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Get Started Free →alirezarezvani/agent-designer
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 agen
Passed 19 of 22 test cases with the skill, and 8 of 22 without it.
and 12 more in the full report
12 fixed8 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to run a detailed evaluation on a pilot log file named `pilot_run.json` and save the output with prefix `pilot_eval`. What exact command should be run?
| Checked | Without skill | With skill |
|---|---|---|
| Formulates command using python3 agent_evaluator.py pilot_run.json --detailed -o pilot_eval. | Missed | Passed |
The instructions your model reads when the skill loads.
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 NOT to use: Claude Code Workflow-tool automations → workflow-builder; single-agent workflow scaffolds → agent-workflow-designer; multi-agent fan-out at runtime → agenthub.
| 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.
All paths relative to this skill folder. Each step's JSON output is the next step's design input.
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.
test cases passed, vs 8 / 22 without
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