MCP.Market

agent-memory-mcp skill

by davila7·davila7/claude-code-templates·33k stars·MIT

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

A100/100content scan

Is the agent-memory-mcp skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-11-09.

No findings.

Install the agent-memory-mcp skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/davila7/claude-code-templates.git /tmp/claude-code-templates
mkdir -p ~/.claude/skills
cp -r /tmp/claude-code-templates/cli-tool/components/skills/ai-research/agent-memory-mcp ~/.claude/skills/agent-memory-mcp
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

Clone the agentMemory project into your agent's workspace or a parallel directory:

  1. Clone the Repository:
git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
  1. Install Dependencies:
cd .agent/skills/agent-memory
   npm install
   npm run compile

Use the helper script to activate the memory bank for your current project:

  1. Start the MCP Server:
npm run start-server <project_id> <absolute_path_to_target_workspace>

Example for current directory:

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.

npm run start-dashboard <absolute_path_to_target_workspace>

Access at: http://localhost:3333

More skills from davila7/claude-code-templates

  • Aab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
  • Aagent-evaluationTesting 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.
  • Aagent-managementCreate, 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.
  • Aagent-manager-skillManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
  • Aagent-memory-systemsMemory 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
  • Aagent-messagingSend 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.
  • Aagent-tool-builderTools 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
  • Aagents-autogptAutonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
  • Aagents-crewaiMulti-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
  • Aagents-langchainFramework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
  • Aagents-llamaindexData framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
  • Aagile-product-ownerAgile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking. Use for story writing, sprint planning, stakeholder communication, and agile ceremonies.

All agent skills → · MCP servers