Best Best AI Agent Skills for Data Analysis

Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from uploaded documents, and closes.

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

Implements efficient similarity search workflows for various applications, including semantic search systems and recommendation engines. By utilizing this skill, developers can significantly improve search accuracy and performance, ensuring quick retrieval of similar items even from large datasets.

MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown, particularly useful for converting documents into LLM-friendly text format.

Conducts detailed exploratory data analysis on various scientific file formats to identify data quality and key insights. This automation helps researchers by providing comprehensive markdown reports, facilitating faster decision-making in their analyses.

Enables semantic search and content extraction from various web pages based on user intent. Speeds up research and information retrieval by allowing users to automate the search process and get structured responses.

Handles the creation of various types of visualizations in Python, ranging from simple line charts to complex 3D plots. Enhances productivity by enabling the development of high-quality graphics, which effectively communicates data insights through attractive and customizable plots.

Generates high-quality statistical visualizations directly from DataFrames in Python projects. This skill enhances data analysis productivity by simplifying complex graph creation, allowing users to visualize relationships and distributions with minimal code.

Handles the task of generating text embeddings using the Google Gemini embeddings API. By using this skill, developers can efficiently translate text into vector representations, enabling advanced applications like semantic search and document clustering.

Handles document uploads and natural language queries in a managed RAG system, leveraging semantic search for over 100 document formats. Automates document retrieval and citation extraction, significantly enhancing search efficiency and accuracy for users.

Facilitates the implementation of various machine learning models for tasks such as text generation, classification, and fine-tuning. By utilizing this skill, developers can expedite model deployment and enhance their applications without extensive manual setup.

Facilitates various statistical modeling processes, including regression analysis, time series forecasting, and hypothesis testing. Users gain accuracy and efficiency in their analyses by automating complex statistical methods, thus enhancing decision-making capabilities.

Computes SHAP values for any machine learning model to explain predictions. This skill enhances model interpretability and trust by providing insights into feature importance and model behavior, ultimately aiding in better decision-making.

Facilitates advanced machine learning tasks with time series data, such as classification, regression, and anomaly detection. By utilizing this skill, developers can enhance their predictive analytics capabilities and improve decision-making with accurate forecasts and insights.

Handles the creation of interactive visualizations using Python by providing over 40 chart types and APIs. Developers benefit from streamlined plotting capabilities that reduce the complexity of visual data representation and enhance data analysis efficiency.

Handles single-cell genomic data analysis, including RNA-seq and chromatin accessibility. By installing this skill, developers can leverage advanced probabilistic models to automate complex analyses, accelerating research and reducing human error.

Retrieves AI-predicted 3D protein structures for over 200 million proteins. Enhances research efficiency by automating data retrieval and integration into computational workflows.

Handles reading, writing, and manipulating DICOM files used in medical imaging, including pixel data and metadata management. By installing this skill, developers can automate the workflow for processing medical images, reducing errors and improving efficiency in handling complex DICOM datasets.

Facilitates programmatic access to extensive DrugBank data for drug research and analysis. Developers can efficiently retrieve detailed drug and interaction information, enhancing pharmacological understanding and data handling capabilities.

Facilitates comprehensive analysis of single-cell RNA-seq data by providing essential preprocessing and visualization tools. Users benefit from streamlined workflows that enhance data insight and enable accurate biological interpretations without extensive manual work.

Facilitates molecular analysis and manipulation through a comprehensive Python API in cheminformatics projects. By automating tasks such as molecular structure reading and descriptor calculation, it enhances efficiency and reduces potential errors in chemical research.

Executes Python code in a controlled, sandboxed environment with access to numerous pre-installed libraries. This allows developers to run scripts safely and efficiently, reducing the risk of code errors and enhancing productivity.

Handles astronomical data analysis tasks such as unit conversions and coordinate transformations. By installing it, developers can streamline their data processing workflows, ensuring accuracy in calculations and saving time on manual conversions.

Facilitates the retrieval of curated SNP-trait associations from the GWAS Catalog database. Enhances research efficiency by providing immediate access to comprehensive genetic variant data and associated statistics, streamlining investigations into genetic associations and disease traits.

Facilitates access to genetic variant data from ClinVar for research and clinical interpretation. By employing this skill, users can effectively analyze genomic variations, saving time and enhancing the accuracy of genetic assessments.

