How do I audit a large scikit-learn repository without blowing my context window?

Atlas uses parallel subagents, launched via the `task` tool, to sweep a scikit-learn repository. Each subagent runs in its own session, preventing its file dumps from entering the main context window, allowing you to audit extensive codebases like `sklearn/ensemble` efficiently.

Can Atlas modify my scikit-learn code during an audit?

When using `subagent_type explore` for an audit, Atlas subagents are deny-by-default and read-only, ensuring no modifications are made to your scikit-learn files or `pyproject.toml`. Any proposed changes in a `general` subagent or main session are permission-gated and require diff approval.

How does Atlas handle scikit-learn specific issues like data leakage?

Atlas can be instructed to identify scikit-learn specific problems, such as data leakage where a scaler is fit on the full dataset outside a `Pipeline`. It can then suggest moving the scaler inside the `Pipeline` definition, ensuring correct cross-validation and preventing this classic bug.

What scikit-learn tools does Atlas integrate with?

Atlas integrates directly with standard scikit-learn development tools. It can run `pytest` for testing, apply `ruff format` for code formatting, and manage packages with `uv`, all behind permission prompts for safety and control.

How do I split an audit of a scikit-learn codebase for parallel processing?

You can split a scikit-learn audit by logical units such as directories (e.g., `sklearn/preprocessing`, `sklearn/cluster`), specific packages, or even by a class of problem. This allows multiple Atlas subagents to work concurrently on distinct parts of the codebase.

What happens if an Atlas subagent fails during a scikit-learn audit?

If an Atlas subagent fails during a scikit-learn audit, the `task` tool will surface the child's error text verbatim. This allows you to diagnose and address the specific issue, whether it's a code problem or a subagent instruction error, directly from your main session.

How does Atlas ensure my scikit-learn code stays local during an audit?

Atlas can build its code index with local Ollama embeddings, ensuring your scikit-learn code never leaves your local machine and remains off third-party servers during the audit process. This provides a secure and private environment for your development.

Audit a scikit-learn Repo with Parallel Subagents in Atlas (2026)

Atlas enables scikit-learn developers in 2026 to audit an entire repository for specific problems, such as data leakage in Pipelines or incorrect estimator implementations, by fanning out work to parallel subagents. This approach prevents the main session's context window from being overwhelmed, allowing you to sweep large codebases efficiently and then apply fixes using `ruff format` after `pytest` validation.

How Atlas Audits scikit-learn Repos with Parallel Subagents

Atlas uses parallel subagents to audit scikit-learn repositories in 2026, allowing developers to sweep large codebases for issues like data leakage across 100s of files without overwhelming the main session's context window. This is achieved by launching independent subagent sessions that return only their conclusions.

When auditing a scikit-learn repository, the primary challenge is often the sheer volume of code and the risk of exceeding the main AI agent's context window. Atlas addresses this by leveraging its `task` tool to launch multiple subagents concurrently. Each subagent operates in its own isolated session, meaning its internal file dumps and intermediate thoughts never enter the main session's context. Only the final conclusion or error message from each subagent is returned to the main Atlas session. This architecture is ideal for sweeping extensive scikit-learn codebases, allowing you to inspect `Pipeline` definitions, `ColumnTransformer` configurations, and custom estimators for common pitfalls like data leakage without performance degradation.

Splitting a scikit-learn Audit for Parallel Execution

To effectively audit a scikit-learn repository, the work is split into independent slices,perhaps by directory, package, or specific rule,allowing 2 or more subagents to run concurrently. This prevents overlap and ensures each subagent focuses on a distinct part of the codebase, maximizing efficiency.

The key to a successful parallel audit in scikit-learn with Atlas is to divide the repository into manageable, non-overlapping slices. For instance, you might assign one subagent to audit the `sklearn/ensemble` directory for estimator API contract violations, another to `sklearn/preprocessing` for data leakage issues, and a third to a custom `my_project/estimators.py` file. This segmentation can be based on file paths, specific scikit-learn components (e.g., all `Pipeline` definitions), or even a particular class of problem you are searching for. The `glob` tool can assist in identifying these distinct slices, ensuring that each subagent has a clear, independent scope.

Launching Concurrent Read-Only Audits in scikit-learn

Atlas launches concurrent read-only audit tasks for scikit-learn codebases using the `task` tool with `subagent_type explore`, ensuring no changes are made to your `pyproject.toml` or source files during the sweep. This allows for safe, parallel inspection of 5 or more distinct code paths simultaneously.

For auditing scikit-learn repositories, the `explore` subagent type is crucial. It is deny-by-default and read-only, making it the perfect choice for sweeping a codebase without any risk of accidental modifications. You can launch multiple `task` calls in quick succession, and Atlas will execute them concurrently. For example, to audit different parts of a scikit-learn project, you might issue commands like: `atlas task --subagent-type explore "Audit sklearn/linear_model for `fit` method issues"` followed by `atlas task --subagent-type explore "Check sklearn/preprocessing for data leakage in Pipelines"`. These tasks will run in parallel, with each subagent focusing on its assigned segment, providing a comprehensive yet safe audit.

