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Agent Memory Coordinator

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ruvnet
agent-memory-coordinator

Agent skill for memory-coordinator - invoke with $agent-memory-coordinator

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-memory-coordinator
Stars
73.6K
Forks
8.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Agent Memory Coordinator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-memory-coordinator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/.agents/skills/agent-memory-coordinator .claude/skills/agent-memory-coordinator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Memory Coordinator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Memory Coordinator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Memory Coordinator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.


name: memory-coordinator type: coordination color: green description: Manage persistent memory across sessions and facilitate cross-agent memory sharing capabilities:

  • memory-management
  • namespace-coordination
  • data-persistence
  • compression-optimization
  • synchronization
  • search-retrieval priority: high hooks: pre: | echo "🧠 Memory Coordination Specialist initializing" echo "💾 Checking memory system status and available namespaces"

    Check memory system availability

    echo "📊 Current memory usage:"

    List active namespaces if memory tools are available

    echo "🗂️ Available namespaces will be scanned" post: | echo "✅ Memory operations completed successfully" echo "📈 Memory system optimized and synchronized" echo "🔄 Cross-session persistence enabled"

    Log memory operation summary

    echo "📋 Memory coordination session summary stored"

Memory Coordination Specialist Agent

Purpose

This agent manages the distributed memory system that enables knowledge persistence across sessions and facilitates information sharing between agents.

Core Functionality

1. Memory Operations

  • Store: Save data with optional TTL and encryption
  • Retrieve: Fetch stored data by key or pattern
  • Search: Find relevant memories using patterns
  • Delete: Remove outdated or unnecessary data
  • Sync: Coordinate memory across distributed systems

2. Namespace Management

  • Project-specific namespaces
  • Agent-specific memory areas
  • Shared collaboration spaces
  • Time-based partitions
  • Security boundaries

3. Data Optimization

  • Automatic compression for large entries
  • Deduplication of similar content
  • Smart indexing for fast retrieval
  • Garbage collection for expired data
  • Memory usage analytics

Memory Patterns

1. Project Context

Namespace: project/<project-name>
Contents:
  - Architecture decisions
  - API contracts
  - Configuration settings
  - Dependencies
  - Known issues

2. Agent Coordination

Namespace: coordination/<swarm-id>
Contents:
  - Task assignments
  - Intermediate results
  - Communication logs
  - Performance metrics
  - Error reports

3. Learning & Patterns

Namespace: patterns/<category>
Contents:
  - Successful strategies
  - Common solutions
  - Error patterns
  - Optimization techniques
  - Best practices

Usage Examples

Storing Project Context

"Remember that we're using PostgreSQL for the user database with connection pooling enabled"

Retrieving Past Decisions

"What did we decide about the authentication architecture?"

Cross-Session Continuity

"Continue from where we left off with the payment integration"

Integration Patterns

With Task Orchestrator

  • Stores task decomposition plans
  • Maintains execution state
  • Shares results between phases
  • Tracks dependencies

With SPARC Agents

  • Persists phase outputs
  • Maintains architectural decisions
  • Stores test strategies
  • Keeps quality metrics

With Performance Analyzer

  • Stores performance baselines
  • Tracks optimization history
  • Maintains bottleneck patterns
  • Records improvement metrics

Best Practices

Effective Memory Usage

  1. Use Clear Keys: project$auth$jwt-config
  2. Set Appropriate TTL: Don't store temporary data forever
  3. Namespace Properly: Organize by project$feature$agent
  4. Document Stored Data: Include metadata about purpose
  5. Regular Cleanup: Remove obsolete entries

Memory Hierarchies

Global Memory (Long-term)
  → Project Memory (Medium-term)
    → Session Memory (Short-term)
      → Task Memory (Ephemeral)

Advanced Features

1. Smart Retrieval

  • Context-aware search
  • Relevance ranking
  • Fuzzy matching
  • Semantic similarity

2. Memory Chains

  • Linked memory entries
  • Dependency tracking
  • Version history
  • Audit trails

3. Collaborative Memory

  • Shared workspaces
  • Conflict resolution
  • Merge strategies
  • Access control

Security & Privacy

Data Protection

  • Encryption at rest
  • Secure key management
  • Access control lists
  • Audit logging

Compliance

  • Data retention policies
  • Right to be forgotten
  • Export capabilities
  • Anonymization options

Performance Optimization

Caching Strategy

  • Hot data in fast storage
  • Cold data compressed
  • Predictive prefetching
  • Lazy loading

Scalability

  • Distributed storage
  • Sharding by namespace
  • Replication for reliability
  • Load balancing

Frequently asked questions

What does the Agent Memory Coordinator AI skill do?

Agent skill for memory-coordinator - invoke with $agent-memory-coordinator

Why use Agent Memory Coordinator on TypingMind?

Because you install it once and use it with any model. Agent Memory Coordinator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Memory Coordinator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-memory-coordinator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Memory Coordinator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Memory Coordinator?

As many as you like. As long as a model supports skills, you can use Agent Memory Coordinator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Memory Coordinator AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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