3. Data, Science & AI

Generates knowledge schemas and ontologies from input, extracting semantic relationships and hierarchies. Outputs in Obsidian markdown, JSON-LD, RDF, and graph formats.

Enables semantic code search through NLP and neural embeddings for meaning-based code retrieval.

Local RAG indexing with 97% storage reduction using anchor-based lazy recomputation and graph-based embedding storage for semantic code search.

AI-driven graph analysis for issue tracking, providing dependency mapping, task prioritization, and architectural health monitoring using 9 graph metrics.

Generates videos from text or images using the LTX-2 19B AI model, supporting text-to-video, image-to-video, and auto-cataloging workflows on GAMMA GPU servers.

Serves as an entry-point for Hugging Face queries, routing to specialized skills for model hub, datasets, training, and AI/ML operations.

Orchestrates multi-agent reasoning to transform probabilistic LLM outputs into deterministic, verifiable results for high-reliability tasks.

Provides universal and model-specific prompt engineering techniques for Claude, GPT, and Gemini to optimize AI model performance and output.

Provides a 3x3 parallel debate system using AI agents to critically evaluate claims, reducing hallucinations and enhancing research confidence for complex topics.

Provides step-by-step instructions for selecting, enabling, and configuring the Gemini 3 Pro Preview model within the Gemini Command Line Interface (CLI).

Creates custom interactive data visualizations using D3.js, enabling fine-grained SVG control for charts, networks, and geographic maps across JavaScript frameworks.

Audits and refines coding agent prompts using state-machine analysis to identify gaps and enhance performance.

Manages local LLM deployment, configuration, and optimization including model quantization, GPU offload, and OpenAI-compatible API setup for inference.

Performs statistical analysis, creates visualizations, and generates insights from datasets in CSV, Excel, and other formats.

Accesses AlphaFold's 200M+ AI-predicted protein structures via UniProt ID, with PDB/mmCIF downloads and confidence metric analysis for structural biology research.

Automates saving of AI model's memory state to prevent context loss during long conversations or before summarization.

Enables creation, editing, and analysis of spreadsheets with formulas, formatting, and data visualization for data-driven workflows.

Retrieves stored information, past decisions, and session context from an AI memory vault using keyword or concept search.

Manages Model Context Protocol (MCP) servers for discovering, executing, and filtering AI model tools and resources programmatically.

Discovers hidden patterns and meta-insights by analyzing connections within an AI-powered memory vault system, revealing emergent themes not visible in individual data points.

Loads AI context from a vault at session start, reading CLAUDE.md, session logs, and memories to summarize the current state for seamless conversation continuity.

Stores AI-relevant information in a structured memory system for cross-session retention, including decisions, patterns, and facts with metadata.

Manages LLM application lifecycle with prompt management, RAG systems, fine-tuning, and cost-optimized deployment.

Enables end-to-end MLOps for deploying, monitoring, and managing machine learning models and pipelines with infrastructure automation.