A layered experience mechanism that provides AI agents with per-skill memory to prevent mistakes before they happen and evolve through error-driven learning.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-experience-layer
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 skill-experience-layer 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 Skill Experience Layer?
The Skill Experience Layer is a specialized framework designed to give AI agents permanent, per-skill memory. Unlike traditional retrospective models, this skill emphasizes a preventive approach by performing pre-call checks against established best practices and common pitfalls. By integrating these Openclaw Skills into your workflow, agents become capable of error-driven learning, where every mistake is recorded and used to refine future performance, ensuring the agent gets smarter with every execution.
This architecture fits perfectly into existing memory hierarchies, transitioning lessons from short-term corrections to long-term architectural best practices. It serves as the missing glue between general self-improvement frameworks and the practical, daily usage of AI tools.
Skill Experience Layer Use Cases
- Setting up a new agent instance with persistent memory capabilities.
- Avoiding the repetition of technical mistakes across different development sessions.
- Integrating self-improving workflows with existing memory hierarchies like hot or short-term storage.
- Building self-growing agents that learn from practical task execution and adapt to specific environments.
How Skill Experience Layer Works
- When a skill is invoked, the agent performs a pre-call check by reading the corresponding experience file in the experiences directory.
- The agent reviews common mistakes and best practices defined in the JSON schema to adjust its approach before execution.
- If a mistake occurs, the agent stops retrying and records the error context in a dedicated corrections file.
- The skill-specific JSON experience file is updated with a new entry, incrementing failure counts and updating prevention strategies.
- If the same mistake repeats twice or more, the system triggers an automatic evolution process to refine the skill's operational logic.
Skill Experience Layer Setup
To implement this within your environment, follow these installation and configuration steps:
- Create the necessary directory structure for experience storage:
mkdir -p memory/experiences/
- Initialize a JSON experience file for your target skill (e.g.,
my-skill.json) with the required metadata and pattern objects. - Configure your agent's read instructions to prioritize checking the
memory/experiences/path before calling any external tools. - Ensure integration with your self-improving or capability-evolver modules to allow for automatic file updates.
Skill Experience Layer Data Schema & Taxonomy
The skill organizes data in JSON format within the memory/experiences/ directory. Each file tracks performance metrics and specific learning instances.
| Field | Type | Description |
|---|---|---|
| name | String | The unique identifier of the skill being tracked. |
| totalExecutions | Integer | Total count of skill calls. |
| successCount | Integer | Total number of successful outcomes. |
| failureCount | Integer | Total number of failed attempts recorded. |
| experiences | Array | Detailed log containing context, lessons, and prevention steps for specific incidents. |
| patterns | Object | Contains lists of commonMistakes and bestPractices for quick pre-call reference. |
Skill Experience Layer Advanced Features
- Pre-call automated reflection to prevent recurring errors using Openclaw Skills.
- First-class integration with capability-evolver for autonomous logic and best-practice improvement.
- Seamless compatibility with multi-layered memory systems (instant, hot, short-term, and long-term).
- Automatic backup and rollback mechanisms for experience file updates to ensure data integrity.
- Cross-skill pattern recognition to apply lessons from one tool to another effectively.
SKILL.md
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