Skill UI

A sophisticated hybrid memory system designed specifically for AI agents. It provides a persistent, searchable knowledge bank to store, retrieve, and manage long-term knowledge, including architectural designs, operational patterns, and critical decision points. By syncing with project documentation, it ensures that crucial institutional knowledge remains accessible throughout the development lifecycle, enhancing agent reliability and consistency.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Persistent LLM conversation memory that manages short-term, long-term, and entity-focused layers to store, retrieve, and consolidate memorable context for smarter conversational AI agents.

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

Loads, matches, and persistently stores project memories so Claude Code sessions remember past decisions, preferences, and conventions for context-aware responses across workflows.

A robust system for managing complex, multi-stage projects and research tasks by treating the file system as persistent 'working memory.' It enforces structure using dedicated Markdown files (`task_plan.md`, `findings.md`, `progress.md`) to log progress, capture key findings, and track decisions. Ideal for long-running workflows that require robust context preservation and structured accountability.

Proactively hunt for Advanced Persistent Threats (APTs) by building hypothesis-driven searches across telemetry, network logs, and memory artifacts, using MITRE ATT&CK, Velociraptor, osquery, Zeek, and Sigma-compatible playbooks to validate or rule out known TTPs.

Transforms Claude Code into a continuous autonomous agent with persistent memory, scheduled operations, remote dispatch, computer automation, and task queuing for repeatable workflows and monitoring.

Provides OpenClaw agents with local persistent memory via MemOS, managing installation, configuration, upgrades, and a memory viewer for secure offline workflows.

A persistent system designed to guide complex, multi-step projects, research, and long-running tasks. Instead of relying on limited volatile context, this skill uses structured Markdown files (task_plan.md, findings.md, progress.md) as a 'disk working memory.' It enforces a rigorous workflow, ensuring key decisions, findings, and progress updates are always logged persistently, thereby preventing information loss and maintaining continuity across sessions.

Guides complex, multi-step work by treating Markdown files as persistent disk memory: create and update task_plan.md, findings.md, and progress.md to capture plans, discoveries, and session logs before and during decisions.

Acontext serves as a robust memory layer for production-grade AI agents. It provides persistent sessions, disk-based storage, and sophisticated learning spaces that distill complex conversations into reusable, structured skills. Users can manage the entire system—from project setup and login to skill uploading—via a powerful Command Line Interface (CLI) and API, significantly enhancing agents' contextual understanding and long-term knowledge retention.