An adaptive communication skill that allows AI agents to learn and refine their explanation styles, formats, and depth based on human interaction patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install explain
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 explain 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 Explain?
The Explain skill is a sophisticated personalization layer designed to optimize how AI agents deliver information to humans. Within the ecosystem of Openclaw Skills, this tool focuses on a feedback-driven core loop that observes user reactions, identifies patterns, and confirms preferences. By tracking what resonates with a user and what leads to confusion, the agent moves away from generic responses and toward highly tailored communication.
This skill is essential for developers and users who interact with AI for complex tasks, as it manages everything from jargon density to the use of analogies. By utilizing the Explain skill, an AI agent becomes more than a responder; it becomes a communicative partner that understands when to provide a high-level overview and when to dive into deep technical detail, ensuring that every explanation is efficient and effective.
Explain Use Cases
- Adjusting technical depth during code explanations based on developer seniority.
- Customizing documentation summaries for quick reference versus deep learning sessions.
- Managing the use of jargon when explaining financial or legal concepts to non-experts.
- Refinement of communication styles for debugging sessions to prioritize actionable steps.
- Personalizing learning paths for self-improvement by tracking which analogy styles work best.
How Explain Works
- Observe: The agent monitors human-facing explanations to detect signals of success or confusion.
- Signal: It identifies explicit cues like "Got it" (success) or follow-up questions (missed) to evaluate its performance.
- Pattern: Once a specific communication style or format shows consistency over two or more signals, the agent identifies it as a pattern.
- Confirm: After receiving an explicit "yes" or confirmation from the user, the agent moves the preference from a pattern to a confirmed state in its memory.
Explain Setup
To initialize the Explain skill within your Openclaw Skills environment, you need to set up the local memory directory and the core tracking file. Use the following commands to get started:
mkdir -p ~/explain
touch ~/explain/memory.md
The skill will automatically begin populating this file as it learns from your interactions.
Explain Data Schema & Taxonomy
The skill maintains its intelligence through a structured memory system stored in ~/explain/memory.md. The data is organized into specific dimensions to ensure the agent can quickly retrieve preference levels:
| Dimension | Data Format | Description |
|---|---|---|
| Format | topic: preference (level) | Tracks if the user prefers bullets, prose, or headers. |
| Depth | topic: depth (level) | Calibrates the level of detail provided for specific topics. |
| Examples | topic: style (level) | Notes whether the user finds analogies and examples helpful. |
| Jargon | domain: level (level) | Manages technical language density per domain. |
| Never | approach (level) | Specifically logs communication styles that consistently fail. |
Each entry includes a level: pattern (initial observation), confirmed (user validated), or locked (reinforced through repeated use).
Explain Advanced Features
- Multi-stage learning lifecycle that transitions from initial patterns to locked preferences.
- Auxiliary reference files (
formats.md,depth.md,analogies.md) for nuanced communication calibration. - Domain-aware intelligence that allows for different explanation styles across code, theory, and decision-making.
- Proactive depth offering, where the agent provides a concise answer first and asks if the user requires more detail.
- Integration-ready structure that fits seamlessly into the broader Openclaw Skills framework for multi-agent support.
SKILL.md
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