Skill UI

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.

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 skill provides comprehensive strategies for implementing robust caching mechanisms for OpenRouter API calls. It covers in-memory caching using Python, persistent caching with Redis for scalability, and utilizing provider-specific features like Anthropic prompt caching. By implementing deterministic key generation and managing Time-To-Live (TTL), developers can significantly reduce API costs and improve application latency when handling repetitive or similar LLM queries, making it ideal for RAG systems and high-volume applications.

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.

An advanced framework designed to transform standard Claude Code into a persistent, self-directing agent system. It integrates crucial components like structured persistent memory (MCP), scheduled operations (Crons), remote dispatching, and computer automation (browser/desktop control). This system is ideal for implementing complex, continuous, or event-driven automated workflows that require statefulness and long-term monitoring.

A robust system for organizing and tracking complex, multi-stage projects. It utilizes persistent Markdown files (task_plan.md, findings.md, progress.md) as permanent working memory, ensuring that project goals, research findings, and operational progress are saved and easily retrieved across multiple sessions. Ideal for managing complex development workflows and deep research tasks.

A tool designed for persistent context management within AI coding sessions. Memory Kit solves the problem of context loss that occurs during compaction or session restarts by saving, loading, updating, and sharing critical session state to a dedicated `MEMORY.md` file. Key functions include creating snapshots of goals and decisions, syncing context across team members via Git, and auditing/pruning stale entries, ensuring continuous workflow and knowledge retention.