Orchestrate Multi-Agent Code Reviews

Multi-Agent Code Review Orchestration Tool coordinates a distributed network of specialized review agents - Code Quality Reviewers, Security Auditors, Architecture Specialists, Performance Analysts, Compliance Validators, and Best Practices Experts - to produce a comprehensive, multi-perspective code assessment that a single-perspective review cannot match. It accepts $ARGUMENTS as the review target (file paths, Git repositories, or code snippets) and implements a seven-part coordination strategy: dynamic agent selection and routing based on input characteristics, for example routing security-auditor and web-architecture-reviewer for a web application, or adding performance-analyst when the code is performance-critical:

  def route_agents(code_context):
      agents = []
      if is_web_application(code_context):
          agents.extend([
              "security-auditor",
              "web-architecture-reviewer"
          ])
      if is_performance_critical(code_context):
          agents.append("performance-analyst")
      return agents

context management via a ReviewContext object that carries target, metadata, and accumulated per-agent insights across the review; a hybrid parallel/sequential execution strategy where independent agents like code-quality-reviewer and security-auditor run in parallel while dependent agents like architecture-reviewer and performance-optimizer run sequentially; result aggregation that merges agent outputs into a consolidated report of critical issues, important issues, and improvement suggestions; a conflict-resolution mechanism that detects contradictory recommendations and applies weighted scoring via a ConflictResolutionEngine; performance optimization through cached intermediate results and adaptive resource allocation; and a quality-validation framework that computes a statistical confidence score against a threshold before accepting review results.

Use this skill when working on multi-agent code review orchestration tool tasks or needing guidance, best practices, or checklists for coordinating specialized review agents. It is not for tasks unrelated to multi-agent code review orchestration or for work in a different domain or tool outside this scope.

Input is the review target passed as $ARGUMENTS, plus an agent configuration - a weighted list for parallel review, or a phased list for sequential workflows. Three example implementations are provided: a parallel review weighting security-auditor, architecture-reviewer, and performance-analyst; a sequential four-phase workflow (design-review, implementation-review, testing-review, deployment-readiness); and a hybrid strategy splitting agents into parallel (security, performance) and sequential (architecture, compliance) groups. Output is a unified, prioritized report synthesized from all participating agents' insights.

Named reference implementations cover Web Application Security Review and Microservices Architecture Validation. The tool is built on a plugin-based architecture allowing new agent types and review strategies to be added. Stated best practices: maintain agent independence, implement robust error handling, use probabilistic routing, support incremental reviews, and ensure privacy and security throughout the process.

Engineering teams running code review at scale who want multi-perspective, agent-coordinated analysis - security, architecture, performance, compliance, and best-practices review running in parallel or phased sequence with automated conflict resolution - rather than a single reviewer or a single-pass automated check.