This is an example custom assistant that will help you complete the Python onboarding in VS Code. After trying it out, feel free to experiment with other blocks or create your own custom assistant.
relace
mistral
voyage
voyage
OpenAI
docker
You have a short session-based memory, so you can use the memory tools (if present) to persist/access data between sessions. Use memory to store insights, notes, and context that is especially valuable for quick access.
- Follow Next.js patterns, use app router and correctly use server and client components.
- Use Tailwind CSS for styling.
- Use Shadcn UI for components.
- Use TanStack Query (react-query) for frontend data fetching.
- Use React Hook Form for form handling.
- Use Zod for validation.
- Use React Context for state management.
- Use Prisma for database access.
- Follow AirBnB style guide for code formatting.
- Use PascalCase when creating new React files. UserCard, not user-card.
- Use named exports when creating new react components.
- DO NOT TEACH ME HOW TO SET UP THE PROJECT, JUMP STRAIGHT TO WRITING COMPONENTS AND CODE.
- Optimize indexes to improve query execution speed.
- Avoid N+1 queries and suggest more efficient alternatives.
- Recommend normalization or denormalization strategies based on use cases.
- Implement transaction management where necessary to ensure data consistency.
- Suggest methods for monitoring database performance.
I am using the Next.js App Router v15.x.x
<!-- Sequential Thinking Workflow -->
<assistant>
<toolbox>
<mcp_server name="sequential-thinking"
role="workflow_controller"
execution="sequential-thinking"
description="Initiate the sequential-thinking MCP server">
<tool name="STEP" value="1">
<description>Gather context by reading the relevant file(s).</description>
<arguments>
<argument name="instructions" value="Seek proper context in the codebase to understand what is required. If you are unsure, ask the user." type="string" required="true"/>
<argument name="should_read_entire_file" type="boolean" default="true" required="false"/>
</arguments>
<result type="string" description="Context gathered from the file(s). Output can be passed to subsequent steps."/>
</tool>
<tool name="STEP" value="2">
<description>Generate code changes based on the gathered context (from STEP 1).</description>
<arguments>
<argument name="instructions" value="Generate the proper changes/corrections based on context from STEP 1." type="string" required="true"/>
<argument name="code_edit" type="object" required="true" description="Output: The proposed code modifications."/>
</arguments>
<result type="object" description="The generated code changes (code_edit object). Output can be passed to subsequent steps."/>
</tool>
<tool name="STEP" value="3">
<description>Review the generated changes (from STEP 2) and suggest improvements.</description>
<arguments>
<argument name="instructions" type="string" value="Review the changes applied in STEP 2 for gaps, correctness, and adherence to guidelines. Suggest improvements or identify any additional steps needed." required="true"/>
</arguments>
<result type="string" description="Review feedback, suggested improvements, or confirmation of completion. Final output of the workflow."/>
</tool>
</mcp_server>
</toolbox>
</assistant>
Create an API route with the following functionality.
https://log-api.newrelic.com/log/v1
npx -y @executeautomation/playwright-mcp-server
npx -y @browsermcp/mcp@latest
npx -y @modelcontextprotocol/server-memory
npx -y @modelcontextprotocol/server-github
npx -y @modelcontextprotocol/server-filesystem ${{ secrets.ctan-dev/ctan-dev-first-assistant/anthropic/filesystem-mcp/PATH }}
npx -y @modelcontextprotocol/server-brave-search
npx -y tavily-mcp@0.1.4
docker run --rm -i mcp/sequentialthinking