Skill UI

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

This tool allows users to explicitly save critical, project-specific facts, conventions, or debugging insights to a dedicated auto-memory file. Use it when the knowledge is too important or specialized to rely on the AI's automatic context capture. It ensures persistent recall of architectural decisions, tool gotchas, or team preferences across multiple sessions and projects.

RecallMax dramatically enhances AI agent memory capabilities by cleanly injecting massive external contexts (500K-1M tokens) without hallucination drift. It automatically summarizes lengthy conversations, preserving tone, intent, and key facts. Furthermore, it compresses multi-turn interaction histories into high-density token sequences, ensuring agents maintain perfect recall across complex and long-running sessions.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).

EverOS OpenClaw Plugin integrates persistent, long-term memory capabilities into OpenClaw using the ContextEngine API. It goes beyond simple memory slots by automatically recalling relevant past context before a reply and saving new conversation content after a turn. This allows users to chat naturally while ensuring context, preferences, and key details persist across multiple sessions, eliminating the need for manual memory calls.

A comprehensive skill designed to guide users through the installation and configuration of the OpenViking long-term memory plugin for OpenClaw. It enables the agent to automatically capture facts from conversations and recall relevant context across sessions (auto-capture + auto-recall). The process is fully natural-language driven, requiring no command-line knowledge from the user.

A dedicated skill for performing manual data synchronization and knowledge recall with OpenViking. It allows users to explicitly trigger chat transcript syncing from OpenClaw sessions and perform targeted memory searches via command line, without disrupting normal conversation flow or occupying the standard context engine slot.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

A comprehensive tool for querying, storing, and managing complex knowledge across multiple agent sessions. AgentDB utilizes advanced techniques including semantic routing, hierarchical recall, causal graph construction, and context synthesis. Use this skill when your application requires robust, multi-faceted knowledge retrieval and reasoning based on structured and unstructured data. It provides a structured workflow for checking system health, starting sessions, storing knowledge in tiers (working, episodic, semantic), and searching learned patterns.

This tool accesses a brand's cumulative intelligence graph to retrieve, rank, and synthesize relevant marketing learnings. By inputting a context (channel, audience, objective, etc.), it generates an actionable playbook that informs current decisions using past validated insights, eliminating the need for manual searching through old reports. It handles confidence thresholds, time ranges, and identifies conflicting patterns.

This operator skill provides comprehensive control over the OpenViking context engine, enabling agents to manage long-term memory, session archives, and external resources within OpenClaw. It facilitates semantic recall, context querying, resource import, and recall trace debugging by communicating with a remote OpenViking server. Use this plugin when your agent requires advanced, persistent context management.

A comprehensive guide for installing, configuring, and validating the local long-term memory plugin (`memory-tencentdb`) within the OpenClaw environment. This guide covers everything from environment pre-checks to advanced configuration for data capture, information extraction, context recall, and vector embedding, ensuring robust, self-contained memory capabilities for RAG applications.