Ai Agents Test
Test your multi-agent system with a sample task, showing agent handoffs,
- Type
- Slash Command
- Repository
- jeremylongshore/tons-of-skills-marketplace
- 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
- 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.
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.
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
fi2. 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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