Use cases

From first prompt to merged pull request.

Every CloudAEye use case in one place. Review code where your agents write it, approve what is safe to merge, and keep CI and your team on track.

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Works where your team builds

  • GitHub
  • GitLab
  • Bitbucket
  • Jira
  • Claude Code
  • Codex
  • Cursor
  • VS Code
  • IntelliJ IDEA
  • PyCharm

Code Review

Catch bugs and security risks with full-codebase context, then decide what is ready to merge and who should look at the rest.

AI SDLC

Keep pace with coding agents. Review the code they write, score its risk, and route each PR to the right reviewer.

  • /ultrareview

    Review Intelligence

    Govern code at AI speed with automatic approvals for low-risk PRs and assign the right human reviewer for higher-risk, higher-impact PRs.

    • Every review of a change in one run
    • Approval limits and review policy you set
    • Each check runs once per change

    One run checks

    • PR checklist
    • Bugs
    • Security
    • AI slop score
    • Risk score
    • Architecture
    • Control flow
    • Task check
    Approved for merge Low-risk PR within your limits
    Requires human review Right reviewers assigned to the PR
  • /review

    Code Review without the noise

    Use the best code review proven by top F1-score on open-source benchmarks. CloudAEye ranked #1 with a 66.7% F1 score, outperforming Qodo, Cursor Bugbot, Greptile, Claude Code, GitHub Copilot, and CodeRabbit.

    • File, line, and severity for each finding
    • Full-codebase context, not just the diff
    • Can review every new PR automatically
  • /check-task

    Check Task

    Verify whether the pending change fulfills a Jira ticket, GitHub issue, or written specification. Returns a DONE, PARTIAL, or NOT DONE verdict with details of missing work.

    • Runs on uncommitted changes
    • Prevents incomplete features
    • Asks before closing the gaps
  • /slop-score

    AI Slop

    Assess how much manual review a change needs by checking nine code quality signals, including duplication, nonexistent APIs, dead code, missing tests, and overengineering. Returns a review band with supporting evidence.

    • A score out of 100 and its band
    • Runs on a change, a path, or a PR
    • About review burden, never authorship

  • Model Inversion

    If a PR was generated by GPT or Claude, automatically use the opposing model for review to improve accuracy.

    • A second model's perspective
    • Reads Co-authored-by trailers
    • Pro and Enterprise plans

  • Pre-commit Flow

    Shift-left and validate tasks using a pre-commit flow in the Claude/Codex plugin.

    • Reviews uncommitted changes
    • Fix, re-run, then commit
    • A review that is part of done
    Claude Code Codex
  • MCP

    Coding Agent & IDE Support

    Use #1 code review from your favorite coding agent such as Claude Code or Codex. Use IDE using CloudAEye MCP. Supports Cursor, VS Code, IntelliJ, and PyCharm.

    • Same commands in every client
    • Any MCP-compatible client

  • PR Walkthrough

    Understand the PR with a structured overview of the changes.

    • What changed, why, and how files relate
    • Sequence diagrams and layers
    • Risk and blast radius explained
  • /triage-prs

    Triage PRs

    Rank pull requests by review priority based on factors such as complexity, risk, business impact, and alignment with OKRs.

    • A priority out of 100 for each PR
    • Across every connected repository
    • Writes nothing on GitHub or Jira
  • /assign-reviewer

    Assignment of Human Reviewers

    Automatically identify and assign the best human reviewer who is available in the team.

    • Code ownership and commit history
    • Balanced against each review queue
    • A note like "Tom is away" steers it
  • /risk-score

    Risk Score

    Identify the risk of a PR by analyzing recent bugs for the code area, churn, and author's history.

    • Bug trend analysis
    • Author history
    • A score out of 100

  • Prevent Recurring Prod Incidents

    Every prod incident automatically generates a regression test and a code review rule targeting its root cause. This prevents recurring prod incidents.

    • Starts from the root cause
    • A regression test for the failure
    • A review rule for the pattern

Developer use cases

Find bugs, fix them, and document, test, and describe your change without leaving your workflow.

  • /add-docs

    Docstrings

    Generate docstrings for undocumented code in an open pull request and post them as review suggestions. Reports which suggestions were generated and which reached the PR.

    • In your repository's convention
    • One-click suggestions you apply
    • Never edits your working tree
  • /add-tests

    Unit Tests

    Generate unit tests for uncovered code in an open pull request, including new test files where needed. Posts the tests as review suggestions for you to review and run.

    • Gaps found through the call graph
    • Edge and negative cases
    • Nothing changes until you apply it
  • /implement

    Fix Suggestions

    Suggest fixes for issues identified by CloudAEye's review. Each fix arrives on the pull request as a commit suggestion you can apply.

    • Reply on any CloudAEye finding
    • Commit suggestion on the PR
    • Fix plan for Claude Code or Codex
  • /ask

    Q&A with Code

    Answer questions about the pending change. Ask what else calls a function, how it behaved before, or where a similar pattern is used.

    • Whole repository, not just open files
    • Cites source files when you ask
    • Follow-up questions keep the context
  • /describe

    PR Description

    Comment /describe on a pull request to generate a structured description from its code changes and commit history.

    • Title, summary, and key changes
    • Many commits, one story
    • GitHub, GitLab, and Bitbucket
  • /inspect

    Bug Review

    Find bugs such as logic errors, missed edge cases, invalid inputs, concurrency problems, and broken code signatures. Returns an approval verdict and numbered findings with file locations and severity.

