Skill UI

This skill automates the creation of a Dataverse publisher and solution, and systematically adds necessary Power Pages site components. It is crucial for managing the Application Lifecycle Management (ALM) and structured deployment of Power Pages sites, ensuring components are correctly packaged for export and import.

A comprehensive security auditing tool for AI agent skills. It combines deterministic static scanning (SkillSpector) with deep semantic analysis to assess safety, trustworthiness, and potential risks. Determines if a skill is safe to install by checking for over-permissions, malicious behavior, and hidden dependencies.

A comprehensive knowledge base covering the theoretical fundamentals and advanced algorithms of Deep Reinforcement Learning (Deep RL). It details core concepts like Bellman equations, policy gradients, and the trade-offs between policy optimization and Q-learning. The guide analyzes leading algorithms such as PPO, SAC, TRPO, and DDPG, and provides rigorous best practices for debugging and running scientifically sound RL experiments.

A disciplined methodology for fixing bugs and small behavioral changes with minimal impact. It involves tracing symptoms to the narrowest responsible mechanism, implementing changes only at that layer, and adding highly focused regression proofs to ensure surrounding functionality remains intact. Ideal for critical systems requiring maximum stability.

Configure Three.js scenes with PBR materials like MeshStandardMaterial, add various light types, enable shadows, and use environment maps for realistic reflections. Essential for fixing black or flat models and achieving physically based rendering.

This skill covers setting up a foundational Three.js scene. It includes configuring import maps, initializing the Scene, PerspectiveCamera, and WebGLRenderer trio, establishing a responsive render loop, and adding OrbitControls. Use this when bootstrapping a new Three.js application or debugging rendering issues like blank canvases. It excludes model loading and material lighting setup.

This skill provides a guided, hands-on tutorial to teach users the Ouroboros workflow. It guides the user through a structured process of refining vague ideas into precise, executable specifications (Seeds). By simulating an interview and exposing hidden assumptions, it reduces the inherent ambiguity in initial project planning, making it ideal for product development, AI prompt engineering, and initial concept validation.

A comprehensive guide for implementing, gating, and managing feature flags within the Webiny system. Learn how to integrate simple boolean flags or complex nested groups, and apply checks at the API, admin UI, and configuration levels, ensuring robust feature rollout and kill switches.

A comprehensive guide for developers on creating custom bulk actions for a Headless CMS. Learn how to implement the `EntriesBulkAction` interface using `loadData` (for fetching batches) and `processData` (for performing updates). This allows developers to add administrative batch tasks, such as applying discounts or transforming entries, directly from the CMS admin UI, while managing background task convergence and complex field filtering.

Read and write a Zotero library from the shell with the zotero-cli command: search papers by keyword or semantics, read PDF full text and page ranges, get and set metadata, manage collections, tags, notes and annotations, add items by DOI, and export bibliographies.

This document defines the critical architectural contract for standardizing advisory transport across diverse AI code review runtimes (e.g., Claude Code, OpenCode, Codex). It mandates that all integrations adhere to a shared Go contract, governing how review results, evidence, and validation are processed. This is essential for ensuring system integrity, managing architectural transitions, and preventing the creation of isolated, proprietary review systems.

This skill outlines a highly rigorous, receipt-driven workflow for managing defects and implementing code corrections. It guides public collaboration by enforcing strict controls over change authority, ensuring comprehensive lineage tracking, and mandating evidence-based validation. Key steps include CodeGraph impact mapping, black-box testing, and adherence to strict change budgets, ensuring every deployed change is fully traceable and robust.