3. Data, Science & AI

Analyzes disease patterns, outbreaks, and public health interventions using epidemiological modeling and surveillance data for evidence-based decision-making.

Enables creation, editing, and analysis of spreadsheets (including .xlsx, .csv, .tsv) with formula support, formatting, data analysis, and visualization capabilities.

Enables critical decision-making through multi-agent consensus voting, weighted by domain expertise, for structured validation in complex scenarios.

Creates interactive Venn diagram visualizations using venn.js, producing standalone HTML files with labels and tooltips for educational comparison charts.

Creates interactive geographic visualizations with Leaflet, producing embeddable maps and documentation for educational and data display use cases.

Generates interactive timeline visualizations using vis-timeline library, creating complete HTML/CSS/JSON packages for historical, project, or event sequences with category filtering.

Creates interactive Chart.js visualizations for data representation in educational content, reports, and dashboards with complete HTML/CSS packages and documentation.

Generates structured reports for geometry course diagrams and MicroSims from markdown files, including status, difficulty, Bloom's Taxonomy, and UI complexity.

Generates quantitative metrics reports for MkDocs Material-based intelligent textbooks, analyzing content structure and producing markdown summaries.

Generates interactive Chart.js bubble charts for priority matrices and multi-dimensional data analysis, including complete MicroSim package with HTML, CSS, and documentation.

Creates interactive Plotly.js-based mathematical function plots with hover tooltips, sliders, and responsive design for educational textbooks in STEM courses.

Generates STEM educational diagrams and animations using Matplotlib and Manim for physics, math, and technical concepts.

Guides users through Power Query to transform messy data into clean, analysis-ready formats with step-by-step coaching.

Optimizes AI system prompts and custom instructions for platforms including Claude, OpenAI, and Gemini to enhance model output quality and effectiveness.

Enables access to external AI models (e.g., DALL-E, GPT) for capabilities beyond Claude's native abilities, such as image generation and real-time data.

Automates extraction and structuring of unstructured data from diverse sources into AI-interpretable formats for seamless model integration.

Enables growth metric analysis, KPI dashboard creation, and cohort analysis for data-driven business decisions.

Optimizes context loading for large MCP tool sets using progressive disclosure and hierarchical indexing to prevent context window bloat in AI systems.

Enables creation and management of AI agent skills with best practices, including multi-agent system design and context optimization.

Tracks skill usage quality through metrics, user satisfaction signals, and feedback loops for continuous improvement.

Generates accessible, data-driven visualizations for React/Next.js applications using modern libraries and design standards.

Builds production computer vision pipelines for object detection, tracking, and video analysis using YOLO, TensorFlow, and PyTorch.

Verifies epistemic quality of documents pre-ingestion for RAG systems, producing Clarity-Gated Documents and validating Source of Truth files.

Verifies epistemic quality of documents pre-ingestion for RAG systems, producing Clarity-Gated Documents and validating Source of Truth files.