    • Catches callers your change broke
    • Flags calls to missing or misspelled names
    • Cheap enough to run after every task
  • /create-issue

    Create Tracking Issues

    Turn selected findings from the last review into Jira tickets, GitHub issues, or GitLab issues, with an optional assignee and note. Returns ticket links and identifies findings already tracked in the same session.

    • Evidence attached to each ticket
    • Jira, GitHub, and GitLab issues
    • From your coding agent or a PR

Enterprise ready

Built for large codebases, strict security, and your own infrastructure.

  • Multi-repo Context

    Full codebase context across hundreds of repositories. Analyzes how files, functions, classes, modules, and dependencies relate to each other.

    • Callers in other repositories
    • Built automatically, no setup
    • Architecture-aware feedback
  • /security

    OWASP Security

    Find security issues across application code, LLMs, AI agents, and MCP tools.

    • OWASP Top 10 for web, LLM, and agentic apps
    • A fix for each finding
    • MCP security recommendations

  • Secret Scanning

    Prevent hardcoded credentials, API keys, and tokens from ever reaching GitHub, GitLab, or Bitbucket.

    • Before you commit and on every PR
    • Scans every changed line for leaks
    • Can block the merge in your PR checklist
  • /check-pr

    PR Checklist

    Enforce engineering standards at scale across your entire organization with 75+ prebuilt checks.

    • Nine categories of checks
    • Advisory or merge-blocking
    • Security, LLM, and MCP checks
  • /control-flow

    Control Flow Analysis

    Draw sequence diagrams showing calls the change added, removed, redirected, or moved. Identifies affected callers in other connected repositories so you can see how the change alters execution flow.

    • Resolved from the code graph
    • Every changed call marked
    • Unintended behavior surfaced
  • /review-arch

    Architecture Review

    Show how a change affects the system architecture, repository layers, and dependencies through three diagrams. Reports architecture findings, cross-repository impact, and questions for a senior review.

    • Layer and boundary violations
    • New cycles and dependencies
    • Questions for a senior review

  • Automated PR Review

    Configure to review every new PR when it is first opened.

    • Inspect, Review, or Ultrareview
    • Filter by label, branch, or author
    • Per repository or global

  • Linters

    Incorporate linting best practices, such as style consistency, error detection, and maintainability checks to keep your codebase cleaner, more readable, and easier to maintain.

    • 14 supported linters
    • One Linter Findings group
    • Configured per repository

  • Self-hosting

    Self-host in your own infrastructure with support for SSO/SAML (Okta, Keycloak) and MFA.

    • Runs in your own AWS account
    • Okta or Keycloak, OIDC or SAML
    • Bring your own LLM

Learning & personalization

Reviews that learn your team's standards.

  • Custom Rules

    Personalized code review for each team using natural language-based custom rules.

    • Scoped by repository or file pattern
    • Switch each rule on or off
    • Also enforced before the commit

  • Learn from Previously Reviewed PRs

    Learn from team comments on PRs and automatically create rules from pattern analysis.

    • Pick the PRs to learn from
    • Exclude authors you choose
    • Review the rule before saving

  • Review Feedback

    Automatically learn and create custom rules from user feedback without any configuration.

    • A thumbs-down and plain English
    • From the pull request or console
    • You approve each rule

  • Tech Stack

    Improve review accuracy by providing documentation to your technology stack or APIs.

    • Official docs for your frameworks and packages
    • Set per repository
    • One or more URLs per technology

Test Failure Analysis

Find out why tests fail in GitHub Actions and Jenkins, and fix them faster.

  • /analyze

    Test Failure Analysis in CI

    Automatic root-cause identification of a test failure in CI, such as GitHub Actions and Jenkins.

    • Root cause and a proposed fix
    • GitHub Actions and Jenkins
    • Shown in GitHub Checks

CodeCortex

Help engineers understand the code and the tickets from their first day, and automate the reports every sprint needs.

Engineering onboarding

Answers from your code, tickets, and team knowledge.

  • Enterprise Search

    Make all internal and external documentation (Google Drive, OneDrive, YouTube, Jira) searchable. Digitize tribal knowledge and find answers instantly.

    • Google Drive, OneDrive, YouTube, Jira
    • Only the sources you choose
    • Docs, PDFs, and team videos
  • /explain

    Explain a Ticket

    Explain a Jira or GitHub issue using the code it touches. Describes the relevant components, their current behavior, and surrounding code so you can understand the work before implementing it.

    • Jira and GitHub issues
    • Related patterns and configuration
    • Also as a comment on the ticket

Agile development

The reports every sprint needs, written from the work itself.

  • Team Insights

    Individual & team insights to understand your team's velocity.

    • Team and individual views
    • Review speed and throughput
    • GitHub, GitLab, Bitbucket, and Jira

  • AI ROI

    Understand ROI for your AI investment.

    • Claude Code, Cursor, Copilot, and Codex
    • Spend by team and person
    • Value returned and net savings

  • Standup

    Save time with automated daily standup reports.

    • Priorities, progress, and problems
    • Blockers counted first
    • Scheduled, or run now

  • Sprint Retro

    Automated sprint reviews that make it convenient to perform the agile ceremony. All closed tickets grouped by theme, shipped vs. bounced, posted before retro starts.

    • What shipped, by objective
    • Worked well, needs attention
    • Measured against your OKRs

  • Performance Reviews

    Give constructive feedback and help with employee career development with automated individual performance reviews that can be generated monthly or quarterly. The system remembers all data, saving managers valuable time.

    • Month, quarter, or custom dates
    • Skill ratings the manager approves
    • Past reviews kept

Start with one pull request.

Connect a repository, comment @cloudaeye /ultrareview on a PR, and see every check and the merge decision in one report.

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