3. Data, Science & AI

Provides assistance with writing, optimizing, and explaining SQL queries, along with database best practices.

Scaffolds cutting-edge AI agents using the Processor Pattern with models like Gemini 3 and Veo 3 for advanced agent workflows.

Provides instant arithmetic, unit conversion, and financial calculations for user queries.

Extracts text and generates summaries from PDF documents for efficient information access and analysis.

Manages biomechanics data pipelines with DataJoint, enabling schema debugging, kinematic reconstruction queries, and relationship mapping across motion capture systems.

Scrapes top posts from specified Reddit subreddits to gather trending community data for analysis or monitoring.

A 5-step systematic framework (Blueprint, Links, Architect, Stylize, Trigger) for developing robust AI agents and automation workflows.

Automates dataset analysis for machine learning tasks, generating Python training scripts and HTML reports for classification, regression, and clustering.

Provides an AI-driven framework for building complex systems (compilers, engines, optimizers) using agentic workflows and enforced golden file testing.

Enables trend modeling with limited data using three-valued logic (INC/DEC/CONST) and scenario generation for qualitative analysis.

Optimizes PostgreSQL queries using EXPLAIN analysis and strategic indexing to enhance database performance and efficiency.

Provides expert guidance for designing PostgreSQL database schemas, selecting optimal data types, and implementing efficient DDL patterns for structured data management.

Provides foundational principles for relational database design, including normalization, keys, and ACID compliance.

Enables real-time coordination of multiple AI agents with low-latency tmux messaging or async file-based inbox for collaborative workflows.

Runs batch inference jobs on Google Cloud Vertex AI for scalable model predictions on large datasets.

Comprehensive toolkit for web scraping, crawling, and structured data extraction using Firecrawl API for data gathering tasks.

Generates Drizzle ORM migration scripts for database schema changes including new tables and relationships.

Configures and manages Clawdbot agent skills, workspace, channels, and multi-agent routing using YAML frontmatter for AI agent systems.

Spawns isolated AI coding assistants with git branch isolation and Redis coordination for parallel development workflows.

Query engineering tool for retrieving project management, investment, and development data from the Span Knowledge Graph API.

Centralized platform for managing machine learning models across their lifecycle, including version control, metadata tracking, and production deployment.

Applies quantization, pruning, and knowledge distillation to compress AI models for efficient edge device deployment.

Comprehensive guide for validating data quality, implementing testing frameworks, detecting anomalies, and ensuring observability in production data pipelines.

Tracks and visualizes data lineage and provenance for comprehensive data flow analysis in ETL and AI/ML pipelines.