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error-debugging-multi-agent-review

Use when working with error debugging multi agent review

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name:error-debugging-multi-agent-reviewdescription:"Use when working with error debugging multi agent review"

Multi-Agent Code Review Orchestration Tool

Use this skill when

  • Working on multi-agent code review orchestration tool tasks or workflows

  • Needing guidance, best practices, or checklists for multi-agent code review orchestration tool

    • Do not use this skill when

  • The task is unrelated to multi-agent code review orchestration tool

  • You need a different domain or tool outside this scope

    • Instructions

  • Clarify goals, constraints, and required inputs.

  • Apply relevant best practices and validate outcomes.

  • Provide actionable steps and verification.

  • If detailed examples are required, open resources/implementation-playbook.md.

    • Role: Expert Multi-Agent Review Orchestration Specialist

    A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.

    Context and Purpose

    The Multi-Agent Review Tool leverages a distributed, specialized agent network to perform holistic code assessments that transcend traditional single-perspective review approaches. By coordinating agents with distinct expertise, we generate a comprehensive evaluation that captures nuanced insights across multiple critical dimensions:

  • Depth: Specialized agents dive deep into specific domains

  • Breadth: Parallel processing enables comprehensive coverage

  • Intelligence: Context-aware routing and intelligent synthesis

  • Adaptability: Dynamic agent selection based on code characteristics

    • Tool Arguments and Configuration

    Input Parameters


  • $ARGUMENTS: Target code/project for review

    • - Supports: File paths, Git repositories, code snippets
    - Handles multiple input formats
    - Enables context extraction and agent routing

    Agent Types


  • Code Quality Reviewers

  • Security Auditors

  • Architecture Specialists

  • Performance Analysts

  • Compliance Validators

  • Best Practices Experts

    • Multi-Agent Coordination Strategy

    1. Agent Selection and Routing Logic


  • Dynamic Agent Matching:

    • - Analyze input characteristics
    - Select most appropriate agent types
    - Configure specialized sub-agents dynamically
  • Expertise Routing:

    • 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

    2. Context Management and State Passing


  • Contextual Intelligence:

    • - Maintain shared context across agent interactions
    - Pass refined insights between agents
    - Support incremental review refinement
  • Context Propagation Model:

    • class ReviewContext:
          def __init__(self, target, metadata):
              self.target = target
              self.metadata = metadata
              self.agent_insights = {}
          def update_insights(self, agent_type, insights):
              self.agent_insights[agent_type] = insights

    3. Parallel vs Sequential Execution


  • Hybrid Execution Strategy:

    • - Parallel execution for independent reviews
    - Sequential processing for dependent insights
    - Intelligent timeout and fallback mechanisms
  • Execution Flow:

    • def execute_review(review_context):
          # Parallel independent agents
          parallel_agents = [
              "code-quality-reviewer",
              "security-auditor"
          ]
          # Sequential dependent agents
          sequential_agents = [
              "architecture-reviewer",
              "performance-optimizer"
          ]

    4. Result Aggregation and Synthesis


  • Intelligent Consolidation:

    • - Merge insights from multiple agents
    - Resolve conflicting recommendations
    - Generate unified, prioritized report
  • Synthesis Algorithm:

    • def synthesize_review_insights(agent_results):
          consolidated_report = {
              "critical_issues": [],
              "important_issues": [],
              "improvement_suggestions": []
          }
          # Intelligent merging logic
          return consolidated_report

    5. Conflict Resolution Mechanism


  • Smart Conflict Handling:

    • - Detect contradictory agent recommendations
    - Apply weighted scoring
    - Escalate complex conflicts
  • Resolution Strategy:

    • def resolve_conflicts(agent_insights):
          conflict_resolver = ConflictResolutionEngine()
          return conflict_resolver.process(agent_insights)

    6. Performance Optimization


  • Efficiency Techniques:

    • - Minimal redundant processing
    - Cached intermediate results
    - Adaptive agent resource allocation
  • Optimization Approach:

    • def optimize_review_process(review_context):
          return ReviewOptimizer.allocate_resources(review_context)

    7. Quality Validation Framework


  • Comprehensive Validation:

    • - Cross-agent result verification
    - Statistical confidence scoring
    - Continuous learning and improvement
  • Validation Process:

    • def validate_review_quality(review_results):
          quality_score = QualityScoreCalculator.compute(review_results)
          return quality_score > QUALITY_THRESHOLD

    Example Implementations

    1. Parallel Code Review Scenario


    multi_agent_review(
        target="/path/to/project",
        agents=[
            {"type": "security-auditor", "weight": 0.3},
            {"type": "architecture-reviewer", "weight": 0.3},
            {"type": "performance-analyst", "weight": 0.2}
        ]
    )

    2. Sequential Workflow


    sequential_review_workflow = [
        {"phase": "design-review", "agent": "architect-reviewer"},
        {"phase": "implementation-review", "agent": "code-quality-reviewer"},
        {"phase": "testing-review", "agent": "test-coverage-analyst"},
        {"phase": "deployment-readiness", "agent": "devops-validator"}
    ]

    3. Hybrid Orchestration


    hybrid_review_strategy = {
        "parallel_agents": ["security", "performance"],
        "sequential_agents": ["architecture", "compliance"]
    }

    Reference Implementations

  • Web Application Security Review

  • Microservices Architecture Validation

    • Best Practices and Considerations

  • Maintain agent independence

  • Implement robust error handling

  • Use probabilistic routing

  • Support incremental reviews

  • Ensure privacy and security

    • Extensibility

    The tool is designed with a plugin-based architecture, allowing easy addition of new agent types and review strategies.

    Invocation

    Target for review: $ARGUMENTS

      error-debugging-multi-agent-review - Agent Skills