Skill UI

Conducts comprehensive AI prompt safety reviews that analyze bias, misinformation, injection, and privacy risks while delivering effectiveness evaluations, security guidance, and best-practice recommendations for prompt engineers.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

A comprehensive meta-skill template designed for developers to scaffold, structure, and define specialized Agent Skills for AI platforms like GitHub Copilot. It guides users through mandatory requirements, including folder naming conventions, YAML frontmatter best practices (name, description), and structuring the skill body with detailed workflows and documentation sections. Use this when building complex, specialized AI capabilities.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

Roundup gathers GitHub, calendar, email, and chat data from your configured sources and synthesizes it into status briefings that match your tone, audience, and requested timeframe.

This technique is used when the required answer in a case query is not directly stated but must be deduced by following a sequence of connected roles, people, or events. It involves mapping complex relationships, such as Victim -> Defendant -> Lawyer -> Witness, to trace indirect connections and reach the final piece of information. Ideal for advanced investigative research and deep knowledge graph traversal.

This technique helps locate video games when the primary information provided is not the game title, but rather indirect clues about the game's developing company. Clues can include the company's name meaning, founder background, corporate history, or foundation activities. You must first decode these clues to identify the company, and then search the company's associated game catalog.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

Stable Baselines3 is a robust, PyTorch-based library providing reliable implementations of state-of-the-art reinforcement learning algorithms (PPO, SAC, DQN, etc.). It is designed for standard RL experiments, quick prototyping, and developing complex single-agent systems. The library supports training agents, creating custom Gymnasium environments, and running vectorized simulations for accelerated learning.

PyTorch Geometric (PyG) is the definitive standard library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for handling diverse graph types, including heterogeneous graphs. The library offers over 60 GNN layer implementations (such as GCN, GAT, GraphSAGE), full support for dataset loading (Planetoid, TUDataset, OGB), and necessary data transformations. It is essential for deep learning tasks involving node classification, graph classification, and link prediction.

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

Automates comprehensive regression testing for all enabled cloud Large Language Model (LLM) providers. This includes verifying core connectivity, standard chat completion, structured JSON output, and Server-Sent Events (SSE) streaming capabilities. Essential for ensuring the reliability and stability of multi-cloud AI integrations in a CI/CD pipeline.