Mmcp.market

agent-memory skill

by sickn33·sickn33/agentic-awesome-skills·47k stars·MIT

A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

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Install the agent-memory 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/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p ~/.claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/agent-memory ~/.claude/skills/agent-memory
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

agentMemory Skill

When to Use

Use this skill when you need a hybrid memory system that provides persistent, searchable knowledge management for AI agents.

This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.

Prerequisites

  • Node.js installed
  • Check if agentMemory is already installed in the project:
ls -la .agentMemory

Setup

  1. Install Dependencies:
npm install
  1. Build the Project:
npm run compile

You need to run the MCP server to interact with the memory bank.

  1. Start the Memory Server:
npm run start-server <project_id> <absolute_path_to_workspace>

Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.

Capabilities (MCP Tools)

Once the server is running, you can use these 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({})

Workflow

  1. Initialization: The first time you run this in a project, it may attempt to import existing markdown memory banks from .kilocode/, .clinerules/, or .roo/.
  2. Development Loop:
  • Before Task: Search memory for relevant context.
  • During Task: Use read/search to answer questions.
  • After Task: Write new findings to memory.
  1. Sync: Your writes are automatically synced to standard markdown files in the project.

Example

User request:

Use @agent-memory for this task: A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

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