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Open GitHub PRs and local changes in pair-review web UI for collaborative review, trigger server-side AI analysis on diffs, apply automated fixes to AI and human feedback, and iterate on suggestions directly in your editor. Requires pair-review MCP server.
npx claudepluginhub in-the-loop-labs/pair-review --plugin pair-reviewFetch AI-generated review suggestions from pair-review and make code changes to address them. Use when the user says "address AI feedback", "address AI suggestions", "fix AI review feedback", or wants to iterate on code based on AI analysis results from pair-review.
Perform AI-powered code review analysis using pair-review's server-side analysis engine via MCP. Requires the pair-review MCP server to be connected. For standalone analysis without MCP, use the `code-critic:analyze` skill instead. Starts analysis via the pair-review MCP start_analysis tool, polls for completion, then fetches and presents the curated suggestions. Results are also visible in the pair-review web UI alongside the diff. Use when the user says "analyze in the app", "analyze in the UI", "run server analysis", "analyze with pair-review", or wants analysis results integrated into the pair-review web UI. If the user says something ambiguous like "analyze my changes" or "run analysis" without specifying a method, and both the `code-critic:analyze` and `pair-review:analyze` skills are available, ask whether they want: (1) agent-based analysis (`code-critic:analyze` — results returned directly in the conversation, no server required), or (2) in-app analysis (`pair-review:analyze` — results appear in the pair-review web UI, requires MCP connection). If only one analysis skill is available, use it directly without asking.
Open local uncommitted changes for review in the pair-review web UI. This only opens the browser — it does not run AI analysis or generate suggestions. Once open, the user can browse the diff, leave comments, and trigger analysis from the web UI themselves. Use when the user says "review my local changes", "review local", "open local review", or wants to open a pair-review session for uncommitted work in the current directory. If the user wants automated AI analysis of their local changes rather than just opening the browser, use the `code-critic:analyze` skill (standalone, requires code-critic plugin) or `pair-review:analyze` skill (requires MCP server) instead. Note that the user can also trigger AI analysis from within the pair-review web UI after opening it.
Open the GitHub pull request for the current branch in the pair-review web UI. This only opens the browser — it does not run AI analysis or generate suggestions. Once open, the user can browse the diff, leave comments, and trigger analysis from the web UI themselves. Use when the user says "review this PR", "review pull request", "open PR review", or wants to open a pair-review session for the current branch's pull request. If the user wants automated AI analysis of the PR rather than just opening the browser, use the `code-critic:analyze` skill (standalone, requires code-critic plugin) or `pair-review:analyze` skill (requires MCP server) instead. Note that the user can also trigger AI analysis from within the pair-review web UI after opening it.
Open outstanding GitHub review requests in pair-review for AI-powered code review. Finds open PRs where my review is pending from the past week and starts pair-review analysis for each. Use when the user says "review requests", "review my PRs", "check review requests", "open review requests", "pair-review my requests", or wants to batch-review their outstanding GitHub review requests.
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AI-powered code review analysis — Run three-level AI analysis and implement-review-fix loops directly in your coding agent. Works standalone, no server required.
Your AI-powered code review partner - Close the feedback loop between you and AI coding agents

pair-review is a local web application for keeping humans in the loop with AI coding agents. Calling it an AI code review tool would be accurate but incomplete — it supports multiple workflows beyond automated review, from reviewing agent-generated code before committing, to judging AI suggestions instead of reading every line, to using AI to guide your attention during a thorough review. You pick what fits your situation.
Tight Feedback Loop for AI Coding Agents
AI-Assisted Human Review Partner
There are no hard boundaries between these — mix and match as needed.
When to use: You're working with a coding agent and want to review its changes before committing.
This is the core feedback loop workflow. When an agent generates code, open pair-review to review the uncommitted changes. With the GitHub-like UI, you can add comments at specific file and line locations, then copy that formatted feedback and paste it back into whatever coding agent you're using (or use MCP/skills to read comments directly into Claude Code).
Compared to giving feedback in chat, this feels like moving from a machete to a scalpel. Instead of trying to capture everything in one message, you can leave targeted comments at dozens of specific locations — and the agent addresses each one with surgical precision.
How it works:
pair-review --local to open the diff UITips:
When to use: You're not going to read every line of code. Let AI be your reader.
Instead of reviewing thousands of lines of code, you review a dozen AI suggestions. The AI reads the code; you review its recommendations. Each suggestion comes with enough context to evaluate it — even when you're not deeply familiar with the language or codebase.