Skill UI

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.

The Ouroboros workflow engine utility used to execute complex Seed specifications. It manages the entire lifecycle, including input validation (YAML), orchestration, real-time progress tracking, and formal convergence evaluation. This tool is crucial for running structured, multi-step computational workflows and assessing outcomes against defined criteria.

A comprehensive guide to product analytics, covering event tracking, funnels, cohort retention, north star metrics, OKRs, and dashboards. Examples include PostHog implementation and Python code for retention analysis. Ideal for product managers and data analysts seeking data-driven decisions.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation. This skill covers exploratory data analysis, hypothesis development, experiment design, feature architecture, analytics instrumentation, and implementation with integrated tracking and monitoring.

Analyze health data trends and patterns over time, including weight, symptoms, medications, and lab results. Identify correlations and significant changes to provide data-driven insights. Supports multi-dimensional analysis and generates interactive HTML visualization reports using ECharts. Ideal for tracking health improvements and monitoring risks.

A comprehensive automation toolkit for Mixpanel product analytics, accessible via Rube MCP. Automate key data tasks including event aggregation, detailed segmentation, conversion funnel analysis, user profile management, cohort tracking, and running custom JQL queries. Ideal for data analysts and product managers needing to deeply analyze user behavior patterns.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

Orchestrate Segment customer-data actions through Rube MCP and Composio, covering event tracking, user identification, grouping, page views, batch dispatches, alias merges, and source management while always refreshing schema info first.