For the complete documentation index, see llms.txt. This page is also available as Markdown.

Reviewing the Pull Request

When the scan detects violations, UnifAI creates a new remediation branch and opens a pull request against the branch you scanned. The pull request contains a violations report and all code changes needed to resolve the identified issues.

The violations report in the pull request description lists the following for every fix:

Column

Description

Policy Name

The UnifAI policy that the code violated.

File

The file where the violation was found.

Lines Changed

The specific line numbers added, modified, or removed to fix the violation.

Remediation

A description of what was changed and why.

To resolve the violations, review the changes in the pull request and merge it into your branch.

As you merge remediation pull requests and run subsequent scans, the violation count decreases. You can track this reduction across scan iterations in the violations report.

The pull request is opened by the account whose PAT was used to run the scan. Ensure that token has permission to create branches and open pull requests on the repository.

Performance and Scope

The time to complete a scan depends on the size of the repository. Use the guidance below to estimate scan duration.

Repository Size

Guidance

Small / Medium

Supported today. Recommended starting point.

Large (10,000+ files)

Coming in the next release.

Up to 5 MCP server instances are supported concurrently.

What You Need

To scan any repository, you provide four inputs:

  • Repository name

  • Branch name

  • SCM token (to access the repository)

Language Model

When a remediation fix is applied, tokens are consumed to generate code from the remediation output. This averages approximately 600 tokens per violation. Token consumption is not controlled by Lineaje.

To use a different model instead of the default, configure the model in the MCP server settings. Only models that Lineaje has tested and certified are supported. Supported models include Claude Sonnet 4.5, 4.6, and Claude Opus 4.6 (SaaS supported). On-premises customers can use these models, but model configuration must be done through AWS Bedrock.

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