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

Atlas employs a dedicated plan agent whose permissions are strictly configured to deny all edit tools across your Flask codebase. The only exception is a specific plan markdown file (`.atlas/plans/*.md`), ensuring that your design phase is entirely read-only for your actual Flask application files like `app/models.py` or `app/routes.py`.

Can I use Atlas to understand my Flask blueprints and application factory?

Yes, Atlas is designed to understand Flask's core patterns. It indexes code by AST declarations using tree-sitter, allowing it to read your blueprints, extensions initialized in `create_app()`, and anything reaching for `current_app` or `g`. You can use `codebase_search` and `lsp` to explore these structures effectively.

What Flask-specific tools does Atlas integrate with for planning and implementation?

Atlas integrates directly with your Flask toolchain. For testing, it supports `pytest (app.test_client)`. For package management, it works with `uv`. For code formatting, it uses `ruff format`. Atlas can even add `pytest` fixtures and run `ruff format` on touched blueprints after your plan is approved.

How do I get my multi-file Flask change plan reviewed before coding?

You document your entire multi-file Flask change plan within the designated plan markdown file using the Atlas plan agent. Once complete, you call `plan_exit`. This tool prompts you for approval to switch to the build agent, providing a natural point for team review and feedback on your proposed Flask architecture before any code is written.

Does Atlas support local embedding models for Flask code analysis?

Yes, Atlas can build its code index with local Ollama embeddings. This means your Flask application's code, including sensitive blueprints or configuration in `pyproject.toml`, remains off third-party servers, enhancing privacy and security during analysis and planning.

What happens if I decide not to proceed with a Flask plan after writing it?

If you decide not to proceed with a Flask plan, or if it requires further refinement, you can answer 'No' when prompted by the `plan_exit` tool. This raises a `Question.RejectedError` and keeps you in the plan agent, allowing you to continue refining your plan without switching to the build agent or making any code changes.

Plan a Multi-File Change Before Editing in Flask with Atlas in 2026

Atlas empowers Flask developers in 2026 to design multi-file changes and secure review approval before writing any code, leveraging a dedicated plan agent. This agent restricts all edit tools, allowing you to research your Flask codebase with `codebase_search`, `grep`, `read`, and `lsp`, then document your strategy in a designated plan markdown file. Once your design is complete, the `plan_exit` tool facilitates a controlled handoff to the build agent, ensuring your Flask application's integrity and adherence to patterns like blueprints and the application factory, all while integrating with `pytest (app.test_client)`, `uv`, and `ruff format`.

How Atlas Plans Multi-File Flask Changes Without Editing

In 2026, Atlas provides a dedicated plan agent that ensures zero accidental edits while you design complex Flask changes. This agent's permissions explicitly deny write access to all files except a specific plan markdown document, allowing you to focus purely on architectural design and research within your Flask project.

Atlas's plan agent is purpose-built for the 'design before edit' workflow, crucial for Flask applications that rely on structured patterns like blueprints and the application factory. When you initiate Atlas in plan mode, its permission set is configured to disallow all edit tools across your entire codebase. This means you can freely explore your Flask project's structure, including `pyproject.toml`, `app` packages, and `create_app()` functions, without any risk of unintended modifications. The only file the plan agent is permitted to write to is a designated markdown file under `.atlas/plans/*.md`, serving as your canvas for detailing the proposed multi-file change. This read-only environment is ideal for mapping out how a new feature might interact with existing Flask blueprints or modify `current_app` and `g` context usage.

Researching Your Flask Codebase with Atlas's Plan Agent

To effectively plan a multi-file change in your Flask project, Atlas's plan agent provides powerful research tools like `codebase_search`, `grep`, `read`, and `lsp`. These tools remain fully functional within the read-only plan mode, allowing you to deeply understand your application's structure and dependencies before proposing any modifications in 2026.

Within the Atlas plan agent, you have access to a suite of tools designed for comprehensive codebase exploration. Use `codebase_search` to semantically locate relevant Flask components, such as specific blueprints or extension initializations within your `create_app()` factory. `grep` allows for precise keyword searches across your project, helping you find all occurrences of a particular configuration variable or a `current_app` usage pattern. The `read` tool lets you inspect the contents of any Flask file, from `app/__init__.py` to specific blueprint modules. Furthermore, the `lsp` tool provides language server protocol capabilities, offering insights into function definitions, type hints, and references within your Flask code, ensuring you have a complete understanding of the impact of your proposed changes on your application's architecture. This thorough research capability is vital for designing robust changes that respect Flask's request context and application factory patterns.

Documenting Your Flask Change Plan and Ensuring Safety

After researching your Flask codebase, you document your multi-file change plan directly into the allowed plan markdown path, typically under `.atlas/plans/my_flask_feature.md`. This is the single location where the plan agent can write, ensuring that your design process remains isolated and safe from unintended code modifications in 2026.

