name: smoke-test
description: Create a Mastra project using create-mastra and smoke test the studio in Chrome
model: claude-opus-4-5

Smoke Test Skill

Creates a new Mastra project using create-mastra@<tag> and performs smoke testing of the Mastra Studio in Chrome.

Usage

/smoke-test --directory <path> --name <project-name> --tag <version> [--pm <package-manager>] [--llm <provider>]
/smoke-test -d <path> -n <project-name> -t <version> [-p <package-manager>] [-l <provider>]

Parameters

Examples

# Minimal (required params only)
/smoke-test -d ~/projects -n my-test-app -t latest

# Full specification
/smoke-test --directory ~/projects --name my-test-app --tag alpha --pm pnpm --llm anthropic

# Using short flags
/smoke-test -d ./projects -n smoke-test-app -t 0.10.6 -p bun -l openai

Step 0: Parameter Validation (MUST RUN FIRST)

CRITICAL: Before proceeding, parse the ARGUMENTS and validate:

1. Parse arguments from the ARGUMENTS string provided above
2. Check required parameters:
3. --directory or -d: REQUIRED - fail if missing
4. --name or -n: REQUIRED - fail if missing
5. --tag or -t: REQUIRED - fail if missing
6. Apply defaults for optional parameters:
7. --pm or -p: Default to npm if not provided
8. --llm or -l: Default to openai if not provided
9. Validate values:
10. pm must be one of: npm, yarn, pnpm, bun
11. llm must be one of: openai, anthropic, groq, google, cerebras, mistral
12. directory must exist (or will be created)
13. name should be a valid directory name (no spaces, special chars)

If validation fails: Stop and show usage help with the missing/invalid parameters.

If -h or --help is passed: Show this usage information and stop.

Prerequisites

This skill requires the Claude-in-Chrome MCP server for browser automation. Ensure it's configured and running.

Execution Steps

Step 1: Create the Mastra Project

Run the create-mastra command with explicit parameters to avoid interactive prompts:

# For npm
npx create-mastra@<tag> <project-name> -c agents,tools,workflows,scorers -l <llmProvider> -e

# For yarn
yarn create mastra@<tag> <project-name> -c agents,tools,workflows,scorers -l <llmProvider> -e

# For pnpm
pnpm create mastra@<tag> <project-name> -c agents,tools,workflows,scorers -l <llmProvider> -e

# For bun
bunx create-mastra@<tag> <project-name> -c agents,tools,workflows,scorers -l <llmProvider> -e

Flags explained:

• -c agents,tools,workflows,scorers - Include all components
• -l <provider> - Set the LLM provider
• -e - Include example code

Being explicit with all parameters ensures the CLI runs non-interactively.

Wait for the installation to complete. This may take 1-2 minutes depending on network speed.

Step 2: Verify Project Structure

After creation, verify the project has:

• package.json with mastra dependencies
• src/mastra/index.ts exporting a Mastra instance
• .env file (may need to be created)

Step 2.5: Add Agent Network for Network Mode Testing

To enable Network mode testing, add an agent network configuration:

1. Create activity-agent.ts in src/mastra/agents/:

import { Agent } from '@mastra/core/agent';
import { Memory } from '@mastra/memory';

export const activityAgent = new Agent({
  id: 'activity-agent',
  name: 'Activity Agent',
  instructions: `You are a helpful activity planning assistant that suggests activities based on weather conditions.`,
  model: '<provider>/<model>', // e.g., 'openai/gpt-4o'
  memory: new Memory(),
});

1. Create planner-network.ts in src/mastra/agents/:

import { Agent } from '@mastra/core/agent';
import { Memory } from '@mastra/memory';
import { weatherAgent } from './weather-agent';
import { activityAgent } from './activity-agent';

export const plannerNetwork = new Agent({
  id: 'planner-network',
  name: 'Planner Network',
  instructions: `You are a coordinator that manages weather and activity agents.`,
  model: '<provider>/<model>',
  agents: { weatherAgent, activityAgent }, // This makes it a network agent
  memory: new Memory(), // Memory is REQUIRED for network agents
});

1. Update index.ts to register the new agents:

import { activityAgent } from './agents/activity-agent';
import { plannerNetwork } from './agents/planner-network';

// In Mastra config:
agents: { weatherAgent, activityAgent, plannerNetwork },

Note: Network mode requires agents property (sub-agents) and memory (mandatory).

Step 3: Configure Environment Variables

Based on the selected LLM provider, check for the required API key:

Check in this order:

1. Check global environment first: Run echo $<ENV_VAR_NAME> to see if the key is already set globally
2. If set globally, the project will inherit it - no .env file needed

3. Skip to Step 4

4. Check project .env file: If not set globally, check if .env exists in the project and contains the key

5. Ask user only if needed: If the key is not available globally or in .env:

6. Ask the user for the API key
7. Create the .env file with the provided key

Only check for the ONE key matching the selected provider - don't check for all providers.

