Agent designer

--- name: "agent -designer" description: "Use when the user wants to design a multi-agent system, select an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or analyze agent execution logs to identify bottlenecks related to cost, latency, and failures. 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, generate schemas for, and evaluate multi-agent systems using three deterministic tools. The scripts are theworkflow:do not design an architecture from scratch when the planner can generate one based on requirements. ## When to use - Designing a new multi-agentsystem from requirements (pattern choice, roles, communications) - Generating provider-ready tool schemas (in Anthropic, and 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-agentworkflow-scaffolds → agent-workflow-designer; multi-agent-fan-out at runtime → agenthub. ## Pattern decision table | Choose | When | Watch out for | |---|---|---| | Singleagent| One bounded task, < ~5 tools | Don’t add unagentss you don’t need | | Supervisor | Central decomposition; specialists report back | Supervisor becomes the bottleneck | | Pipeline | Strictly sequential stages with handoffs | Rigid order; slowest stage limits throughput | | Hierarchical | Multiple organizational layers, > ~8 agents | Communication overhead per level | | Swarm | Parallel peers, fault tolerance over predictability | Hard to debug; requires consensus rules | The planner applies this scoring deterministically:run it rather than choosing based on intuition. ## Workflow All paths are relative to this folder: skill. Each step’s JSON output serves as 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): ``bash 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/ sampletooldescriptions.json), then: `bash 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/ sampleexecutionlogs.json for a dry run): `bash 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: 1. toolschemagenerator.py --validate reports 0 invalid schemas. 2. 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. 3. Compare your outputs against expected_outputs / to confirm the schema shape you're consuming hasn't drifted. ## References - references/ agentarchitecturepatterns.md : pattern trade-offs in depth - references/ tooldesignbest_practices.md : schema, idempotency, error-handling rules - references/ evaluation_methodology.md` : metric definitions the eval