Reviewing and Merging scikit-learn Audit Findings

After parallel subagents complete their audit of a scikit-learn repository, Atlas collects each subagent's final message, including any verbatim error text if a task failed, allowing you to review up to 10 concurrent findings. These conclusions are then merged into a unified `todowrite` list for action.

Once all parallel subagents have finished their assigned scikit-learn audit tasks, Atlas consolidates their findings. The `task` tool is designed to surface the final message from each child subagent. If a subagent encountered an issue or failed to complete its task, its error text is presented verbatim, providing immediate insight into the problem. You can then review these individual conclusions, identify recurring patterns or critical issues, and merge them into a single `todowrite` list within your main Atlas session. This centralized list serves as your actionable plan for addressing the identified problems, such as fixing a `get_params` implementation in a custom estimator or correcting a cross-validation split.

Ensuring Safety and Permissions in scikit-learn Audits

Atlas ensures safety during scikit-learn repository audits through permission-gated tool calls and a read-only `explore` subagent type, preventing unintended modifications to critical files like `sklearn/base.py` or your `pyproject.toml`. Every proposed edit is surfaced as a unified diff for approval before writing, offering 100% control.

Safety is paramount when auditing a scikit-learn codebase. Atlas provides multiple layers of protection. The `explore` subagent type is inherently read-only, guaranteeing that no changes are made during the sweep. For any actions that might modify files, such as fixing a data leakage bug in `sklearn/preprocessing/data.py` or adjusting a `Pipeline` definition, Atlas employs a strict permission system. Every tool call is permission-gated against allow, ask, and deny rules. Furthermore, Atlas drafts a plan in a read-only plan agent before switching to a build agent, and computes a unified diff for every file edit. This diff is surfaced for your explicit approval before any changes are written to disk, giving you complete control over modifications to your scikit-learn project, including running `pytest` or applying `ruff format`.

Step by step

01Ensure your scikit-learn project has a `pyproject.toml` and run Atlas within the project root.
02Split your scikit-learn codebase audit into independent slices, for example, by directory like `sklearn/linear_model` or `sklearn/cluster`.
03Launch concurrent read-only audit tasks for each slice using `atlas task --subagent-type explore "Sweep sklearn/linear_model for `fit` method issues"`.
04Issue multiple `atlas task` calls together to run them concurrently, such as `atlas task --subagent-type explore "Check sklearn/preprocessing for data leakage"` and `atlas task --subagent-type explore "Verify custom estimators in my_project/estimators.py"`.
05Collect the final messages from each subagent, noting any verbatim error text if a task failed or was cancelled.
06Merge the findings into a `todowrite` list in your main Atlas session, for example, `atlas todowrite add "Fix leakage in StandardScaler in sklearn/preprocessing/data.py"`.
07Use `atlas edit` to apply fixes, such as moving a scaler that was fit on the full dataset inside a `Pipeline` definition.
08Run `pytest` to validate changes, e.g., `atlas ask "run pytest sklearn/preprocessing/tests/test_data.py"`.
09Apply `ruff format` to the diff, e.g., `atlas ask "apply ruff format to the diff"`.
10Review the unified diff for approval before Atlas writes the changes to your scikit-learn codebase.

Frequently asked questions

How do I audit a large scikit-learn repository without blowing my context window?: Atlas uses parallel subagents, launched via the `task` tool, to sweep a scikit-learn repository. Each subagent runs in its own session, preventing its file dumps from entering the main context window, allowing you to audit extensive codebases like `sklearn/ensemble` efficiently.
Can Atlas modify my scikit-learn code during an audit?: When using `subagent_type explore` for an audit, Atlas subagents are deny-by-default and read-only, ensuring no modifications are made to your scikit-learn files or `pyproject.toml`. Any proposed changes in a `general` subagent or main session are permission-gated and require diff approval.
How does Atlas handle scikit-learn specific issues like data leakage?: Atlas can be instructed to identify scikit-learn specific problems, such as data leakage where a scaler is fit on the full dataset outside a `Pipeline`. It can then suggest moving the scaler inside the `Pipeline` definition, ensuring correct cross-validation and preventing this classic bug.
What scikit-learn tools does Atlas integrate with?: Atlas integrates directly with standard scikit-learn development tools. It can run `pytest` for testing, apply `ruff format` for code formatting, and manage packages with `uv`, all behind permission prompts for safety and control.
How do I split an audit of a scikit-learn codebase for parallel processing?: You can split a scikit-learn audit by logical units such as directories (e.g., `sklearn/preprocessing`, `sklearn/cluster`), specific packages, or even by a class of problem. This allows multiple Atlas subagents to work concurrently on distinct parts of the codebase.
What happens if an Atlas subagent fails during a scikit-learn audit?: If an Atlas subagent fails during a scikit-learn audit, the `task` tool will surface the child's error text verbatim. This allows you to diagnose and address the specific issue, whether it's a code problem or a subagent instruction error, directly from your main session.
How does Atlas ensure my scikit-learn code stays local during an audit?: Atlas can build its code index with local Ollama embeddings, ensuring your scikit-learn code never leaves your local machine and remains off third-party servers during the audit process. This provides a secure and private environment for your development.

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