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agent-memory-mcp

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

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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:

    git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
    
  2. Install Dependencies:

    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:

    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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

1. Overview

This skill provides domain-specific logic and rules for its respective BDB pipeline component to ensure standardization across multi-agent workflows.

3. Core Process

  1. Read the provided context and ensure preconditions are met.
  2. Run the required script or tool and confirm the state change.
  3. Verify exit codes, file modifications, or DB counts to guarantee success before reporting completion.

4. Common Rationalizations

RationalizationReality
"The code change was small, so I skipped updating OpenWiki docs."Every state change must be reflected in the relevant system records.
"The ingest script exited without an error, so the memB index must be updated."Silent failures happen; explicit verification of the side effect is mandatory.
"I'll let the /startcycle proceed without a defined rollback path."Proceeding without a rollback path corrupts the workflow integrity and safety.
"I trust the cached agent registry instead of rescanning after a skill change."Caches stale out quickly; explicit rescans prevent ghost failures.

5. Red Flags

  • Bypassing the verification step after a script execution.
  • Proceeding to the next pipeline stage without confirming the previous stage's side effects.
  • Ignoring domain-specific constraints listed in this skill.

6. Verification

  • Verified script exit codes are explicitly checked.
  • Confirmed target files or database records reflect the expected change.
  • Ensured no silent failures were ignored before reporting success.

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