates with a deny-by-default permission set, allowing it to `grep`, `glob`, `read`, and `websearch` without any ability to modify files, ensuring a safe, read-only exploration of your scikit-learn project.

How does Atlas help me track what I've learned during onboarding?

Atlas provides the `todowrite` tool, allowing you to record observations, open questions, and insights as you build your mental model of the scikit-learn codebase. This ensures that your learnings are preserved and accessible for subsequent turns or future reference.

Onboard to an Unfamiliar scikit-learn Codebase with Atlas in 2026

To build a working mental model of an unfamiliar scikit-learn repository without reading every file, Atlas provides a terminal-native AI coding agent that starts from meaning, not filenames, integrating directly with your `pytest`, `uv`, and `ruff format` toolchain. In 2026, Atlas leverages its semantic index, built from AST declarations, to quickly surface relevant code snippets, allowing you to grasp complex interactions within `Pipeline` and `ColumnTransformer` definitions efficiently. This approach ensures you focus only on the files that truly matter, accelerating your understanding of the codebase's core logic and structure.

How do I semantically search a scikit-learn codebase with Atlas?

In 2026, Atlas helps you quickly find relevant code in an unfamiliar scikit-learn repository by starting with a plain-language question, leveraging its semantic index built from AST declarations. This approach means you don't need to know exact filenames or keywords, as Atlas understands the underlying concepts of your `Pipeline` or custom estimator.

Atlas's `codebase_search` tool is your entry point for understanding an unfamiliar scikit-learn project. Instead of guessing file names, you can ask a question like 'how are requests authenticated?' or 'where is the custom `ColumnTransformer` defined that handles categorical features?'. Atlas queries its hybrid semantic and keyword retrieval index, fused by reciprocal rank fusion, to return ranked snippets with file paths. This index is built locally using Ollama embeddings, ensuring your proprietary scikit-learn code remains off third-party servers. For instance, if you're looking for how a specific `Pipeline` handles data preprocessing, `codebase_search` will pinpoint the exact `Pipeline` definition and related components, even if they are spread across multiple files like `src/sklearn_project/pipelines.py` and `src/sklearn_project/transformers/custom_transformer.py`.

How do I understand scikit-learn package layout and naming conventions?

After an initial semantic search, understanding the overall directory structure of a scikit-learn project is crucial, and Atlas's `glob` tool provides this overview in seconds. By running `glob` on the top-level directories, you can quickly discern the package layout and naming conventions, which is vital for navigating projects with 100s of files.

Once `codebase_search` has given you initial pointers, use the `glob` tool to get a high-level map of the repository. Running `glob` on the top-level directories, such as `src/sklearn_project/*`, reveals the package structure without opening any files. This helps you identify common scikit-learn patterns, like `estimators/`, `preprocessing/`, or `model_selection/` directories. For example, you might see `src/sklearn_project/estimators/custom_model.py` or `src/sklearn_project/preprocessing/feature_scaler.py`. This quick overview helps you anticipate where `Pipeline` definitions, custom `ColumnTransformer` implementations, or specific estimator API contracts (`fit`, `transform`, `predict`) might reside, informing your subsequent targeted reads and explorations.

How do I deep dive into scikit-learn code with Atlas's LSP integration?

To build a detailed mental model of specific scikit-learn components, Atlas allows you to `read` the two or three files ranked highest by `codebase_search`, then follow imports using the `lsp` tool's `goToDefinition` operation. This targeted approach helps you trace the flow of data through `Pipeline` steps or understand complex `ColumnTransformer` logic in under 5 minutes.

After identifying key files with `codebase_search` and understanding the layout with `glob`, use the `read` tool to inspect the most relevant scikit-learn source files. For instance, you might `read src/sklearn_project/pipelines.py` to see how a `Pipeline` is constructed. From there, if you encounter an unfamiliar custom estimator or `ColumnTransformer` import, Atlas's `lsp` tool becomes invaluable. You can use `lsp goToDefinition` on an imported class like `CustomEstimator` to jump directly to its definition, perhaps in `src/sklearn_project/estimators/custom_estimator.py`. This allows you to quickly understand its `fit`, `transform`, and `predict` methods, or how `get_params` and `set_params` are implemented to pass `check_estimator` tests, without manually searching the entire codebase.

How do I delegate wide sweeps and ensure safety in scikit-learn exploration?

For broader explorations across a scikit-learn codebase, Atlas lets you delegate wide sweeps to the `explore` subagent through the `task` tool, which operates with a deny-by-default permission set. This ensures that while the subagent can `grep` for patterns or `read` many files, it cannot inadvertently modify your project, providing a safe environment for discovery in 2026.