Handles the deployment and management of Streamlit applications directly within the Snowflake environment. This skill allows developers to create data applications more efficiently by eliminating external hosting and integrating seamlessly with Snowflake's data infrastructure.

Facilitates the querying and analysis of extensive single-cell genomics data from the CZ CELLxGENE repository. It accelerates research by automating data access and processing, enabling users to focus on insights rather than data management.

Facilitates access to extensive FDA regulatory data regarding drugs, medical devices, and related safety information. Enables researchers to effectively monitor and analyze regulatory actions, improving safety assessment and compliance efforts.

Retrieves and analyzes protein-protein interaction networks to understand biological relationships. By using this skill, developers can efficiently explore large datasets and validate structural biology hypotheses, streamlining the process of discovering functional relationships among proteins.

Facilitates comprehensive patent and trademark searches via specialized APIs for IP analysis. Enhances research efficiency by automating data retrieval, enabling users to focus on strategic insights rather than manual querying.

Automates the generation and testing of scientific hypotheses using language models, allowing researchers to systematically explore data-driven insights. By streamlining hypothesis creation, it reduces the time needed for research development and enhances the rigor of scientific investigation.

Facilitates querying and accessing statistical data through the Data Commons Python API. Users can efficiently retrieve various statistical observations, allowing for improved data analysis and informed decision-making.

Facilitates access to AI-ready datasets and benchmarks specifically designed for drug discovery and development. By using this skill, practitioners streamline data handling and model benchmarking across various therapeutic tasks, enhancing their research efficiency.

Facilitates the retrieval and management of nucleotide sequence data from the European Nucleotide Archive (ENA). By using this skill, developers can efficiently integrate genomic data into bioinformatics pipelines, automating data access and reducing the time spent on manual data handling.

Automates the end-to-end research process, facilitating tasks like hypothesis generation and paper writing. By streamlining scientific workflows, it significantly reduces the time and effort required to move from data analysis to publication-ready manuscripts.

Handles querying metabolite data and accessing comprehensive metabolomics study information. By using this skill, developers streamline research processes and eliminate errors, thereby enhancing the efficiency of metabolomics investigations.

Facilitates access to comprehensive structural data of biological macromolecules, enabling users to search and retrieve detailed coordinates and metadata for various structures. Users benefit from streamlined workflows in drug discovery and structural biology research by automating data retrieval and analysis processes.

Handles the creation, manipulation, and storage of annotated data matrices, specifically tailored for genomic analysis tasks involving large datasets. Developers gain efficient data management capabilities, allowing for seamless integration and analysis of single-cell RNA-sequencing data, ultimately enhancing productivity.

Facilitates efficient molecular operations in cheminformatics through a Python interface built over RDKit. Developers gain streamlined workflows for tasks such as molecule standardization, descriptor computation, and file I/O, ultimately enhancing productivity in chemical data analysis.

Facilitates seamless retrieval and mapping of protein data across various databases, enabling efficient search and analysis of protein sequences. By using this skill, users can streamline research processes, reduce manual data entry errors, and enhance productivity in protein-related projects.

Manages the import and processing of mass spectrometry data from various file formats. By automating data handling and analysis, it greatly enhances workflow efficiency and accuracy in research and lab settings.

Handles reading, writing, and parsing of Flow Cytometry Standard (FCS) files. By automating file operations and metadata extraction, it significantly speeds up data preprocessing and reduces the risk of manual errors.

LaminDB handles the task of managing and querying biological datasets efficiently. By automating data lineage tracking and providing standardized metadata, it enhances reproducibility and compliance with FAIR data principles.

Processes high-density neural recordings from Neuropixels probes, transforming raw data into curated, publication-ready units. This skill enhances research efficiency by automating complex analysis tasks and minimizing errors in spike sorting and quality assessment.

Facilitates the creation of a high-performance vector database for efficient semantic searches and recommendation systems. By implementing this skill, developers can achieve low-latency querying and better management of large datasets, significantly enhancing application performance.

Extracts usage cost data for AI models from CodexBar logs, providing insights into which models are most cost-effective. This helps developers optimize usage and manage expenses by automating the extraction and summarization of cost information.