An advanced skill for building dynamic user profiles through passive information extraction and proactive topic exploration to optimize communication.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install user-insight
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 user-insight 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 User Insight?
User Insight is a sophisticated module designed to help AI agents develop a deep, progressive understanding of their users. By functioning within the Openclaw Skills framework, it constructs a dynamic user profile by analyzing daily conversations. It identifies interests, habits, and preferences without intrusive questioning, instead using natural dialogue flows to gather data.
The skill operates on the principle of continuous evolution, much like a recommendation algorithm. It doesn't just wait for user input; it actively discovers what makes a user tick by testing different topic categories and measuring engagement levels. This ensures that every interaction feels personalized, understood, and increasingly relevant over time.
User Insight Use Cases
- Personalizing agent responses based on long-term user preferences and communication styles.
- Re-engaging users after periods of inactivity by initiating conversations on high-interest topics.
- Identifying and avoiding sensitive or disliked subjects to maintain a positive user experience.
- Discovering hidden user interests through structured boundary-pushing and topic testing.
How User Insight Works
- The skill passively monitors conversations to extract interests, habits, and values, storing them in a local memory workspace.
- It employs Guided Collection strategies, such as storytelling or hypothetical scenarios, to elicit user views naturally.
- During periods of low engagement, Active Exploration logic selects candidate topics from diverse categories like technology, lifestyle, or history.
- User reactions (reply speed, length, and sentiment) are analyzed to calculate interest scores.
- The system runs a recommendation algorithm that balances 70 percent deep-diving into known interests with 30 percent exploration of new boundaries.
User Insight Setup
To get started with User Insight in your Openclaw Skills environment, ensure your workspace memory directory is initialized:
mkdir -p ~/.openclaw/workspace/memory/insights
The skill will automatically generate the required user-profile.json and topic-exploration.json files upon the first interaction. No external API configuration is necessary as it utilizes local memory for privacy.
User Insight Data Schema & Taxonomy
The skill organizes its data within the ~/.openclaw/workspace/memory/ directory using the following structure:
| File/Folder | Purpose |
|---|---|
user-profile.json |
Stores the primary user profile, including interest scores, communication style, and basic info. |
topic-exploration.json |
Maintains a log of all topic exploration attempts and identifies success patterns. |
insights/ |
A directory containing raw daily insight logs in YYYY-MM-DD.json format. |
Each interest in the profile is assigned a score (0.0 to 1.0) and categorized for precise recommendation logic.
User Insight Advanced Features
- Multi-agent collaboration using cron for scheduled topic scouting and web_search for fresh content discovery.
- Self-improving feedback loops that identify which opening styles (direct, story-based, or curiosity-driven) work best for the specific user.
- Granular privacy controls allowing users to stop collection, delete specific items, or reset the entire profile via natural language commands.
- Sentiment and energy detection to adjust the verbosity and formality of the AI responses in real-time.
SKILL.md
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