An automated system for analyzing session history to keep your AI agent's memory files synchronized with your latest preferences and feedback.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install memory-analyzer 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 Memory Analyzer?
The Memory Analyzer is a sophisticated utility designed to bridge the gap between ephemeral chat sessions and persistent agent behavior. By leveraging high-performance LLMs like Google Gemini, this tool scans conversation logs to identify explicit and implicit user instructions, ensuring that every piece of feedback is permanently integrated into the agent's long-term storage. Using this within Openclaw Skills ensures your AI evolves alongside your project needs without requiring manual documentation updates.
This skill acts as the cognitive engine for your workspace, processing raw dialogue into structured knowledge. It allows the agent to remember exactly how you prefer your code formatted, which libraries you favor, and how you want tasks prioritized, effectively building a customized digital assistant over time.
Memory Analyzer Use Cases
- Capturing specific coding style preferences expressed during a live chat session.
- Automatically updating agent behavioral rules based on direct user feedback such as "always use TypeScript."
- Persisting project-specific context or identity notes across multiple independent sessions.
- Extracting recurring feedback patterns to refine the agent's personality and interaction tone.
How Memory Analyzer Works
- The skill monitors the session history directory to detect new interactions and dialogue.
- It analyzes the text to extract critical data points including user preferences, technical requirements, and feedback.
- The analyzer categorizes these insights into specific domains like user behavior, agent rules, or project facts.
- Relevant Markdown memory files are automatically updated with new structured data to ensure persistence.
Memory Analyzer Setup
To deploy this skill within your Openclaw Skills environment, ensure you have the model configuration ready. You can trigger the analyzer with the following command:
# Run memory-analyzer skill with default Google model
Run memory-analyzer skill
For manual execution or custom model overrides, use the direct path:
python3 /home/ubuntu/.openclaw/workspace/skills/memory-analyzer/analyzer.py --model google/gemini-3-flash-preview
Memory Analyzer Data Schema & Taxonomy
The Memory Analyzer organizes information into a specific set of Markdown files to maintain a structured and readable knowledge base.
| File Name | Data Type | Description |
|---|---|---|
| MEMORY.md | Long-term Memory | Stores factual project context and long-term knowledge. |
| AGENTS.md | Rules | Contains operational protocols and behavioral constraints. |
| USER.md | Preferences | Tracks user-specific habits, preferred tools, and formats. |
| IDENTITY.md | Identity | Defines the agent's core purpose and system-level notes. |
| SOUL.md | Personality | Manages the tone, voice, and personality updates. |
Memory Analyzer Advanced Features
- Automated feedback loop integration native to Openclaw Skills.
- Powered by Google Gemini 3 Flash Preview for high-speed, cost-effective context analysis.
- Intelligent extraction of implicit preferences from natural language triggers.
- Multi-file synchronization to maintain a consistent agent persona across different contexts.
SKILL.md
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