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agent-designer

Design multi-agent systems, create agent architectures, define communication patterns, and build autonomous agent workflows with structured evaluation frameworks.

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SebastianLuser/claude-code-arcane스타 3(GitHub 3 + cccskills 0)

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포함된 파일(14)

  • SKILL.md3.2 KB
  • agent_evaluator.py52.0 KB
  • agent_planner.py38.0 KB
  • assets/sample_execution_logs.json15.5 KB
  • assets/sample_system_requirements.json2.2 KB
  • assets/sample_tool_descriptions.json15.7 KB
  • expected_outputs/sample_agent_architecture.json13.6 KB
  • expected_outputs/sample_evaluation_report.json15.0 KB
  • expected_outputs/sample_tool_schemas.json11.7 KB
  • README.md12.2 KB
  • references/agent_architecture_patterns.md9.6 KB
  • references/evaluation_methodology.md20.0 KB
  • references/tool_design_best_practices.md13.4 KB
  • tool_schema_generator.py36.9 KB

SKILL.md(원문)

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Agent Designer

Comprehensive toolkit for designing, architecting, and evaluating multi-agent systems. Provides structured approaches to agent architecture patterns, tool design principles, communication strategies, and performance evaluation frameworks.

Core Capabilities

Agent Architecture Patterns

  • Single Agent -- Simple, focused tasks with clear boundaries. Minimal complexity, easy debugging.
  • Supervisor -- One supervisor coordinating multiple specialist agents. Clear command structure, centralized decisions.
  • Swarm -- Multiple autonomous agents with shared objectives. High parallelism, fault tolerance, emergent intelligence.
  • Hierarchical -- Tree structure with managers and workers at different levels. Natural organizational mapping.
  • Pipeline -- Agents in sequential processing stages. Clear data flow, specialized optimization per stage.

Agent Role Definition

  • Identity: Name, purpose statement, core competencies
  • Responsibilities: Primary tasks, decision boundaries, success criteria
  • Capabilities: Required tools, knowledge domains, processing limits
  • Interfaces: Input/output formats, communication protocols
  • Constraints: Security boundaries, resource limits, operational guidelines

Common Agent Archetypes

  • Coordinator -- Orchestrates workflows, makes high-level decisions, handles escalations
  • Specialist -- Deep domain expertise, optimized tools, clear handoff protocols
  • Interface -- External interactions, protocol translation, auth management
  • Monitor -- Health monitoring, metrics collection, anomaly detection, compliance

Implementation Guidelines

Architecture Decision Process

  1. Requirements Analysis: Understand system goals, constraints, scale
  2. Pattern Selection: Choose appropriate architecture pattern
  3. Agent Design: Define roles, responsibilities, interfaces
  4. Tool Architecture: Design tool schemas and error handling
  5. Communication Design: Select message patterns and protocols
  6. Safety Implementation: Build guardrails and validation
  7. Evaluation Planning: Define success metrics and monitoring
  8. Deployment Strategy: Plan scaling and failure handling

Guardrails and Safety

  • Input Validation: Schema enforcement, content filtering, rate limiting, auth checks
  • Output Filtering: Content moderation, consistency validation, audit logging
  • Human-in-the-Loop: Approval workflows, escalation triggers, override mechanisms

Reference Documentation

See references/agent_architecture_patterns.md for detailed pattern catalog with scaling considerations and failure handling.

See references/tool_design_best_practices.md for schema design, error handling patterns, and idempotency requirements.

See references/evaluation_methodology.md for task completion metrics, quality assessment, cost analysis, and latency distribution frameworks.

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