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Slash Command

Ai Agents Test

Test your multi-agent system with a sample task, showing agent handoffs,

Type
Slash Command
GitHub stars
2.8k
License
MIT
Repo last updated
Sep 27, 2026
Model
sonnet

What Ai Agents Test is

Ai Agents Test is a slash command published in the jeremylongshore/tons-of-skills-marketplace repository on GitHub, which has about 2.8k stars. The repository describes itself as: “Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.”

A slash command is a reusable prompt saved as a markdown file and run by typing its name after a slash. In Claude Code, custom commands have been merged into skills: a file in .claude/commands/ and a skill folder in .claude/skills/ both create the same kind of command, and existing command files keep working.

Ai Agents Test gives you a repeatable way to run the same instructions without retyping them, optionally with arguments.

How to install Ai Agents Test

Claude Code

  1. Download ai-agents-test.md from the repository.
  2. Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md.
  3. Run it by typing / followed by its name.

Claude Cowork

  1. Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it.
  2. In Customize → Skills, click +, then upload the ZIP.
  3. Run it from any task with / and the skill name.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from plugins/ai-ml/ai-sdk-agents/commands/ai-agents-test.md, shared under the repository's MIT license. Read the full file on GitHub.

You are an expert in multi-agent system testing and observability.

Mission

Test a multi-agent orchestration system by:

  • Running a sample task through the agent network
  • Showing real-time agent handoffs and routing
  • Displaying performance metrics (time, handoff count)
  • Validating agent coordination and output quality
  • Identifying bottlenecks or issues

Usage

User invokes: /ai-agents-test "Task description"

Examples:

  • /ai-agents-test "Build a REST API with authentication"
  • /ai-agents-test "Research best practices for React performance"
  • /ai-agents-test "Debug this authentication error"

Test Process

1. Validate Setup

First check if the multi-agent project exists:

# Check for required files
if [ -f "index.ts" ] && [ -d "agents" ]; then
  echo "✅ Multi-agent project found"
else
  echo "❌ Multi-agent project not found"
  echo "💡 Run /ai-agents-setup first to create the project"
  exit 1
fi

2. Parse Test Query

Extract the task from user input:

  • If provided: Use their task
  • If empty: Use default test task

Default tasks by category:

  • Code generation: "Build a TODO API with CRUD operations"
  • Research: "Research microservices best practices"
  • Debug: "Why is my JWT authentication failing?"
  • Review: "Review this code for security issues"

3. Start Test Execution

Create a test runner script:

test-runner.ts

import { runMultiAgentTask } from './index';

interface TestMetrics {
  startTime: number;
  endTime?: number;
  handoffs: Array<{
    from: string;
    to: string;
    reason: string;
    timestamp: number;
  }>;
  agentsInvolved: Set<string>;
  totalDuration?: number;
}

async function testMultiAgentSystem(task: string) {
  console.log('🚀 Multi-Agent System Test\n');
  console.log('━'.repeat(60));
…

4. Enhanced Orchestration with Metrics

Update index.ts to emit events for testing:

export async function runMultiAgentTask(task: string, options?: {
  onHandoff?: (event: HandoffEvent) => void;
  onComplete?: (result: any) => void;
  verbose?: boolean;
}) {
  const verbose = options?.verbose ?? true;

  if (verbose) {
    console.log(`\n🤖 Starting multi-agent task: ${task}\n`);
  }

  const handoffs: Array<{
    from: string;
    to: string;
    reason: string;
    timestamp: number;
  }> = [];
…

5. Execute Test

Run the test:

# Using ts-node
ts-node test-runner.ts "Build a REST API with authentication"

# Or using npm script
npm run test:agents "Build a REST API with authentication"

6. Display Real-Time Progress

Show live updates during execution:

🚀 Multi-Agent System Test

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 Task: Build a REST API with authentication
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔄 Handoff: coordinator → researcher
   Reason: Need to research authentication best practices

🔄 Handoff: researcher → coder
   Reason: Research complete, ready to implement

🔄 Handoff: coder → reviewer
   Reason: Implementation complete, needs review

🔄 Handoff: reviewer → coordinator
   Reason: Review complete, all checks passed
…

7. Add Test Script to package.json

{
  "scripts": {
    "test:agents": "ts-node test-runner.ts"
  }
}

8. Create Pre-defined Test Scenarios

Create tests/scenarios.json:

{
  "scenarios": [
    {
      "name": "Code Generation",
      "task": "Build a REST API with authentication and CRUD operations",
      "expectedAgents": ["coordinator", "researcher", "coder", "reviewer"],
      "expectedHandoffs": 4,
      "maxDuration": 60000
    },
    {
      "name": "Research Task",
      "task": "Research best practices for microservices architecture",
      "expectedAgents": ["coordinator", "researcher"],
      "expectedHandoffs": 2,
      "maxDuration": 20000
    },
    {
      "name": "Debug Task",
…

9. Troubleshooting

If test fails, check:

# 1. Environment variables
if [ -z "$ANTHROPIC_API_KEY" ]; then
  echo "❌ Error: ANTHROPIC_API_KEY not set"
  echo "💡 Add your API key to .env file"
  exit 1
fi

# 2. Dependencies installed
if [ ! -d "node_modules/@ai-sdk-tools/agents" ]; then
  echo "❌ Error: Dependencies not installed"
  echo "💡 Run: npm install"
  exit 1
fi

# 3. Agents registered
if ! grep -q "researcher" index.ts; then
  echo "⚠️  Warning: Not all agents registered in index.ts"
fi

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Ai Agents Test?

Ai Agents Test is a slash command for Claude Code and Claude Cowork from the jeremylongshore/tons-of-skills-marketplace repository on GitHub. Test your multi-agent system with a sample task, showing agent handoffs,

How do I install Ai Agents Test in Claude Code?

Download ai-agents-test.md from the repository. Save it to ~/.claude/commands/ (all projects) or .claude/commands/ (one project). As a skill, you can instead save it as ~/.claude/skills/<name>/SKILL.md. Run it by typing / followed by its name.

Can I use Ai Agents Test in Claude Cowork?

Turn the command into a skill: create a folder with the file saved as SKILL.md and zip it. In Customize → Skills, click +, then upload the ZIP. Run it from any task with / and the skill name.

Is Ai Agents Test safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.