Step 4: Start the Development Server

Navigate to the project directory and start the dev server:

cd <directory>/<project-name>
<packageManager> run dev

The server typically starts on http://localhost:4111. Wait for the server to be ready before proceeding.

Step 5: Smoke Test the Studio

Use the Chrome browser automation tools to test the Mastra Studio.

5.1 Initial Setup

1. Get browser context using tabs_context_mcp
2. Create a new tab using tabs_create_mcp
3. Navigate to http://localhost:4111

5.2 Test Checklist

Perform the following smoke tests using the Chrome automation tools:

Navigation & Basic Loading

• [ ] Studio loads successfully (page contains "Mastra Studio" or shows agents list)
• [ ] Take a screenshot of the home page

Agents Page (/agents)

• [ ] Navigate to agents page
• [ ] Verify at least one agent is listed (the example agent from --default)
• [ ] Take a screenshot

Agent Detail (/agents/<agentId>/chat)

• [ ] Click on an agent to view details
• [ ] Verify the agent overview panel loads
• [ ] Verify model settings panel is visible
• [ ] Take a screenshot

Agent Chat

• [ ] Send a test message to the agent (e.g., "Hello, can you help me?")
• [ ] Wait for response
• [ ] Verify response appears in the chat
• [ ] Take a screenshot of the conversation

Network Mode (/agents/planner-network/chat)

• [ ] Navigate to the planner-network agent
• [ ] Select "Network" in Chat Method settings
• [ ] Send a message: "What activities can I do in [city] based on the weather?"
• [ ] Verify network coordination (shows weatherAgent indicator)
• [ ] Verify completion check shows success
• [ ] Take a screenshot

Tools Page (/tools)

• [ ] Navigate to tools page
• [ ] Verify tools list loads (should show weatherTool)
• [ ] Take a screenshot

Tool Execution (/tools/weatherTool)

• [ ] Click on the weatherTool to open detail page
• [ ] Find the city input field and enter a test city (e.g., "Tokyo")
• [ ] Click Submit button
• [ ] Wait for execution to complete
• [ ] Verify JSON output appears with weather data (temp, condition, etc.)
• [ ] Take a screenshot

Workflows Page (/workflows)

• [ ] Navigate to workflows page
• [ ] Verify workflows list loads (should show weatherWorkflow)
• [ ] Take a screenshot

Workflow Execution (/workflows/weatherWorkflow)

• [ ] Click on the weatherWorkflow to open detail page
• [ ] Verify visual graph displays (shows workflow steps)
• [ ] Find the city input field and enter a test city (e.g., "London")
• [ ] Click Run button
• [ ] Wait for execution to complete
• [ ] Verify steps show success (green checkmarks)
• [ ] Click to view JSON output modal
• [ ] Verify execution details with timing appear
• [ ] Take a screenshot

Settings Page (/settings)

• [ ] Navigate to settings page
• [ ] Verify settings page loads
• [ ] Take a screenshot

Observability Page (/observability)

• [ ] Navigate to observability page
• [ ] Verify traces list shows recent activity (from previous tests)
• [ ] Click on a trace to view details
• [ ] Verify timeline view shows steps and timing
• [ ] Take a screenshot

Scorers Page (/scorers)

• [ ] Navigate to scorers page
• [ ] Verify scorers list loads (shows 3 example scorers)
• [ ] Take a screenshot

Scorer Detail (use direct URL navigation)

• [ ] Navigate directly to /scorers/completeness-scorer (don't click - use URL navigation)
• [ ] Verify scorer detail page loads with name, description, and scores table
• [ ] Take a screenshot
• [ ] Note: Use direct URL navigation for scorer details due to client-side routing timing issues

Additional Pages (verify load only)

• [ ] Templates page (/templates) - Gallery of starter templates
• [ ] Request Context page (/request-context) - JSON editor
• [ ] Processors page (/processors) - Empty state OK
• [ ] MCP Servers page (/mcps) - Empty state OK

5.3 Report Results

After completing all tests, provide a summary:

• Total tests passed/failed
• Any errors encountered
• Screenshots captured
• Recommendations for issues found

Quick Reference

Troubleshooting

Server won't start

• Verify .env has required API key
• Check if port 4111 is available
• Try <pm> install to reinstall dependencies

Browser can't connect

• Wait a few seconds for server to fully start
• Check terminal for server ready message
• Verify no firewall blocking localhost

Agent chat fails

• Verify API key is valid
• Check server logs for errors
• Ensure LLM provider API is accessible

Studio Routes

Notes

• The -e flag includes example agents, making smoke testing meaningful
• If the user doesn't specify an LLM provider, default to OpenAI as it's most common
• Take screenshots at each major step for documentation/debugging
• Keep the dev server running in the background during testing
• Always use explicit flags (-c, -l, -e) to ensure non-interactive execution
• Network mode requires the agents property AND memory in the Agent constructor
• Scorer detail pages may have issues with browser automation but work manually
• Observability traces appear automatically after running agents or workflows