Implements an immutable, layered context stack for assembling system prompts, tools, documents, and mounts per request. Uses discriminated-union layers with priority-based sorting and conflict detection. This pattern cleanly separates concerns from multiple sources (system prompts, tools, skills, documents) and can be extracted to any LLM application that needs dynamic, composable prompt construction without tight coupling to the hosting framework.

Defines a composable interceptor chain for chat request/response processing. Interceptors implement `ChatRequestLoopInterceptor` or `ChatResponseLoopInterceptor` interfaces and are executed sequentially by phase (initialization, preprocessing, LLM, postprocessing). The chain supports idempotent re-injection and is testable in isolation. This pattern is reusable in any request-processing pipeline that needs modular, cross-cutting concerns without framework dependency.

Provides an abstract `SandboxProvider` and `SandboxSession` for safe code execution. It decouples code execution from the environment via mount management and environment hydration, supporting local and remote backends. The abstraction includes session lifecycle, resource cleanup, and path translation. This is a well-crafted isolation pattern that can be reused in any system requiring sandboxed execution of untrusted code (bash, Python) with configurable mounts and environment variables.

Implements an incremental citation extractor that parses streaming text for source citations, handling edge cases like nested brackets, backtick-wrapped markers, and chunk boundaries. Uses a stateful parser that emits citations as soon as they are complete (for streaming events) and recovers from malformed input. This is a non-trivial NLP pattern that can be extracted to any system requiring real-time citation or reference extraction from streaming LLM output.