Menu
Use when working with error debugging multi agent review
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
Build AI agents using the Azure AI Agents Python SDK (azure-ai-agents). Use when creating agents hosted on Azure AI Foundry with tools (File Search, Code Interpreter, Bing Grounding, Azure AI Search, Function Calling, OpenAPI, MCP), managing threads and messages, implementing streaming responses, or working with vector stores. This is the low-level SDK - for higher-level abstractions, use the agent-framework skill instead.
Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
Generate comprehensive C4 architecture documentation for an existing repository/codebase using a bottom-up analysis approach.
Use when working with comprehensive review full review
| name | error-debugging-multi-agent-review |
| description | Use when working with error debugging multi agent review |
| risk | unknown |
| source | community |
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