Perform AI-powered code review analysis by spawning parallel Task agents directly within the coding agent's context. Does not require the pair-review MCP server — works standalone. Runs Level 1 (diff isolation), Level 2 (file context), and Level 3 (codebase context) as parallel tasks, then orchestrates results into curated suggestions. Results are returned directly in the conversation and also pushed to the pair-review web UI (if running). Use when the user says "analyze", "analyze my changes", "run analysis", "analyze using tasks", "analyze directly", "analyze here", or wants code review analysis of their changes. This is the default analysis skill. If the user says something ambiguous like "analyze my changes" or "run analysis", use this skill unless they specifically ask for in-app analysis.
Dispatch a review task to 3 randomly-selected reasoning models in parallel for diverse perspectives, then merge all suggestions into a single result.
Guidance for selecting models when performing code review with subtasks. Load this skill to enable intelligent model selection for review analysis — choosing faster models for simple tasks and deeper reasoning models for complex analysis.
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.
Implement code, review changes with AI analysis, fix issues, and repeat until clean or max iterations reached. Creates a tight implement-review-fix feedback loop. Use when the user says "critic loop", "code review loop", "implement and review", "build with review loop", or wants iterative development with automated quality checks.
Fetch 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
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.