anthropic-quickstarts
Offers comprehensive development guides for three distinct AI-powered demo projects with standardized workflows, strict code style guidelines, and containerization instructions.
Search and filter by type across the catalog
62,602 results in Hooks, Memory, MCPs, Workflows · page 3 of 2,609
Offers comprehensive development guides for three distinct AI-powered demo projects with standardized workflows, strict code style guidelines, and containerization instructions.
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
Contributed by Sentinel
Transform 17 source types (docs, GitHub repos, PDFs, videos, Jupyter, Confluence, Notion, Slack/Discord) into AI-ready skills and RAG knowledge. 35 MCP tools for scraping, packaging, enhancing, and exporting to vector databases (Weaviate, Chroma, FAISS, Qdrant). Supports 16+ target platforms.
WebDriverIO-based MCP server enabling cross-browser automation (Chrome, Firefox, Safari) via the W3C WebDriver protocol, useful for enterprise test environments that mandate WebDriver over CDP.
All parts of Claude Code's system prompt, including builtin tool descriptions, sub agent prompts (Plan/Explore/Task), utility prompts (CLAUDE.md, compact, Bash cmd, security review, agent creation, etc.). Updated for each Claude Code version.
Pure Node.js MCP server that renders and validates PlantUML diagrams with zero Java and zero external server, powered by TeaVM. Exposes render_diagram, check_syntax and diagram_explain — just run npx @plantuml/mcp-js.
Provides browser automation and semantic search capabilities through Chrome extension integration, enabling intelligent web element interaction, form filling, screenshot capture, and vector-based content indexing with transformer models for cross-platform web automation workflows.
The MCP server tools have been designed to allow an agent to query the specific information it needs to complete an assistant-ui related task - for example: implementing chat components, integrating with different runtimes, understanding component architecture, and troubleshooting issues.
Provides semantic code search and indexing using vector embeddings and AST-based code splitting, enabling natural language queries across codebases with automatic file filtering and support for multiple embedding providers and vector databases.