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.

A comprehensive framework for managing the entire software development lifecycle (SDLC), from initial vision to final implementation. This skill structures projects into adaptive phases: Specify, Design, Tasks, and Execute. It automatically adjusts the required depth based on complexity and scope, supporting everything from quick bug fixes to large, multi-component features. It ensures rigorous requirements traceability, atomic task breakdown, and persistent memory across sessions, making it ideal for initializing new projects or mapping complex existing codebases.

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.