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Use when working with error debugging multi agent review
Use when CrossFrame Suite routes explicit Chinese casebook work: turning materials into reusable cases, anonymized entries, mechanisms, and retrieval indexes.
Use only when the user explicitly names crossframe-critical for a Chinese structural critique dossier, article plan, or long-form critical essay.
Use when CrossFrame Suite routes explicit Chinese proposition testing, debate analysis, hidden-premise review, rebuttal design, or withdrawal condition checks.
Use when CrossFrame Suite routes explicit Chinese reader replies, editor responses, consultation-style short answers, or boundary-aware structural advice.
Use when explicit CrossFrame work needs a Chinese critical insight essay, commentary, concept essay, public piece, or structure-to-article draft after diagnosis.
Use when CrossFrame Suite routes explicit Chinese notes for books, theories, articles, excerpts, bidirectional reading, absorption, or conflict mapping.
| name | error-debugging-multi-agent-review |
| description | Use when working with error debugging multi agent review |
| risk | unknown |
| source | community |
| date_added | 2026-02-27 |
resources/implementation-playbook.md.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.
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:
$ARGUMENTS: Target code/project for review
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
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
def execute_review(review_context):
# Parallel independent agents
parallel_agents = [
"code-quality-reviewer",
"security-auditor"
]
# Sequential dependent agents
sequential_agents = [
"architecture-reviewer",
"performance-optimizer"
]
def synthesize_review_insights(agent_results):
consolidated_report = {
"critical_issues": [],
"important_issues": [],
"improvement_suggestions": []
}
# Intelligent merging logic
return consolidated_report
def resolve_conflicts(agent_insights):
conflict_resolver = ConflictResolutionEngine()
return conflict_resolver.process(agent_insights)
def optimize_review_process(review_context):
return ReviewOptimizer.allocate_resources(review_context)
def validate_review_quality(review_results):
quality_score = QualityScoreCalculator.compute(review_results)
return quality_score > QUALITY_THRESHOLD
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}
]
)
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"}
]
hybrid_review_strategy = {
"parallel_agents": ["security", "performance"],
"sequential_agents": ["architecture", "compliance"]
}
The tool is designed with a plugin-based architecture, allowing easy addition of new agent types and review strategies.
Target for review: $ARGUMENTS