This repository is neo4rs, a production-grade asynchronous Rust driver for the Neo4j graph database. It implements the Bolt binary protocol (versions 4.x with partial 5.x support) and provides a complete client library for querying, transaction management, connection pooling, cluster routing, and serialization of Neo4j's rich data types (nodes, relationships, paths, temporal and spatial values). The architecture is modular: a connection layer handles TCP/TLS handshakes and chunked message framing; a PackStream serializer/deserializer converts between Rust types and the Bolt wire format; a routing module manages cluster membership and load balancing; and a high-level API (Graph, Session, Txn, RowStream) offers async query execution with automatic retry on transient errors. The codebase includes two parallel Bolt protocol implementations—a legacy one and a newer feature-gated version—which introduces maintenance overhead. Despite this, the project is actively maintained with extensive integration tests against multiple Neo4j versions and a robust CI pipeline. The library is suitable for any Rust application that needs to interact with Neo4j, offering both a high-level ergonomic API and low-level control over the protocol.
This repository is a curated collection of system prompts, tool definitions, and behavioral configurations extracted from dozens of commercial and open-source AI coding assistants and products. It contains over 100 text files organized by vendor (Anthropic, Cursor, VSCode Agent, Manus, Devin, Replit, etc.), each representing a snapshot of how these AI agents are instructed to behave, what tools they can use, and what safety rules they follow. The collection is not an application or a data pipeline; it is a reference archive for researchers, prompt engineers, and developers studying AI agent design patterns, prompt injection risks, and competitive analysis. The repository includes both full system prompts (e.g., Claude Code 2.0, Cursor Agent, Manus) and structured tool JSON schemas that define function-calling APIs. While the content is valuable for understanding AI agent architecture, the repository itself has no executable code, no versioning of individual prompts, and no automated validation, making it a static snapshot rather than a living project.