TechPrufer
Get started
Atlassian
Atlassian logoAtlassian · Feature trail

Rovo Dev CLI for Accelerated Debugging

6 posts · Nov 2024 – Apr 2026

What this trail covers

Atlassian's RovoDev Coding Agent is an AI-assisted software development tool designed to work alongside engineers, embedded in Jira. It guides tasks from planning to coding, with a human in the loop for critical decisions. The agent drafts coding plans, generates code validated by tools and human review, and submits it for standard team review. Recent research indicates that RovoDev-generated code is as readable as human-written code across most metrics, supporting its adoption for scaling SWE Agents in the enterprise.

All Atlassian trails
  1. Build smarter with Claude Opus 4.7 and deep Atlassian context in Rovo DevThis post announces the availability of Claude Opus 4.7 within the Rovo Dev CLI and IDE, highlighting its advanced software engineering capabilities. It also introduces a limited-time promotion for Rovo Dev Standard customers, reducing the credit multiplier for Claude Opus 4.7. The post details how to switch models and adjust efficiency levels using slash commands within the CLI.
  2. From Days to Hours: Accelerated K8s Debugging with Rovo Dev CLIThis post introduces the Rovo Dev CLI as a tool for accelerated debugging. It details a specific incident where the CLI was used to diagnose intermittent 404 errors in a Kubernetes environment, identifying an Ingress host-rule mismatch due to subtle Apache HttpClient behavior. The CLI enabled logging, compared ingress rules, generated a test client, and suggested a fix by setting a default OpenSearch backend. The post highlights the CLI's ability to reduce debugging time from engineer-days to hours, create repeatable workflows, facilitate knowledge sharing, and compensate for domain-specific knowledge gaps.
  3. How We Turned Feature Flag Cleanup Into a Mostly‑Hands‑Off AI WorkflowThis post details the application of the Rovo Dev CLI to automate feature flag cleanup. It describes the creation of repo-tailored AI commands, the use of a self-improvement loop to refine these commands based on feedback, and the integration with Jira Automation and a queueing system to achieve a hands-off workflow. The process involves defining specific prompts for feature flag cleanup, iterating on them to improve accuracy (raising success rate from 5/9 to 8/9 PRs requiring no manual intervention), and then automating the trigger via Jira labels. A queueing mechanism was implemented to manage concurrent cleanup tasks and prevent merge conflicts. The core contribution is demonstrating how to specialize AI tools for specific engineering tasks and integrate them into existing workflows for increased efficiency and reduced drudgery.
  4. Supercharge your Security Testing with Rovo Dev SkillsThis post details how security teams can leverage Rovo Dev Skills to extend the Rovo Dev CLI for security testing. It introduces the concept of Rovo Dev Skills as an open format for providing agents with instructions, scripts, and resources. Specific skills are demonstrated for security testing, including `language-analyzer` (using `cloc`), `semgrep-scanner` (using `semgrep`), `secret-scanner` (using `trufflehog`), `endpoint-enumerator` (using `rg`), `security-reviewer` (AI-powered code auditing), `cvss-scorer` (using Python CVSS library), `sast-investigator` (triage findings), and `generate-test-leads` (prioritized test leads). The post also explains how to write and organize skills, and discusses when to use skills over Model Context Protocol (MCP) integrations for simpler CLI tools.
  5. Rovo Dev is now generally available in VS CodeThis post announces the general availability of Rovo Dev within the Atlassian VS Code extension. It highlights how Rovo Dev, an AI agent powered by the Teamwork Graph, connects to Atlassian tools (Jira, Confluence, Bitbucket) and third-party platforms (GitHub) to provide context-aware assistance directly within the IDE. Key capabilities include starting tasks faster by answering codebase questions, automating busywork like drafting Jira tickets and PRs, staying in flow with work suggestions and review management, receiving instant alerts, and leveraging organizational context. Example workflows for managing Jira tickets and creating/reviewing PRs are provided.
  7. Streamlining your Rovo Dev CLI workflow with event hooksIntroduced event hooks to the Rovo Dev CLI, allowing users to script custom behavior triggered by specific agent lifecycle events (e.g., response generation, error occurrence, tool permission requests). This enables automation of repetitive tasks like running linters, builds, or tests after an agent completes, or triggering notifications when tool permissions are requested. Provided examples for playing sound notifications on macOS, Linux, and Windows.
  8. How Specialised Sub-Agents Enhanced Rovo Dev CLI Reliability for My ProjectThis post details a personal experimentation with Rovo Dev CLI, moving from a single monolithic agent to a network of specialized sub-agents coordinated by a main orchestrator. The sub-agents have distinct responsibilities (Backend, Frontend, Test, Performance Guardian, Architecture Guardian, Historian, Documentation, Git/Quality Gate). This approach aims to improve reliability, reduce regressions, and manage complexity in larger projects by assigning narrow, clear responsibilities to individual agents and focusing the orchestrator on coordination and decision-making. The author uses stronger models for critical agents and lighter models for simpler tasks to optimize cost and performance.
  9. Using Rovo Dev multi‑stage prompts for refactoring UI componentsThis post introduces a workflow for using multi-stage prompts with Rovo Dev CLI to refactor UI components. It breaks down a complex refactoring task into four incremental stages: refactoring a component to render dynamic data, integrating it into a parent component, adapting to requirement changes by simplifying identifier passing and extending link utilities, and correcting mock data for stable testing. Each stage uses a narrowly-scoped prompt, resulting in focused AI output, reviewable diffs, and easier course correction. The core technical contribution is the demonstration of an effective pattern for leveraging AI agents in complex software development tasks by decomposing work into explicit, incremental steps.
