Claude Review: IntelliJ Plugin for Real-Time Code Review with Claude Code
Claude Review is an IntelliJ plugin that provides real-time code review using Claude Code. Every time you save a file, the plugin takes your unstaged git diff and sends it to Claude Code with a customizable prompt.
How It Works
The plugin operates automatically on file save. It captures unstaged changes from git and sends them to claude -p with a prompt you can customize. Claude responds with line-level findings categorized as BUG, WARNING, or INFO.
Key Features
- Zero friction workflow — reviews happen automatically in the background when you save files
- Native IntelliJ integration — findings appear as annotations with gutter icons and severity-colored underlines
- Content-hash caching — prevents wasted API calls on unchanged files
- Customizable prompts — you control what Claude focuses on (security, performance, etc.)
Requirements
- Claude Code CLI must be installed
- Project must be git-tracked
Availability
The plugin is open source under MIT license. You can find it on the JetBrains Marketplace at https://plugins.jetbrains.com/plugin/30307-claude-review and the source code on GitHub at https://github.com/kmscheuer/intellij-claude-review.
📖 Read the full source: r/ClaudeAI
👀 See Also
Agents & A.I.mpires: Strategy Game Where AI Agents Play and Humans Spectate
Agents & A.I.mpires is a persistent real-time strategy game on a hex-grid globe where AI agents autonomously claim territory, attack, form alliances, and write daily war blogs via HTTP API calls. Humans only spectate the emergent behavior.
LLMs Leak Reasoning into Structured Output Despite Explicit Instructions
A developer building a tool that makes parallel API calls to Claude and parses structured output found that validation models intermittently output reasoning text before corrected content, despite explicit instructions to return only corrected text. The fix involved prompt tightening plus a defensive strip function that runs before parsing.
SWE-CI: New Benchmark Tests AI Agents on Long-Term Code Maintenance via CI
SWE-CI is a repository-level benchmark that evaluates LLM-powered agents on maintaining codebases through continuous integration cycles, shifting focus from static bug fixing to long-term maintainability across 100 real-world tasks.
MemAware benchmark shows RAG-based agent memory fails on implicit context retrieval
The MemAware benchmark tests whether AI agents can surface relevant past context when users don't explicitly ask for it, revealing that current memory systems score only 2.8% accuracy on hard implicit queries versus 0.8% with no memory.