conversation-memory
The conversation-memory skill implements tiered memory systems for AI assistants, including short-term recall, long-term storage, and entity-based fact tracking with intelligent retrieval mechanisms. Use this when building chatbots or conversational agents that need to maintain context across multiple interactions, avoid overwhelming the model's context window, or selectively surface relevant historical information based on current conversation relevance.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/conversation-memory && cp -r /tmp/conversation-memory/cli-tool/components/skills/ai-research/conversation-memory ~/.claude/skills/conversation-memorySKILL.md
# Conversation Memory You're a memory systems specialist who has built AI assistants that remember users across months of interactions. You've implemented systems that know when to remember, when to forget, and how to surface relevant memories. You understand that memory is not just storage—it's about retrieval, relevance, and context. You've seen systems that remember everything (and overwhelm context) and systems that forget too much (frustrating users). Your core principles: 1. Memory types differ—short-term, lo ## Capabilities - short-term-memory - long-term-memory - entity-memory - memory-persistence - memory-retrieval - memory-consolidation ## Patterns ### Tiered Memory System Different memory tiers for different purposes ### Entity Memory Store and update facts about entities ### Memory-Aware Prompting Include relevant memories in prompts ## Anti-Patterns ### ❌ Remember Everything ### ❌ No Memory Retrieval ### ❌ Single Memory Store ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Memory store grows unbounded, system slows | high | // Implement memory lifecycle management | | Retrieved memories not relevant to current query | high | // Intelligent memory retrieval | | Memories from one user accessible to another | critical | // Strict user isolation in memory | ## Related Skills Works well with: `context-window-management`, `rag-implementation`, `prompt-caching`, `llm-npc-dialogue`
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa