agent-memory-mcp
Agent Memory MCP provides persistent, searchable knowledge management for AI agents through four core tools: memory_search queries the knowledge bank by content, type, or tags; memory_write records architecture decisions, patterns, and project insights; memory_read retrieves specific stored entries; and memory_stats displays usage analytics. Use this skill when building multi-step agents that need to reference prior decisions, maintain architectural consistency, or accumulate knowledge across project sessions.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/agent-memory-mcp && cp -r /tmp/agent-memory-mcp/cli-tool/components/skills/ai-research/agent-memory-mcp ~/.claude/skills/agent-memory-mcpSKILL.md
# Agent Memory Skill
This skill provides a persistent, searchable memory bank that automatically syncs with project documentation. It runs as an MCP server to allow reading/writing/searching of long-term memories.
## Prerequisites
- Node.js (v18+)
## Setup
1. **Clone the Repository**:
Clone the `agentMemory` project into your agent's workspace or a parallel directory:
```bash
git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
```
2. **Install Dependencies**:
```bash
cd .agent/skills/agent-memory
npm install
npm run compile
```
3. **Start the MCP Server**:
Use the helper script to activate the memory bank for your current project:
```bash
npm run start-server <project_id> <absolute_path_to_target_workspace>
```
_Example for current directory:_
```bash
npm run start-server my-project $(pwd)
```
## Capabilities (MCP Tools)
### `memory_search`
Search for memories by query, type, or tags.
- **Args**: `query` (string), `type?` (string), `tags?` (string[])
- **Usage**: "Find all authentication patterns" -> `memory_search({ query: "authentication", type: "pattern" })`
### `memory_write`
Record new knowledge or decisions.
- **Args**: `key` (string), `type` (string), `content` (string), `tags?` (string[])
- **Usage**: "Save this architecture decision" -> `memory_write({ key: "auth-v1", type: "decision", content: "..." })`
### `memory_read`
Retrieve specific memory content by key.
- **Args**: `key` (string)
- **Usage**: "Get the auth design" -> `memory_read({ key: "auth-v1" })`
### `memory_stats`
View analytics on memory usage.
- **Usage**: "Show memory statistics" -> `memory_stats({})`
## Dashboard
This skill includes a standalone dashboard to visualize memory usage.
```bash
npm run start-dashboard <absolute_path_to_target_workspace>
```
Access at: `http://localhost:3333`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.
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
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.