Skill UI

A comprehensive guide covering advanced scripting for Rhinoceros 3D. This skill addresses multiple surfaces, including RhinoPython, VBScript (RhinoScript), and direct RhinoCommon (.NET). Use it to write production-quality scripts, automate complex modeling tasks, manipulate geometry (layers, blocks), build custom command macros, and interact with the Rhino environment efficiently.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

Use the composio CLI to orchestrate complex, end-to-end product releases across multiple services: Stripe for billing updates, Supabase for database migrations, and Vercel for frontend deployments. This single pipeline ensures that billing models, database schemas, and user interfaces are consistently updated in the correct sequence, guaranteeing reliable full-stack product launches.

This skill provides comprehensive intelligence across finance and media. Users can search real-time news, analyze media bias across multiple dimensions, get live stock/crypto data with AI forecasts, price financial options using ML models, and synthesize balanced perspectives on current events. It is ideal for deep research, investment analysis, and media literacy.

An expert skill for generating, auditing, and improving Best Practice Analyzer (BPA) rules for Power BI semantic models. It guides users through an interactive, multi-phase Q&A process (Double Diamond) to gather comprehensive requirements, analyze model structures, and deliver tailored, actionable rule sets for governance and compliance.

This tool traces the entire downstream dependency chain from a core semantic model to all connected reports and assets across multiple Fabric workspaces. Use it for crucial impact analysis before modifications, auditing model usage, or mapping cross-workspace data dependencies. It helps data stakeholders understand the full scope of influence a model has on the reporting layer.

A comprehensive guide for searching documentation across multiple BI sources (TE docs, DAX.guide, SQLBI, Power BI/Fabric) using the pbi-search CLI. It also details the structure and use of critical configuration files, such as .tmuo, Preferences.json, and UiPreferences.json, essential for advanced data modeling and deployment.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

This skill guides you through a comprehensive, multi-phase interview to capture every necessary detail of a new project idea. It is designed to run BEFORE any code is written, forcing detailed specification gathering across purpose, features, data models, and technical constraints. The output is a clean, self-contained prompt.md file, ensuring subsequent development is based on solid, unambiguous requirements and preventing costly mistakes.

A sophisticated automated loop that uses vision or video models to evaluate subjective aesthetic quality (e.g., timing, color grade, layout). Instead of relying on numeric metrics, the system renders multiple parameterized variants into one artifact, asks the AI to rate them, and suggests optimal values, repeating the process until convergence. Ideal for design iteration where human judgment is the primary success criteria.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.