Data Science & ML Agent Skills

PyTorch Docstring Formatter

Generates and formats standardized PyTorch docstrings following Sphinx and reStructuredText conventions.

Tisane Automation

Automates advanced natural language processing tasks including text moderation, sentiment analysis, and language detection via the Tisane API.

ArXiv Research Assistant

Analyzes ArXiv papers by fetching LaTeX source code and generating project-specific summaries for technical implementation.

Streamlit Feature Implementation

Streamlines the addition of new elements, widgets, and cross-stack functionality to the Streamlit framework.

Financial Modeling Suite

Constructs advanced financial models including DCF analysis, sensitivity testing, and Monte Carlo simulations for professional investment valuation.

CrewAI Multi-Agent Orchestration

Builds and orchestrates collaborative multi-agent AI teams using the CrewAI framework and Composio toolkits.

Startup Financial Modeling

Builds comprehensive 3-5 year financial models with revenue projections, burn rate calculations, and scenario planning for early-stage startups.

GPT Researcher Development

Empowers developers to build, customize, and integrate autonomous AI research agents using the GPT Researcher framework.

Azure AI Voice Live for .NET

Enables real-time, bidirectional voice AI capabilities in .NET applications using Azure AI and WebSocket communication.

Business Analyst

Transforms complex data into actionable business intelligence through advanced analytics, KPI frameworks, and predictive modeling.

Excel & Spreadsheet Automation

Automates the creation, editing, and analysis of Excel spreadsheets with professional formatting, formula integrity, and financial modeling standards.

RAG Implementation Framework

Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in custom knowledge.

Apache Spark Optimization

Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning strategies.

Data Storytelling for Analytics

Transforms raw data and complex analytics into persuasive business narratives and structured executive presentations.

Hybrid Search Implementation

Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in RAG systems.

Embedding Strategies for RAG

Optimizes embedding model selection and chunking strategies for semantic search and Retrieval-Augmented Generation (RAG) applications.

Risk Metrics Calculation

Calculates comprehensive portfolio risk metrics and performance indicators for quantitative finance and investment management.

Vector Search & Similarity Patterns

Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.

Trading Backtesting Frameworks

Builds robust, bias-aware backtesting systems to validate quantitative trading strategies and produce reliable performance estimates.

LangChain Architecture Design

Architects sophisticated LLM applications using LangChain's modular framework for agents, memory, and complex workflows.

Prompt Engineering Patterns

Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning, chain-of-thought, and modular templates.

ML Pipeline Workflow

Orchestrates end-to-end MLOps pipelines from data preparation and model training through to production deployment and monitoring.

LLM Evaluation Framework

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and systematic benchmarking.

Vector Index Tuning

Optimizes vector index performance for production environments by balancing latency, recall, and memory usage.

Embedding Strategies

Optimizes semantic search and RAG applications through intelligent model selection, chunking strategies, and embedding quality evaluation.

Similarity Search Patterns

Implements efficient similarity search and vector database patterns for semantic search, RAG retrieval, and recommendation engines.

LLM Evaluation

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking to ensure performance and quality.

RAG Implementation

Builds Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases, semantic search, and advanced retrieval strategies.

Spark Optimization

Optimizes Apache Spark workloads through advanced partitioning, memory management, and shuffle tuning strategies.

Prompt Engineering Patterns

Optimizes LLM performance and reliability through advanced techniques like few-shot learning, chain-of-thought reasoning, and structured prompt templates.