Skill UI

Interactive workflow that interrogates scope, deliverables, constraints, and success criteria while guiding users to refine task prompts; copies the final markdown with Joyride clipboard commands and never writes code.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Generates and edits draw.io (.drawio/.drawio.svg/.drawio.png) files via mxGraph XML, supporting templates, layout guidance, and the VS Code Draw.io extension so diagrams for flowcharts, architecture, UML, ER, sequences, and networks open without manual fixes.

FAF Expert is an advanced tool for creating standardized, persistent context for complex software projects. It uses the IANA-registered FAF format to transform codebases into AI-intelligent assets. Use it for multi-AI workflow management (Claude, Gemini, etc.), enterprise-grade deployment, and standardizing project context across development lifecycles.

This skill provides advanced debugging capabilities for failing Power Automate cloud flows using the FlowStudio MCP server. Unlike the limited status codes from the standard Graph API, this tool gives agents deep, action-level inputs and outputs, allowing users to find the true root cause of failures. It is essential for diagnosing complex issues like expression failures, connector authentication errors, or internal run errors.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.

Tracks every code change via structured JSON records, enforces a lifecycle state machine, and maintains session handoff context with scripts for init/create/update/status/resume/close/export workflows.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

Update existing specification files for solutions based on new requirements or code changes. Ensures documentation is optimized for Generative AI consumption, machine-readable, and self-contained. Follows structured formatting standards including requirements, constraints, interfaces, and acceptance criteria to facilitate AI-driven development workflows.

This skill performs visual inspection and validation of website design quality, identifying and fixing issues at the source code level. It supports static sites, SPA frameworks, and full-stack applications. The workflow includes information gathering, visual inspection for layout, responsive, and accessibility issues, followed by source code fixes and re-verification. It ensures responsive design, visual consistency, and accessibility compliance across various viewports.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

Builds molecular property prediction and MoleculeNet workflows with DeepChem: SMILES and graph featurization, scaffold or grouped holdouts, masked labels, graph models, and pretrained encoder transfer for ADMET, toxicity and solubility ML.