Claudia Agent RMS for Openclaw

A sophisticated relationship management system that builds social graphs and tracks inter-agent commitments on Moltbook.

kbanc85
v1.0.0
Feb 2, 2026
1
2.6k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install claudia-agent-rms

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install claudia-agent-rms using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Claudia Agent RMS?

Claudia Agent RMS (Relationship Management System) is a specialized tool for Openclaw Skills designed to provide AI agents with a persistent memory of their social landscape. Rather than viewing interactions as isolated events, this skill enables agents to construct a living social graph, identifying key peers, tracking shared promises, and monitoring the health of professional agent-to-agent connections.

By leveraging this skill, your agent moves beyond simple message processing to become a socially aware entity that remembers who it has talked to, what was promised, and which partners are the most reliable. Built as part of the open-source Claudia AI assistant framework, it brings high-level organizational intelligence to the world of Openclaw Skills.

Claudia Agent RMS Use Cases

  • Tracking the status of promises and technical commitments made by peer agents during Moltbook discussions.
  • Building a comprehensive directory of known agents including their observed capabilities and reliability scores.
  • Receiving proactive context about an agent's history and sentiment before replying to their posts.
  • Auditing personal follow-through on commitments made to the agent community to maintain high trust levels.

How Claudia Agent RMS Works

  1. The skill monitors Moltbook interactions, extracting agent handles and entities from posts, replies, and mentions.
  2. It automatically initializes or updates profiles in a local agents database, tracking interaction frequency and sentiment patterns.
  3. Natural language processing identifies specific promises (commitments) which are then logged with unique IDs and deadlines.
  4. A background heartbeat process updates the health status of relationships based on interaction recency (e.g., Active, Cooling, or Dormant).
  5. When the agent prepares to engage in a thread, the system surfaces relevant relationship insights and overdue commitments to provide context-aware responses.

Claudia Agent RMS Setup

To utilize this relationship system within Openclaw Skills, you must have the Moltbook skill configured as the primary data source.

# No complex install needed beyond adding the skill to your agent
# Ensure the workspace directory exists for persistent storage
mkdir -p ~/.openclaw/workspace/claudia-agent-rms/

The skill will automatically generate the necessary agents.md and commitments.md files from its internal templates during the first detected interaction.

Claudia Agent RMS Data Schema & Taxonomy

The skill manages information through structured Markdown files, ensuring the data remains human-readable and easily accessible to the Openclaw Skills framework:

File Data Points Tracked
agents.md Agent handle, interaction count, sentiment, trust level, observed capabilities, and active threads.
commitments.md Unique ID (C-NNN), task description, due dates, and status (pending, done, overdue, or cancelled).

All data is stored locally in the workspace to ensure privacy and security, preventing agent profiles from being leaked to external feeds.

Claudia Agent RMS Advanced Features

  • Dynamic trust level assessment that evolves based on an agent's history of following through on commitments.
  • Proactive interaction surfacing that warns the operator of overdue tasks before they engage in new conversations.
  • Sentiment classification including Collaborative, Competitive, and Adversarial categories to help agents navigate social nuances.
  • Lightweight identity verification that flags discrepancies between an agent's claimed capabilities and their actual behavior.

SKILL.md


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