3. Data, Science & AI

Provides a guide for managing Supabase database schemas, Row Level Security (RLS) policies, and migration workflows.

Local speech-to-text transcription service using NVIDIA NeMo's Parakeet model via command line interface.

Converts Product Requirements Documents (PRDs) into Ralph agent-compatible JSON format for seamless integration with the autonomous agent system.

Provides guidelines for implementing data persistence and caching mechanisms in Streamlit dashboards to enhance performance and user experience.

Retrieves and summarizes Interactive Brokers portfolio positions and profit/loss data for clear financial overview.

Comprehensive guide enabling AI agents to perform text summarization, sentiment analysis, and full-text translation.

Streamlines AI agent application development with code generation, model comparison, tracing, and evaluation setup for efficient AI workflow creation.

Performs statistical analysis, exploratory data analysis (EDA), and data visualization to uncover patterns and drive data-driven decisions from datasets.

Applies query tuning and indexing strategies to optimize PostgreSQL database performance and efficiency.

Provides assistance with MATLAB and GNU Octave for numerical computations, data analysis, scientific visualization, and converting between MATLAB/Python code.

Configures and switches between LLM providers (OpenAI, Anthropic, Azure, Ollama) for seamless AI model management.

Performs statistical analysis and data visualization to extract actionable insights from datasets, enabling data-driven decision making.

Continuous learning system for AI generation templates, tracking feedback and quality metrics to refine outputs over time.

Enables spectral similarity scoring and compound identification in metabolomics through mass spectrum comparison and library searching.

Configures agent specifications, inheritance, skills, and tools within the .parac/ workspace for setting up or customizing AI agents.

Enables AI agents to store and retrieve learned patterns, performance metrics, and self-assessments using ChromaDB for continuous improvement without altering static configurations.

Configures and switches between LLM providers (OpenAI, Anthropic, Azure, Ollama) for seamless AI model integration and management.

Generates references and inputs for a deterministic 2.5D art pipeline using Gemini CLI.

Comprehensive guide for designing and implementing specialized AI agents following v2 architecture patterns and best practices.

Provides enhanced AI functionality including web search, code execution, MCP integration, and autonomous agent spawning.

Enables semantic search, knowledge base construction, and persistent memory for agents using ChromaDB integration patterns.

Configures agent specifications, inheritance, skills, and tools in the .parac/ workspace for agent setup and customization.

Provides enhanced meta-orchestration for selecting and combining AI reasoning patterns with weighted selection and confidence aggregation for complex problem-solving.

Protocol enabling seamless handover between AI reasoning patterns (ToT, BoT) with state checkpointing and IR-v2 orchestration for complex problem-solving.