A structured framework for AI agents to autonomously learn from mistakes, user preferences, and recurring patterns to improve over time.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-improving-openclaw
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 self-improving-openclaw 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 Self-Improving OpenClaw?
Self-Improving OpenClaw provides a comprehensive learning loop for AI agents, allowing them to capture, review, and promote insights within a workspace. By utilizing Openclaw Skills, this system enables agents to transform fleeting interactions into permanent knowledge, effectively building a memory tier system that ranges from raw logs to core behavioral instructions found in SOUL.md or AGENTS.md.
The skill focuses on high-fidelity feedback loops where corrections, preferences, and feature requests are documented in real-time. This ensures that the agent evolves alongside the developer, reducing the need for repetitive instructions and increasing the overall efficiency of the coding assistant within the Openclaw Skills ecosystem.
Self-Improving OpenClaw Use Cases
- Persistent agent alignment when a user provides specific behavioral corrections or stylistic preferences.
- Systematic tracking of tool failures and API errors to identify and solve recurring technical debt.
- Automating the transition of project-specific knowledge into long-term workspace memory tiers.
- Managing complex user preferences across multiple development sessions for personalized assistance.
How Self-Improving OpenClaw Works
- The agent captures learning signals like user feedback, preferences, or command errors into raw files within the .learnings/ directory.
- During a heartbeat or maintenance phase, the agent reviews these logs to identify recurring patterns.
- Patterns that meet specific frequency thresholds are promoted to HOT or WARM memory tiers.
- Stable, long-term insights are integrated into permanent files such as SOUL.md or TOOLS.md for global application.
- The system maintains efficiency by archiving stale data and compacting verbose entries periodically.
Self-Improving OpenClaw Setup
To begin using this skill within the Openclaw Skills framework, run the initialization script to prepare the workspace directory structure:
bash scripts/init-workspace.sh
This command creates the .learnings/ and .self-improving/ directories necessary for data ingestion and tier management.
Self-Improving OpenClaw Data Schema & Taxonomy
The skill organizes information into a structured hierarchy to manage the agent's context window effectively:
| Tier | Storage Location | Accessibility |
|---|---|---|
| RAW | .learnings/ | Intake only; not automatically loaded into context. |
| HOT | .self-improving/HOT.md | High-priority patterns loaded at session start. |
| WARM | .self-improving/domains/ | Context-specific data loaded on demand. |
| COLD | .self-improving/archive/ | Historical data preserved for manual queries. |
| PERMANENT | SOUL.md / AGENTS.md | Core identity and tool instructions. |
Self-Improving OpenClaw Advanced Features
- Multi-tier memory management that optimizes token usage by loading only relevant context.
- Conflict resolution logic that prioritizes specific project rules over global agent behaviors.
- Automated heartbeat integration for background maintenance and pattern promotion.
- Transparency features where the agent cites its learned sources (e.g., from HOT.md) during execution.
- Security boundaries that automatically redact credentials and sensitive data from learning logs.
SKILL.md
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