A dual-core efficiency tool that builds personalized user portraits and standardizes feedback workflows to eliminate communication overhead.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install get-to-know-you
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 get-to-know-you 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 Get To Know You?
The Get To Know You skill is a comprehensive personalization and workflow standardization enhancement designed for the Openclaw Skills ecosystem. It functions as a dual-core engine: first, it actively constructs a high-fidelity user portrait through Socratic guided Q&A to populate core configuration files; second, it enforces a strict four-step communication protocol for task optimization and negative feedback.
By systematically collecting data on work backgrounds, preferences, and habits, the skill ensures that AI agents operate with deep contextual awareness. Simultaneously, its mandatory alignment process prevents the common pitfall of iterative, unconfirmed modifications, fundamentally reducing token wastage and saving developer time during the skill tuning process.
Get To Know You Use Cases
- Initial onboarding and configuration of new Openclaw Skills environments.
- Transitioning from generic AI responses to a highly personalized assistant that understands specific job roles and reporting structures.
- Managing negative feedback or unsatisfactory task results without repetitive, unguided revisions.
- Requesting functional upgrades or optimizations to existing agent skills while ensuring requirement alignment.
How Get To Know You Works
- The skill triggers automatically upon installation or via user intent to initiate a personalized deep-dive.
- In Questionnaire Mode, it asks targeted, one-at-a-time questions to build a profile across dimensions like work info, communication style, and output preferences.
- Collected data is automatically synchronized and mapped to core files such as USER.md, SOUL.md, and AGENTS.md.
- When negative feedback is received, the skill activates a mandatory 4-step workflow: Align Requirements, Output Plan, User Confirmation, and Execution.
- Execution only proceeds once the user provides 100% confirmation of the proposed optimization plan.
Get To Know You Setup
To initialize the personalization process within your Openclaw Skills environment, use the provided collection script:
# Start the full information collection process
python3 scripts/collector.py --full
# Targeted collection of specific dimensions (e.g., work preferences)
python3 scripts/collector.py --dimension work_preferences
# Manually update a specific preference in the user configuration
python3 scripts/collector.py --add "doc_output_preference=concise" --target USER.md
Get To Know You Data Schema & Taxonomy
The skill organizes user intelligence into a structured taxonomy across the following system files:
| Information Type | Target Configuration File | Purpose |
|---|---|---|
| Role & System Config | AGENTS.md | Defines how agents interact and their specific roles. |
| Values & Conduct | SOUL.md | Establishes the ethical and behavioral guidelines for the AI. |
| Experience & Memory | MEMORY.md | Stores project history, decision records, and past summaries. |
| Personal Preferences | USER.md | Tracks habits, communication styles, and document specifications. |
| Skill-Specific Data | [Skill Directory] | Stores configurations unique to individual Openclaw Skills. |
Get To Know You Advanced Features
- Socratic Guided Q&A: Prevents information overload by asking single, focused questions.
- Auto-Resume Progress: Automatically saves the state of a questionnaire if the user switches topics mid-conversation.
- Fragmented Resident Mode: Passively recognizes and confirms new user preferences mentioned in casual dialogue.
- Mandatory 4-Step Process: A rigid workflow for skill optimization that prohibits execution without prior plan approval.
- Custom Question Bank: Extensible reference bank in
references/question_bank.mdfor tailored industry onboarding.
SKILL.md
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