3. Data, Science & AI

Intelligently routes AI model requests using task classification, cost tiers, and high-stakes overrides to optimize performance and cost.

CLI tool for managing AI model lifecycle operations including addition, updates, deprecation, and pricing configuration in a model database.

Builds multi-layer system prompts for AI backends with context management, priority ordering, and budget awareness for efficient LLM interactions.

Provides optimized Postgres query patterns, schema design guidelines, and configuration best practices for Supabase database performance.

Provides AI-driven decision-making by researching best practices, selecting one option, and explaining reasoning without user input.

Provides tools for fetching, displaying, and adding new spell, item, and DBC data types within the World of Warcraft game data portal.

Enables creation and management of autonomous research agents, including prompt engineering, tool execution, and multi-agent coordination for AI-driven research workflows.

Enables development of decision engines for trading systems, managing feature-to-signal pipelines and position logic execution.

Manages end-to-end machine learning model training pipelines, including data preparation, GPU resource allocation, and progress tracking.

Manages technical indicator computation, including multi-output indicators and category management for financial data analysis.

Assists in designing, saving, loading, and configuring neural network models including architecture, metadata, and training parameters for AI applications.

A backtesting engine for financial strategies, handling position management, performance metrics, and model integration via API endpoints.

Provides tools for designing and configuring fuzzy logic systems, including membership functions, fuzzy sets, and fuzzy-to-neural integration.

Manages checkpoint persistence, restoration, and cleanup for machine learning training, backtesting, and agent systems.

Handles financial data acquisition including IB Gateway integration, data loading, caching, and API endpoints for trading applications.

Manages V3 strategy configuration, validation, and migration for AI agent strategies using YAML and model definitions.

Analyzes intention drift, technical quality, and self-improvement opportunities in AI model behavior through calibration checks.

Creates, updates, and validates configurations for AI agents, ensuring proper functionality in LLM-based workflows.

Extracts claims from URLs and formats them for inclusion in sources.yml configuration files.

Provides guidance on Effect AI integration, LLM service usage, and building AI agents with structured output for OpenAI and Anthropic providers.

Creates rigorous evaluation frameworks for LLM agents and multi-agent systems, measuring effectiveness with metrics like precision, recall, and F1.

Analyzes SQL stored procedures and queries to map table-level and field-level data lineage across MySQL, Oracle, and Hive for data governance and ETL documentation.

Provides offline text-to-speech synthesis using MLX framework and Kokoro model for local AI-driven speech generation.

Converts PDF documents to clean Markdown, extracting text and describing image content for AI-ready data processing.