The core of the planning workflow involves writing your detailed strategy into the designated plan markdown file. This document becomes the blueprint for your multi-file Flask change, outlining how new routes will be added to existing blueprints, how `uv` will manage new dependencies in `pyproject.toml`, or how `ruff format` will be applied to new code. Atlas's permission-gated system is a critical safety feature here; it strictly enforces that the plan agent can only write to this specific markdown file. This prevents any accidental edits to your actual Flask application code, such as `app/views.py` or `app/config.py`, during the design phase. The plan markdown file serves as a comprehensive record of your proposed changes, ready for review by your team before any implementation begins, ensuring alignment with Flask best practices and project standards.

Reviewing and Handoff for Flask Implementation with Atlas

Once your multi-file Flask change plan is complete and documented, the `plan_exit` tool initiates a controlled review and handoff process. This tool asks for explicit confirmation to switch from the read-only plan agent to the build agent, ensuring that implementation only begins after your design has been approved, typically after 0 lines of code have been modified.

The `plan_exit` tool is the gateway from design to implementation. When you're satisfied with your detailed plan for modifying your Flask application, calling `plan_exit` prompts Atlas with a clear question: 'Plan at <path> is complete. Would you like to switch to the build agent and start implementing?' Answering 'Yes' transitions Atlas to the build agent, which has the necessary permissions to modify your Flask codebase. Answering 'No' keeps you in the plan agent, allowing further refinement of your design. This explicit approval step is crucial for team collaboration and code quality, as it ensures that your multi-file Flask change, whether it involves new blueprints or refactoring `create_app()`, has been thoroughly reviewed and agreed upon. Atlas also computes a unified diff for every file edit and surfaces it for approval before writing, and can stage and create commits on your behalf, integrating direct with your `git` workflow.

Step by step

01Start Atlas in plan mode within your Flask project, ensuring your `pyproject.toml` and `app` package with `create_app()` are present.
02Use Atlas's `codebase_search`, `grep`, `read`, and `lsp` tools to research your Flask application's blueprints, extensions, and `current_app` or `g` usage.
03Write your detailed multi-file change plan into the designated markdown file, for example, `.atlas/plans/new_feature_design.md`, outlining Flask-specific modifications.
04Review your plan for completeness and accuracy, ensuring it addresses how `pytest (app.test_client)` will be used for testing and `ruff format` for formatting.
05Call the `plan_exit` tool and confirm 'Yes' to switch from the read-only plan agent to the build agent, initiating the implementation phase.
06Allow Atlas to add `pytest` fixtures around `app.test_client()` and run `pytest` behind a permission prompt, then review the diff.
07Let Atlas run `ruff format` on the touched Flask blueprints and other files, reviewing the unified diff before committing.

Frequently asked questions

How does Atlas prevent accidental edits in my Flask project during planning?: Atlas employs a dedicated plan agent whose permissions are strictly configured to deny all edit tools across your Flask codebase. The only exception is a specific plan markdown file (`.atlas/plans/*.md`), ensuring that your design phase is entirely read-only for your actual Flask application files like `app/models.py` or `app/routes.py`.
Can I use Atlas to understand my Flask blueprints and application factory?: Yes, Atlas is designed to understand Flask's core patterns. It indexes code by AST declarations using tree-sitter, allowing it to read your blueprints, extensions initialized in `create_app()`, and anything reaching for `current_app` or `g`. You can use `codebase_search` and `lsp` to explore these structures effectively.
What Flask-specific tools does Atlas integrate with for planning and implementation?: Atlas integrates directly with your Flask toolchain. For testing, it supports `pytest (app.test_client)`. For package management, it works with `uv`. For code formatting, it uses `ruff format`. Atlas can even add `pytest` fixtures and run `ruff format` on touched blueprints after your plan is approved.
How do I get my multi-file Flask change plan reviewed before coding?: You document your entire multi-file Flask change plan within the designated plan markdown file using the Atlas plan agent. Once complete, you call `plan_exit`. This tool prompts you for approval to switch to the build agent, providing a natural point for team review and feedback on your proposed Flask architecture before any code is written.
Does Atlas support local embedding models for Flask code analysis?: Yes, Atlas can build its code index with local Ollama embeddings. This means your Flask application's code, including sensitive blueprints or configuration in `pyproject.toml`, remains off third-party servers, enhancing privacy and security during analysis and planning.
What happens if I decide not to proceed with a Flask plan after writing it?: If you decide not to proceed with a Flask plan, or if it requires further refinement, you can answer 'No' when prompted by the `plan_exit` tool. This raises a `Question.RejectedError` and keeps you in the plan agent, allowing you to continue refining your plan without switching to the build agent or making any code changes.

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