Agent Designer

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

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Audit 0.4.1 · source sha 6e3fcbdbb5cf · 19 Oct 2026Report an issue or request a re-scan

• Publisherborghei GitHub userOwner of the repository this skill is read from
• Source folderborghei/Claude-Skills/engineering/agent-designerThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict6e3fcbdbb5cfA new audit runs when the source changes
• Repository statusNot archivedLast push 12 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent…

---
name: agent-designer
description: >
  Designs multi-agent system architectures with orchestration patterns, tool
  schemas, and performance evaluation. Use when building AI agent systems,
  designing agent workflows, creating tool schemas, or evaluating agent
  performance.
license: MIT + Commons Clause
metadata:
  version: 1.1.0
  author: borghei
  category: engineering
  domain: ai-agents
  tier: POWERFUL
  updated: 2026-06-17
---
# Agent Designer - Multi-Agent System Architecture

A toolkit for designing, architecting, and evaluating multi-agent systems. It provides structured approaches to agent architecture patterns, tool design principles, communication strategies, and performance evaluation frameworks for building robust, scalable AI agent systems.

## Core Capabilities

- **Architecture pattern selection** — single agent, supervisor, swarm, hierarchical, and pipeline patterns with use-case fit and trade-offs.
- **Agent role definition** — identity, responsibilities, capabilities, interfaces, and constraints; common archetypes (coordinator, specialist, interface, monitor).
- **Tool design** — schema design, error handling, idempotency requirements, and validation rules.
- **Communication & orchestration** — message passing, shared state, event-driven architecture; centralized, decentralized, and hybrid orchestration.
- **Guardrails & safety** — input validation, output filtering, and human-in-the-loop checkpoints.
- **Evaluation frameworks** — task completion, quality, cost, and latency metrics with bottleneck analysis.
- **Memory, scaling & failure handling** — short/long/shared memory, horizontal/vertical scaling, retries, fallbacks, and circuit breakers.

## When to Use

- Building AI agent systems or designing multi-agent workflows.
- Creating tool schemas for OpenAI function calling or Anthropic tool use.
- Selecting an architecture pattern for a new system.
- Evaluating agent performance from execution logs.

## Clarify First

Before designing the system, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **System goal & scale** — the task the agents perform and expected load (drives which architecture pattern: single, supervisor, swarm, hierarchical, or pipeline)
- [ ] **Tool protocol target** — OpenAI function calling vs Anthropic tool use (sets the schema format `tool_schema_generator.py` emits)
- [ ] **Optimization priority** — cost, latency, or quality (determines agent roles, model tiers, and which metrics the evaluator weights)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Tools

| Tool | Purpose | Command |
|------|---------|---------|
| `agent_planner.py` | Design architecture from requirements (pattern, roles, topology, Mermaid diagram, roadmap) | `python agent_planner.py requirements.json -o my_system --format both` |
| `agent_evaluator.py` | Evaluate performance from execution logs (success, cost, latency, bottlenecks) | `python agent_evaluator.py execution_logs.json -o perf_report --format both --detailed` |
| `tool_schema_generator.py` | Generate OpenAI/Anthropic tool schemas with validation | `python tool_schema_generator.py tools.json -o my_tools --format both --validate` |

## References

Load the reference that matches the task — keep this file lean and pull detail on demand:

- **[references/core-capabilities.md](references/core-capabilities.md)** — the full Core Capabilities catalog (architecture patterns, role definition, tool design, communication, guardrails, evaluation, orchestration, memory, scaling, failure handling) plus Implementation Guidelines. Read when designing any part of a system.
- **[references/agent_architecture_patterns.md](references/agent_architecture_patterns.md)** — deep catalog of architecture patterns with structure diagrams, characteristics, use cases, and implementation considerations. Read when selecting or comparing patterns.
- **[references/tool_design_best_practices.md](references/tool_design_best_practices.md)** — best practices for designing tools in multi-agent systems (single responsibility, idempotency, composability, schemas, error handling). Read when designing tools or schemas.
- **[references/evaluation_methodology.md](references/evaluation_methodology.md)** — full evaluation methodology across performance, reliability, cost, and satisfaction dimensions. Read when planning evaluation or interpreting reports.
- **[references/troubleshooting-and-tool-reference.md](references/troubleshooting-and-tool-reference.md)** — troubleshooting table, success criteria, and the complete CLI parameter reference for all three scripts. Read when a tool misbehaves or you need full command options.

## Scope & Limitations

**Covers:**
- Multi-agent architecture pattern selection (single agent, supervisor, swarm, hierarchical, pipeline)
- Agent role definition with responsibilities, capabilities, tools, and communication interfaces
- Tool schema generation in OpenAI and Anthropic formats with validation rules and error handling
- Performance evaluation from execution logs including bottleneck analysis and optimization recommendations

**Does NOT cover:**
- Runtime agent orchestration or execution engines (see `engineering/agent-workflow-designer` for workflow execution)
- LLM prompt engineering or system prompt design (see `engineering/prompt-engineer-toolkit`)
- MCP server implementation or protocol details (see `engineering/mcp-server-builder`)
- Self-improving agent feedback loops or autonomous learning (see `engineering/self-improving-agent`)

## Integration Points

| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| `engineering/agent-workflow-designer` | Workflow definitions consume architecture designs from Agent Designer | Agent roles and communication topology feed into workflow step definitions |
| `engineering/prompt-engineer-toolkit` | System prompts are crafted 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/borghei/Claude-Skills
git -C Claude-Skills checkout 6e3fcbdbb5cf

Works in

1. 2026-10-19
   !CAUTION B 89 / 100

   first audit · source 6e3fcbdbb5cf

Questions about Agent Designer

• github.com/borghei/Claude-Skills/tree/main/engineering/agent-des — the audited source at commit 6e3fcbdbb5cf and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology