3. Data, Science & AI

Optimizes SQL and NoSQL database queries by detecting N+1 issues and providing index recommendations for improved performance.

Infers benefits from data snippets in analysis_v2.1.19.json, generating Before/After comparisons and benefit explanations.

Enables multimodal AI tasks including computer vision, image/video generation, speech-to-text, and text-to-speech.

Coordinates multiple Claude AI instances to collaboratively solve complex tasks through parallel processing and agent coordination.

Creates valid test data by analyzing database schemas, ensuring consistency for development and testing environments.

Automates creation of database migration files based on detected schema modifications.

Verifies code against mathematical specifications from theses, ensuring formula correspondence and numerical algorithm validation.

Manages the Aenea consciousness system, including starting/stopping, monitoring thought cycles, and debugging system state.

Integrates PostgreSQL, MySQL, and MongoDB database servers via MCP for seamless data access and management.

Generates consistent charts across report versions using version inheritance and config tracking for standardized data visualization.

Manages Supabase PostgreSQL database schemas, including migrations, RLS policies, and type generation for table structures and relationships.

Integrates LLM APIs including OpenAI, Claude, and Gemini with prompt engineering and function calling capabilities.

Generates time series forecasts for revenue and transactions using Facebook's Prophet library with uncertainty intervals.

Automates exploratory data analysis with data profiling, distribution insights, and pattern discovery to understand datasets before formal analysis.

Enables direct Gemini AI research using the user's Chrome session, bypassing API limits through browser interface interaction.

Orchestrates multiple AI agents with specialized expertise to handle parallel tasks or provide comprehensive analysis from diverse perspectives.

Analyzes SQL query execution plans, recommends indexes, detects N+1 issues, and optimizes joins for improved database performance.

Command-line tool for querying, transforming, and formatting JSON data, enabling efficient extraction and manipulation from files and API responses.

Orchestrates multi-agent workflows for scalable tasks including research swarms, parallel builds, and agent-based pipelines.

Guides creation, review, and updating of agents and multi-agent workflows, covering key patterns and context engineering for .agent.md files.

Designs optimal database schemas, storage strategies, and technology choices (SQL/NoSQL) based on application requirements and data patterns.

Dispatches tasks to Jules AI agents via CLI, generating context-rich prompts from Beads issues and spawning async sessions for automated execution.

Segments customers into behavioral groups using RFM analysis and clustering for targeted marketing and persona development.

Enables Snowflake database querying via CLI (snow_query.py) and schema exploration through MCP.