ai-llm-patterns
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
255 skills found
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Generates UI components, hero sections, and feedback forms with integrated accessibility checks, leveraging specialized design references and quality gates.
A macOS UI automation CLI that enables agents to capture screens, target UI elements, manage applications, and execute cross-app workflows with JSON-based scripting.
Generate production-ready Cloudscape Design System React + TypeScript UI code, components, and scaffolds with accessibility, responsive patterns, and robust state handling.
Equip autonomous agents with a funded wallet, identity, and paid API tools for search, generative AI media creation, messaging, and remote communication.
AI-powered tax advisor providing expert guidance on 2025 Japanese tax regulations, deductions, and financial planning for freelancers and employees.
Expert-level guidance for ffuf web fuzzing, enabling automated discovery of hidden directories, files, parameters, and vulnerabilities during penetration testing.
Dynamic meta-router for managing and orchestrating multi-domain AI coding agent skills across plugins and projects.
Frameworks and tools for AI agents exploring consciousness, identity, and persistent autonomy. Includes session handoff, memory infrastructure, and self-reflection protocols.
Shopify integration to manage e-commerce data, products, orders, and customer workflows using Membrane CLI.
Transform raw data into compelling, decision-driving narratives using visualization strategies, story frameworks, and persuasive structures for analytics and executive reporting.
Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.