Skill UI

Codified expertise for sequencing work orders, balancing discrete and batch lines, reducing changeover time, and shielding bottlenecks using FCS, DBR, and SMED so manufacturing meets commitments despite disruptions.

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Extract HEADER, TEXT, ANALYSIS, and channel metadata, retrieve event data as NumPy arrays, handle multi-dataset legacy files, and export FCS 3.1 list-mode data. For compensation, transforms, or gating, use FlowKit instead.

Converts complex, long-form markdown (like RFCs, specs, or reports) into a single, polished, and lightly interactive HTML file. It features a sticky Table of Contents, search functionality, advanced code-copy buttons, and integrates with design systems for professional branding, all without needing a build step or framework runtime.

This meta-skill scans a target codebase path, intelligently determines the most appropriate architectural diagram type (e.g., sequence, component, class), and concurrently generates the diagram in two standardized formats: PlantUML source code and draw.io XML. Finally, it composes these deliverables into a cohesive architecture document (DOCX). Ideal for creating comprehensive onboarding guides or technical RFCs that require both text-friendly and highly editable views of the system architecture.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

This skill guides the creation of technical documentation (such as RFCs, READMEs, PR descriptions, and architecture guides) that minimizes the mental effort required for the reader. It emphasizes structural patterns like 'Lead with the answer,' 'Chunking,' and 'Progressive disclosure' to ensure content is scannable, actionable, and easy to review, significantly reducing cognitive load for maintainers and contributors.