3. Data, Science & AI

Audits prompts against Claude best practices, identifying clarity issues and providing fixes and grades.

Analyzes Firefox bookmarks and tags from places.sqlite, providing statistics, tag popularity, and metadata exploration for data extraction and organization.

Extracts structured data from SANS course-related files including materials and documentation.

Searches JLCPCB and LCSC databases for electronic components, providing specs, availability, and alternatives for PCB design and assembly.

Configures data quality tests, profiling, alerts, and incident management in OpenMetadata for end-to-end data observability.

Provides foundational SQL patterns for query writing, schema design, and understanding dialect differences across PostgreSQL, MySQL, and SQLite.

Queries KBase BERDL pangenome database for genomic data including species information, genome statistics, gene clusters, and functional annotations.

Extracts structured data from SANS course materials, including course outlines and content files.

Optimizes AI agent context handling to overcome limitations, reduce costs, and improve performance in long-running conversations and memory-constrained systems.

Enables data discovery, governance, and collaboration via OpenMetadata UI for managing assets, ownership, glossary terms, and data contracts.

Guides efficient data extraction from Datomic using `d/q`, `d/index-pull`, and indexing for optimal scalability and multiuser performance.

Performs structured audits of Anthropic Cookbook notebooks against a predefined rubric to ensure quality and adherence to AI/ML best practices.

Generates videos from text prompts using AI models like Google Veo and OpenAI Sora.

Provides best practices for ZeroDB vector database usage, semantic search patterns, and RLHF workflows in AI memory systems.

Provides a guide for designing and implementing multi-agent systems with collaboration patterns and orchestration strategies aligned with 2025-2026 standards.

Guides design, review, and optimization of AI agent workflows using SSOT, SRP, and patterns like Prompt Chaining and Orchestrator-Workers.

Creates new data scanners for extracting information from specific external sources such as Twitter and Bloomberg, enabling structured data collection workflows.

Modifies GPT API calls, agent prompts, and structured output configurations for precise AI interactions.

Monitors success metrics of development tasks to enhance AI model routing decisions and future task allocation.

Guides end-to-end data analysis design from purpose clarification to visualization using BigQuery and Looker Studio.

Optimizes and designs effective prompts for LLM interactions, improving output quality and reliability in production environments.

Adds new technical indicators (e.g., Bollinger Bands, Stochastic) to financial analysis tools without involving trading strategies or agents.

Generates images for any purpose using Google's Gemini 2.5 Flash, including designs, illustrations, and graphics.

Analyzes database queries and recommends missing indexes with performance impact assessment for optimized data operations.