mahdi-ahmadi/mahdi-ahmadi-first-assistant icon
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Published on 4/3/2025
My First Assistant

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.

Rules
Prompts
Models
Context
Data
anthropic Claude 3.7 Sonnet model icon

Claude 3.7 Sonnet

anthropic

200kinput·8.192koutput
anthropic Claude 3.5 Haiku model icon

Claude 3.5 Haiku

anthropic

200kinput·8.192koutput
mistral Codestral model icon

Codestral

mistral

voyage Voyage AI rerank-2 model icon

Voyage AI rerank-2

voyage

voyage voyage-code-3 model icon

voyage-code-3

voyage

You are a Python coding assistant. You should always try to - Use type hints consistently - Write concise docstrings on functions and classes - Follow the PEP8 style guide
- 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.
- 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.
You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting
- Follow Django style guide
- Avoid using raw queries
- Prefer the Django REST Framework for API development
- Prefer Celery for background tasks
- Prefer Redis for caching and task queues
- Prefer PostgreSQL for production databases
- Follow NestJS's modular architecture to ensure scalability and
maintainability.
- Use DTOs (Data Transfer Objects) to validate and type API requests.
- Implement Dependency Injection for better service management.
- Use the Repository pattern to separate data access logic from the rest of the application.
- Ensure that all REST APIs are well-documented with Swagger.
- Implement caching strategies to reduce database load.
- Suggest optimizations to improve PostgreSQL query performance.
Pythonhttps://docs.python.org/3/
Pandashttps://pandas.pydata.org/docs/
NumPyhttps://numpy.org/doc/stable/
Reacthttps://react.dev/reference/

Prompts

Learn more
Write Cargo test
Write unit test with Cargo
Use Cargo to write a comprehensive suite of unit tests for this function
Page
Creates a new Next.js page based on the description provided.
Create a new Next.js page based on the following description.
RAG Pipeline Design
Comprehensive retrieval-augmented generation system design
Design a RAG (Retrieval-Augmented Generation) system with:

Document Processing:
- Text extraction strategy
- Chunking approach with size and overlap parameters
- Metadata extraction and enrichment
- Document hierarchy preservation

Vector Store Integration:
- Embedding model selection and rationale
- Vector database architecture
- Indexing strategy
- Query optimization

Retrieval Strategy:
- Hybrid search (vector + keyword)
- Re-ranking methodology
- Metadata filtering capabilities
- Multi-query reformulation

LLM Integration:
- Context window optimization
- Prompt engineering for retrieval
- Citation and source tracking
- Hallucination mitigation strategies

Evaluation Framework:
- Retrieval relevance metrics
- Answer accuracy measures
- Ground truth comparison
- End-to-end benchmarking

Deployment Architecture:
- Caching strategies
- Scaling considerations
- Latency optimization
- Monitoring approach

The user's knowledge base has the following characteristics:
API route inspection
Analyzes API routes for security issues
Review this API route for security vulnerabilities. Ask questions about the context, data flow, and potential attack vectors. Be thorough in your investigation.
Client component
Create a client component.
Create a client component with the following functionality. If writing this as a server component is not possible, explain why.
Exploratory Data Analysis
Initial data exploration and key insights
Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Context

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@code
Reference specific functions or classes from throughout your project
@docs
Reference the contents from any documentation site
@diff
Reference all of the changes you've made to your current branch
@terminal
Reference the last command you ran in your IDE's terminal and its output
@problems
Get Problems from the current file
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@codebase
Reference the most relevant snippets from your codebase
@file
Reference any file in your current workspace
@url
Reference the markdown converted contents of a given URL
@currentFile
Reference the currently open file
@repo-map
Reference the outline of your codebase
@open
Reference the contents of all of your open files
@jira
Reference the conversation in a Jira issue
@greptile
Query a Greptile index of the current repo/branch
@clipboard
Reference recent clipboard items
@commit
@os
Reference the architecture and platform of your current operating system

Google Cloud Storage

${{ secrets.mahdi-ahmadi/mahdi-ahmadi-first-assistant/continuedev/google-cloud-storage-dev-data/GCP_SERVER_URL }}

Azure Blob Storage

${{ secrets.mahdi-ahmadi/mahdi-ahmadi-first-assistant/continuedev/azure-blob-storage-dev-data/AZURE_SERVER_URL }}

New Relic

https://log-api.newrelic.com/log/v1

S3

${{ secrets.mahdi-ahmadi/mahdi-ahmadi-first-assistant/continuedev/s3-dev-data/AWS_SERVER_URL }}

MCP Servers

Learn more

Docker MCP Postgres

docker run -i --rm mcp/postgres ${{ secrets.mahdi-ahmadi/mahdi-ahmadi-first-assistant/docker/mcp-postgres/POSTGRES_CONNECTION_STRING }}

Exa

npx -y exa-mcp-server

Memory

npx -y @modelcontextprotocol/server-memory

Playwright

npx -y @executeautomation/playwright-mcp-server

Docker MCP Slack

docker run -i --rm -e SLACK_BOT_TOKEN -e SLACK_TEAM_ID mcp/slack

Postgres

npx -y @modelcontextprotocol/server-postgres ${{ secrets.mahdi-ahmadi/mahdi-ahmadi-first-assistant/anthropic/postgres-mcp/CONNECTION_STRING }}