When you need to perform a wider search, such as finding all instances of a specific `Pipeline` step or identifying all custom estimators that don't implement `get_params`, delegate this work to the `explore` subagent using the `task` tool. The `explore` subagent is designed for read-only operations, defined with a deny-by-default permission set that only allows `grep`, `glob`, `read`, `bash`, `webfetch`, and `websearch`. This means it can safely look around, run `grep -r 'check_estimator'` across the `tests/` directory, or `glob` for all `*.py` files in `src/sklearn_project/estimators/`, without any risk of altering your scikit-learn project. Every Atlas tool call is permission-gated against allow, ask, and deny rules, and the `explore` subagent's strict permissions provide an additional layer of safety, especially when dealing with unfamiliar code.

How do I record learnings and review changes in a scikit-learn project?

As you build your mental model of a scikit-learn codebase, Atlas allows you to record what you've learned as a `todowrite` list, ensuring open questions and insights survive into the next turn. When Atlas proposes changes, such as moving a scaler inside a `Pipeline` to fix a leakage bug, it computes a unified diff for every file edit, surfacing it for approval before writing, providing 100% transparency.

Throughout your onboarding process, use the `todowrite` tool to jot down observations, questions, or potential areas for improvement. This helps consolidate your understanding of the scikit-learn project's `Pipeline` structures, `ColumnTransformer` logic, or estimator API implementations. For example, you might note 'TODO: Investigate why `StandardScaler` is fit on full dataset outside `Pipeline` in `src/sklearn_project/preprocessing.py`'. When Atlas assists with tasks, like refactoring to fix a classic leakage bug by moving a scaler into a `Pipeline`, it first drafts a plan in a read-only plan agent and asks for approval before switching to a build agent. Crucially, Atlas computes a unified diff for every file edit and surfaces it for your approval before writing any changes to disk. This ensures you have full control and visibility over any proposed modifications, such as changes to `pyproject.toml` or `src/sklearn_project/pipelines.py`, allowing you to review and understand every line before it's committed.

Step by step

01**Start with a semantic query:** Ask Atlas's `codebase_search` a plain-language question about a scikit-learn concept, like "how does the `Pipeline` handle feature scaling?" to get ranked snippets.
02**Map the package layout:** Run `glob src/sklearn_project/*` to understand the top-level directory structure and identify common scikit-learn patterns like `estimators/` or `preprocessing/`.
03**Deep dive into key files:** `read` the top 2-3 files identified by `codebase_search`, then use `lsp goToDefinition` on any unfamiliar `Pipeline` steps or custom `ColumnTransformer` imports.
04**Delegate broad exploration:** Use `task explore` to safely `grep` for all instances of `check_estimator` in the `tests/` directory or `glob` for all `*.py` files implementing `fit` and `predict` methods.
05**Record your findings:** Document open questions or insights about the scikit-learn codebase using `todowrite`, such as "TODO: Verify `get_params` and `set_params` for `CustomEstimator`."
06**Review and approve changes:** If Atlas proposes a fix, like moving a scaler into a `Pipeline` to prevent data leakage, review the unified diff and approve the changes before Atlas writes them.
07**Run tests and format:** After any changes, execute `bash pytest tests/` to verify functionality and `bash ruff format src/sklearn_project/` to ensure code style consistency.

Frequently asked questions

How does Atlas handle large scikit-learn codebases?: Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and uses hybrid semantic and keyword retrieval. This allows it to efficiently search and retrieve relevant scikit-learn code snippets, even in projects with thousands of files, focusing on meaning rather than just text.
Can Atlas help me understand custom scikit-learn estimators?: Yes, Atlas can read your custom estimator definitions, including `fit`, `transform`, and `predict` methods. You can use `codebase_search` to find them and `lsp goToDefinition` to work through their implementations, helping you understand how `get_params` and `set_params` are correctly implemented to pass `check_estimator`.
Is my scikit-learn code safe with Atlas?: Absolutely. Atlas builds its code index with local Ollama embeddings, keeping your scikit-learn code off third-party servers. Every Atlas tool call is permission-gated against allow, ask, and deny rules, and all proposed file edits are presented as a unified diff for your explicit approval before writing.
How does Atlas integrate with my existing scikit-learn development tools?: Atlas is designed to integrate direct. It recognizes `pyproject.toml` for pinning scikit-learn versions and allows you to run your existing `pytest` tests and apply `ruff format` to diffs directly through its `bash` tool, ensuring your familiar scikit-learn toolchain remains central.
Can Atlas help me identify common scikit-learn bugs like data leakage?: Yes. Atlas can be asked to identify and even fix classic leakage bugs, such as a scaler that was fit on the full dataset outside a `Pipeline`. It will propose moving the scaler inside the `Pipeline`, presenting a diff for your review and approval.
What if I need to explore a scikit-learn repository without making any changes?: You can delegate wide sweeps to the `explore` subagent using the `task` tool. This subagent operates with a deny-by-default permission set, allowing it to `grep`, `glob`, `read`, and `websearch` without any ability to modify files, ensuring a safe, read-only exploration of your scikit-learn project.
How does Atlas help me track what I've learned during onboarding?: Atlas provides the `todowrite` tool, allowing you to record observations, open questions, and insights as you build your mental model of the scikit-learn codebase. This ensures that your learnings are preserved and accessible for subsequent turns or future reference.

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