AI Skills for Data Analyst

Enables fast web browsing and interaction with web pages via a headless Chromium daemon for efficient data extraction and testing.

Compares performance metrics of AI models like Claude, GPT, and Gemini to determine the best option for specific tasks.

Manages project learnings by reviewing, searching, pruning, and exporting insights from past sessions for better decision-making.

Enables users to extract data from web pages efficiently, returning structured JSON for further analysis.

Generates thorough, cited research reports from multiple web sources using firecrawl and exa tools for in-depth analysis.

Facilitates neural search for web, code, and company research using Exa MCP, enhancing information retrieval and analysis.

Processes and converts documents using the Nutrient DWS API, supporting various formats and OCR capabilities.

Provides best practices for ClickHouse database patterns, query optimization, and analytics for high-performance data engineering.

Provides a decision framework for choosing between regex and LLM for structured text parsing, optimizing accuracy and cost.

Facilitates a research-first workflow to identify existing tools and libraries before coding, enhancing efficiency and reducing redundancy.

Provides PostgreSQL database patterns for query optimization, schema design, indexing, and security based on Supabase best practices.

Enables tracking of user interactions in Metabase by adding product analytics events using Snowplow for enhanced data insights.

Facilitates the creation, editing, and analysis of spreadsheet files, ensuring professional formatting and error-free formulas.

Automates browser tasks for AI agents, enabling seamless interaction with websites for data extraction, form filling, and testing.

Enhances SQL query performance through optimization techniques, indexing strategies, and EXPLAIN analysis for faster database operations.

Facilitates data quality validation using Great Expectations and dbt tests to ensure reliable data pipelines and metrics.

Optimizes Apache Spark jobs through partitioning, caching, and memory tuning to enhance performance and scalability.

Guides implementation of GDPR-compliant data handling, focusing on consent management and privacy controls for EU personal data.

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

Optimizes Python code performance through profiling and best practices, enhancing application speed and efficiency.