hindsight-docs

The hindsight-docs skill provides complete technical documentation for Hindsight, a biomimetic memory system designed for AI agents. It acts as a centralized knowledge resource for understanding the system’s architecture, concepts, configuration methods, APIs, deployment approaches, and operational guidance. The skill helps users solve the problem of locating and understanding fragmented technical information by organizing all available documentation into a structured reference system. It is intended to reduce integration friction and make it easier to understand how memory functionality operates within AI systems, including concepts such as memory retention, retrieval, and reasoning workflows.

The skill covers the main components and behaviors described in Hindsight documentation. It provides guidance for retain, recall, and reflect operations, explains memory banks and disposition configuration, and documents retrieval approaches including semantic, BM25, graph-based, and temporal strategies. The documentation also includes information about API endpoint schemas through an OpenAPI specification, deployment methods for environments such as Docker, Kubernetes, and pip-based setups, and integration guidance for SDKs including Python, Node.js, and Rust. Additional resources include cookbook examples, application demonstrations, configuration references, FAQs, changelogs, and best-practice recommendations related to missions, tags, content formatting, observation scope, and anti-patterns.

This skill is intended for developers, AI engineers, integrators, and technical teams building systems that rely on memory-aware agent behavior. It can be used during implementation, troubleshooting, performance optimization, or system design activities. Common scenarios include configuring memory banks, reviewing API behavior, integrating SDKs into applications, understanding retrieval strategies, and applying documented patterns for long-term conversational or agent-based memory workflows.