  10. Spec Driven Development with Rovo DevThis post introduces and elaborates on the concept of 'Spec-Driven Development' (SDD) as a methodology for interacting with AI coding agents. It contrasts SDD with 'vibe coding,' highlighting the benefits of structured planning, iterative review, and clear guardrails provided by specifications. The post details how to create `.agents.md` files or similar rule sets to guide AI behavior, and provides an example of an SDD plan generated by Rovo Dev CLI for consolidating benchmark scripts, demonstrating the iterative review and execution process.
  11. Mobbing with AIThis post details the application of AI tools (Rovo Dev CLI, Rovo Chat, Rovo Dev Code Review) within a mob programming workflow to accelerate feature development and improve code quality. It describes a process loop involving defining standards, creating specs, AI code generation, mob review and refinement, AI test generation, and knowledge capture. The team observed a significant increase in AI-generated code quality and a reduction in development time compared to traditional workflows. The post also touches on AI's role in requirements gathering, spikes, prototyping, and design/architecture.
  12. How to effectively utilise AI to enhance large-scale refactoringThis post details a practical, end-to-end approach for using AI (specifically Rovo Dev) to identify and remove feature gates (FGs) and mocks across a large monorepo. It introduces a repeatable, safe, and scalable workflow for large-scale codebase refactoring. Key contributions include establishing persistent AI context via a 'memory' file, using discovery prompts to enumerate patterns, defining package-level cleanup methods, and leveraging multiple threads with DevBox for concurrent code changes. The post highlights AI's ability to act as a thinking partner, not just a script generator, and emphasizes the importance of codified context, batching with tight feedback loops, discovery prompts, and guardrails for quality.
  13. Ship faster with focused Subagents in Rovo Dev CLIThis post introduces the concept of 'subagents' within the Rovo Dev CLI. Subagents are specialized, reusable AI helpers that can be tested, permissioned, and executed independently. They enable reproducible, stepwise automation with contract-first outputs, least-privilege execution, context discipline, cost control, parallel execution, caching, team reuse, governance, and faster feedback loops. The post details interactive creation and manual configuration of subagents, providing example prompts and a lifecycle flowchart. It emphasizes practical patterns like keeping subagents narrow, explicit scoping, and standardizing outputs, drawing parallels to the Zen of Python.
  14. Rovo Dev agent, now available in the CLIThis post announces the release of the Rovo Dev CLI, bringing the Rovo Dev Agent to the command line. It highlights the CLI's capabilities in code understanding, development acceleration (including AI-assisted completion, refactoring, testing, and debugging), and integration with the Atlassian ecosystem (Jira, Confluence, Bitbucket). It also mentions security and administration features, extensibility, and its #1 ranking on the SWE-bench benchmark. Real-world usage scenarios are demonstrated, including codebase exploration, integration with Atlassian tools, adaptive memory system, and code migration assistance.
  15. Reimagining software delivery with AI-powered workflows in Jira & BitbucketThis post introduces agentic CI/CD in Bitbucket Pipelines, enabling natural language instructions for AI agents to execute tasks within pipelines. New triggers like package publication and deployment completion are introduced. Agentic steps powered by Rovo Dev can interact with Bitbucket Cloud via a custom MCP server to perform actions like raising/updating pull requests, creating comments, and reading logs. An example scenario demonstrates automatically fixing flaky tests by analyzing issues, generating code, and opening a pull request. The post also mentions a new Test Management feature in development.
  16. How to Use Forge MCP Server to Improve and Debug Your Atlassian Forge AppsThis post introduces the Forge MCP Server, an AI-powered development tool that integrates with IDEs and coding agents to assist Forge app developers. It enables understanding app code and logic through natural language queries, planning and executing app enhancements by suggesting Forge modules and updating code, and troubleshooting issues by adding contextual logging. The MCP Server supports tools like Cursor, VS Code, and Rovo Dev CLI, emphasizing iterative development and the use of AI assistants in an 'Agent' or 'Edit' mode for automatic code updates.
  17. Atlassian Research: What Do Developers Think About Code Readability in the Age of LLMs?This post presents research findings on code readability in the age of LLMs, specifically comparing human-written code with RovoDev-generated code. The research surveyed 118 practitioners and conducted an empirical study measuring code readability using metrics like Lines of Code, Comment Ratio, Cyclomatic Complexity, Maintainability Index, and Halstead Metrics. Key findings indicate that RovoDev-generated code is as readable as human-written code across most metrics, with negligible differences in Java, Kotlin, Go, and Scala, and only small differences in TypeScript and Python. The research concludes that LLM agents can produce trustworthy and maintainable code, supporting the widespread adoption of tools like RovoDev Coding Agent.
  18. Rovo Dev CLI releases with GPT-5 supportThis post announces an update to Rovo Dev CLI that now supports OpenAI's GPT-5 model. GPT-5 is highlighted for its improvements in reasoning, code quality, and user experience, leading to reduced latency and resource use. The update includes an enhanced /models menu for easy switching between GPT-5 and other models, and adds functionality for image and PDF file input, improved copy commands, and better session management and cross-platform support.
  19. How to integrate GitHub Copilot with Bitbucket, Jira & ConfluenceThis post details how to integrate GitHub Copilot with Bitbucket repositories by cloning them into a supported IDE (like VS Code) and enabling the Copilot extension. It also introduces Atlassian's Rovo Dev, an AI agent that connects with Jira, Confluence, and Bitbucket for code review, build troubleshooting, and deployment summaries. Native Bitbucket AI features like AI PR descriptions and AI chat are also highlighted. Future initiatives include generating code from Jira issues, fixing builds with AI, and agentic CI/CD.