memory-search
The memory-search skill queries conversation history and semantic memory using hybrid, semantic, term, or symbol matching to recall previous discussions, decisions, and context across sessions. Use it when users explicitly request to search memory or recall past conversations, or proactively before starting work to gather relevant prior context from the AI Maestro platform.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/memory-search && cp -r /tmp/memory-search/cli-tool/components/skills/ai-maestro/memory-search ~/.claude/skills/memory-searchSKILL.md
# AI Maestro Memory Search Search your conversation history using semantic, keyword, and symbol matching. Recall past decisions, discussions, and context across sessions. Part of the [AI Maestro](https://github.com/23blocks-OS/ai-maestro) suite. ## Prerequisites Requires [AI Maestro](https://github.com/23blocks-OS/ai-maestro) running locally. Memory indexing uses CozoDB for vector search. ```bash # Install memory tools git clone https://github.com/23blocks-OS/ai-maestro-plugins.git cd ai-maestro-plugins && ./install-memory-tools.sh ``` ## Core Behavior Before starting any task, search memory for relevant context: ``` Receive instruction -> Search memory -> Then proceed ``` ## Commands | Command | Description | |---------|-------------| | `memory-search.sh "<query>"` | Hybrid search (recommended) | | `memory-search.sh "<query>" --mode semantic` | Find conceptually related | | `memory-search.sh "<query>" --mode term` | Exact text matching | | `memory-search.sh "<query>" --mode symbol` | Code symbol matching | | `memory-search.sh "<query>" --role user` | Only user messages | | `memory-search.sh "<query>" --role assistant` | Only assistant messages | ## Search Modes | Mode | Best For | |------|----------| | `hybrid` (default) | General search, most cases | | `semantic` | Related concepts, different wording | | `term` | Exact function/class names | | `symbol` | Code identifiers across contexts | ## Usage Examples ```bash # User asks to continue previous work memory-search.sh "authentication" # Find a specific component discussion memory-search.sh "PaymentService" --mode term # Find related design discussions memory-search.sh "error handling patterns" --mode semantic # Find code symbol references memory-search.sh "processPayment" --mode symbol ``` ## Combining with Other Skills For complete context, pair with docs-search and graph-query: ```bash memory-search.sh "feature" # What did we discuss? docs-search.sh "feature" # What do docs say? graph-describe.sh ComponentName # What is the structure? ``` ## Full AI Maestro Experience This skill is part of the [AI Maestro](https://github.com/23blocks-OS/ai-maestro) platform, which provides **6 skills** for AI agent orchestration: messaging, memory, docs, graph, planning, and agent management.
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