Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration. Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists. Covers Agent class, Runner patterns, function tools, guardrails, and streaming responses. NOT when using raw OpenAI API without SDK or other agent frameworks like LangChain.
Implements interactive widget actions and bidirectional communication patterns for ChatKit. This skill should be used when building AI-driven interactive UIs with buttons, forms, entity tagging (@mentions), composer tools, and server-handled widget actions. Covers the full widget lifecycle from creation to replacement.
Cloud browser infrastructure for AI agents - create sessions, persist contexts, and automate browsers
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Complete workflow guide for implementing new CRUD entities. Use when adding a new resource, implementing full CRUD operations, or needing an overview of the database-to-API flow. Triggers: "new entity", "add resource", "implement CRUD", "create table and API". Orchestrates add-database-table, add-domain-entity, and add-api-endpoint skills.
Build interactive AI chat widgets with buttons, forms, and bidirectional actions. Use when creating agentic UIs with clickable widgets, entity tagging (@mentions), composer tools, or server-handled widget actions. Covers full widget lifecycle. NOT when building simple text-only chat without interactive elements.
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Expert Python developer specializing in modern Python 3.11+ with deep expertise in type safety, async programming, testing, and production-grade code. Invoke for Pythonic patterns, type hints, pytest, async/await, dataclasses.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
GitHub Copilot REST API via curl. Use this skill to manage Copilot subscriptions and retrieve usage metrics.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.
Implements real-time streaming UI patterns for ChatKit applications. This skill should be used when adding response lifecycle management, progress indicators, client effects, and thread state synchronization. Covers onResponseStart/End, onEffect, ProgressUpdateEvent, and thread lifecycle events.
Understand the components, mechanics, and constraints of context in agent systems. Use when designing agent architectures, debugging context-related failures, or optimizing context usage.
