Skill UI

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

This skill aggregates a user's entire year's work—including GitHub PRs, Notion documents, and Linear tickets—into a single, structured Notion document. It automatically groups these artifacts by project, providing a comprehensive and organized summary ideal for professional reviews or annual performance documentation (e.g., SRED preparation).

Streamlines issue and PR triage across GitHub and Linear to keep public-facing activity in sync with internal execution lanes, helping teams map active work, classify outcomes, and decide when Linear ownership is warranted.

Operates an ECC-native notification workflow that captures scattered events from GitHub, Linear, hooks, desktop alerts, and communication surfaces, then classifies, routes, deduplicates, and surfaces operator actions.

Automates Linear issues, projects, cycles, labels, comments, and GraphQL workflows through Rube MCP’s Composio toolkit so teams can manage states, tasks, and custom queries without juggling direct APIs.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

Guides symbolic mathematics work in Python, covering algebraic simplification, calculus, equation solving, linear algebra, physics, number theory, geometry, and code generation to deliver exact symbolic results instead of numeric approximations.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.