agent-designer

name agent-designer description Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer). Agent Designer — Multi-Agent System Architecture Design, schema-generate, and evaluate multi-agent systems with three deterministic tools. The scripts are the workflow — do not freehand an architecture when the planner can score one from requirements. When to use Designing a new multi-agent system from requirements (pattern choice, roles, comms) Generating provider-ready tool schemas (Anthropic + OpenAI formats) from plain tool descriptions Evaluating execution logs: success rate, latency distribution, cost, bottlenecks When NOT to use: Claude Code Workflow-tool automations → workflow-builder ; single-agent workflow scaffolds → agent-workflow-designer ; multi-agent fan-out at runtime → agenthub . Pattern decision table Choose When Watch out for Single agent One bounded task, < ~5 tools Don't add agents you don't need Supervisor Central decomposition, specialists report back Supervisor becomes the bottleneck Pipeline Strictly sequential stages with handoffs Rigid order; slowest stage gates throughput Hierarchical Multiple org layers, > ~8 agents Communication overhead per level Swarm Parallel peers, fault tolerance over predictability Hard to debug; needs consensus rules The planner applies this scoring deterministically — run it rather than picking by feel. Workflow All paths relative to this skill folder. Each step's JSON output is the next step's design input. 1. Design the architecture Write a requirements JSON (copy assets/sample_system_requirements.json — keys: goal , tasks[] , constraints{max_response_time, budget_per_task, concurrent_tasks} , team_size ): python3 agent_planner.py requirements.json --format json -o arch Emits arch.json with architecture_design (pattern, agents, communication links), mermaid_diagram , and implementation_roadmap . Read architecture_design.pattern and the per-agent role list; present the mermaid diagram to the user. 2. Generate tool schemas Describe each agent's tools in plain JSON (copy assets/sample_tool_descriptions.json ), then: python3 tool_schema_generator.py tool_descriptions.json --validate -o tools Emits tools.json ( tool_schemas , validation_summary ) plus provider-specific tools_anthropic.json / tools_openai.json . Gate: every tool must print ✓ Valid . Fix any invalid schema before proceeding — never hand an agent an unvalidated schema. 3. Evaluate execution logs Once the system runs (or against assets/sample_execution_logs.json for a dry run): python3 agent_evaluator.py execution_logs.json --detailed -o eval Emits eval.json with summary , agent_metrics , bottleneck_analysis , error_analysis , cost_breakdown , sla_compliance , and optimization_recommendations , plus split files ( eval_errors.json , eval_recommendations.json ). 4. Verification loop The design is not done until: tool_schema_generator.py --validate reports 0 invalid schemas. agent_evaluator.py on a pilot run reports 0 critical issues (the tool prints CRITICAL: N critical issues when found). If N > 0, apply the top item in eval_recommendations.json , re-run the pilot, and re-evaluate. Compare your outputs against expected_outputs/ to confirm the schema shape you're consuming hasn't drifted. References references/agent_architecture_patterns.md — pattern trade-offs in depth references/tool_design_best_practices.md — schema, idempotency, error-handling rules references/evaluation_methodology.md — metric definitions the evaluator implements