Imprint is an adaptive operator modeling skill that enables AI agents to learn user preferences and decision patterns through passive observation rather than manual configuration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install imprint
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 imprint 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 Imprint?
Imprint represents the next generation of personalization for AI agents. Instead of relying on static system prompts or personality quizzes, this skill allows an agent to build a sophisticated, predictive model of an operator by watching their workflows in real-time. It moves beyond generic AI responses by creating a unique behavioral profile that evolves with every interaction.
By integrating Imprint into your Openclaw Skills library, your agent gains the ability to anticipate needs, match communication styles, and recognize work rhythms. The system is designed to be self-correcting, using a feedback loop where explicit user corrections carry higher weight to refine the model's accuracy, ensuring the agent becomes more effective the more you use it.
Imprint Use Cases
- Reducing prompt engineering by having the agent naturally adopt your preferred tone and level of detail.
- Automating routine file management and workspace preparation based on predicted user needs.
- Streamlining decision-making processes by allowing the agent to pre-load context for choices you are likely to make.
- Personalizing agent autonomy levels, letting the agent handle routine tasks silently while flagging high-importance items for review.
How Imprint Works
- Observe: The agent passively tracks operator signals including decision patterns, communication style, and attention shifts without active interrogation.
- Model: Observations are processed into a lightweight operator profile stored in a local JSON file, featuring confidence scores for various traits.
- Anticipate: The agent uses high-confidence traits to pre-emptively adjust its behavior, such as pre-loading files or adjusting response depth.
- Correct: The system continuously learns from prediction failures; explicit corrections from the operator are weighted heavily to update the model.
- Decay: A mathematical decay function ensures that older observations lose weight over time, allowing the agent to adapt as the user's habits change.
Imprint Setup
To integrate Imprint into your local environment, ensure you have the core framework installed and follow these steps:
# Navigate to your agent skills directory
cd your-agent-workspace/skills
# Clone or copy the Imprint skill files
mkdir -p imprint/observations
# Initialize the operator model
touch imprint/operator-model.json
Configure your agent to load the imprint/operator-model.json file at the start of every session to apply learned traits.
Imprint Data Schema & Taxonomy
Imprint organizes its data locally to ensure privacy and fast access. The primary data structures include:
| File | Description |
|---|---|
operator-model.json |
The main repository of learned traits, confidence scores, and accuracy metrics. |
observations/YYYY-MM-DD.md |
Historical logs of significant behavioral signals captured during sessions. |
operator-model-schema.json |
The formal JSON schema defining the structure of the trait model. |
Key traits tracked include communication_style, decision_speed, detail_preference, and autonomy_preference.
Imprint Advanced Features
- Confidence Thresholding: Dynamic behavior adjustment based on the mathematical certainty of a trait (e.g., traits with >0.8 confidence become strong defaults).
- Behavioral Decay Function: Uses an exponential decay formula to ensure recent user behavior takes precedence over legacy data.
- Local-First Privacy: All modeling data remains within the local workspace with no external API calls or network transmission of behavioral patterns.
- Feedback Weighting: Implements a 5x signal weight for explicit corrections compared to implicit observations for rapid model stabilization.
SKILL.md
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