3. Data, Science & AI

Optimizes context handling in AI agent systems by analyzing components, mechanics, and constraints for effective prompt engineering and agent performance.

Executes Google Gemini CLI for AI-driven code analysis, multimodal reasoning, and repository-scale reviews.

Manages game design planning tables with CSV export and tool update capabilities for efficient data handling.

Universal reasoning framework using Lambda-ot calculus for complex decisions, research synthesis, and adaptive system design with cognitive architecture.

Reads, creates, and manipulates Excel spreadsheets for data extraction, formula application, and chart generation in data analysis workflows.

Provides advanced graph analysis and compression for knowledge structures, including structural equivalence-based reduction and hierarchical modeling.

Optimizes diverse inputs into Suno V5 prompts with constraints, metatags, and emotion tags for effective music generation.

Provides CLI tools for multimodal video and audio analysis with graph-based context retrieval to enhance data processing and insight extraction.

Provides a comprehensive guide to Chain-of-Thought prompting techniques for complex reasoning tasks, including step-by-step logic and symbolic manipulation.

Enables hybrid knowledge graph construction by integrating structured data with extracted entities from documents, supporting GraphRAG and metadata enrichment via Neo4j.

Evaluates and optimizes Claude Code agent prompts, skills, and commands through systematic testing of context engineering and improvement metrics.

Declarative framework for building and optimizing AI systems, including RAG pipelines and language model agents, using DSPy.

Orchestrates knowledge graph analysis workflows for text network analysis, content gap detection, and research question generation using InfraNodus tools.

Automatically validates and enhances AI system prompts for optimal performance and compliance with best practices.

Optimizes and designs production-ready prompts for LLMs and agent systems to enhance output quality and reliability.

Framework for metacognitive reasoning using recursive decomposition and modular reconstruction to solve complex problems through first principles.

Defines structure, rubrics, and validation patterns for creating and updating AI agent evaluation suites.

Generates medical education responses (SAQ/VIVA) by composing knowledge primitives via operators, integrating research and PDF processing tools.

Designs and constructs knowledge graphs for semantic search, entity relationship modeling, and knowledge base creation with schema and database selection.

Topological reasoning framework using simplicial complexes and persistent homology for scale-invariant knowledge representation with topological invariants.

Enables developers to interact with Google's Gemini AI models directly via terminal for AI integration and experimentation.

Provides a graph architecture for AI systems requiring multi-scale reasoning, structured uncertainty handling, and self-refactoring with Pareto-optimized complexity.

Automates batch processing of Obsidian markdown vaults, including wikilink extraction, tag normalization, and frontmatter management for vault-wide transformations and migration.

Routes data processing, knowledge graph, and analytics tasks by triggering on ETL, database, and CSV/JSON data formats.