A recursive learning framework that allows AI agents to codify user feedback into a permanent, evolving set of operational rules.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sw-self-improving-agent
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 sw-self-improving-agent 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 Agent Skill?
The Self-Improving Agent Skill is a sophisticated framework designed to transition AI agents from static execution to dynamic, iterative learning. By implementing this within your Openclaw Skills library, you enable a systematic correction loop where every user intervention is analyzed, generalized, and documented in a persistent storage file. This ensures that the agent does not just fix a mistake in the moment but learns the underlying preference to prevent future occurrences.
This skill focuses on the long-term optimization of agent behavior. Instead of relying on hard-coded instructions, the agent builds its own refined operating manual. This makes it an essential tool for developers and power users who require their agents to adapt to complex, highly specific workflows without constant manual prompt engineering.
Self-Improving Agent Skill Use Cases
- Reducing repetitive feedback by teaching agents specific communication styles and preferences.
- Maintaining client-specific protocols and formal communication standards across multiple projects.
- Automating the refinement of operational workflows, such as calendar management and status reporting.
- Scaling organizational knowledge by allowing agents to build a shared repository of operational rules.
How Self-Improving Agent Skill Works
- The user identifies a mistake or provides a correction during an active session.
- The agent analyzes the feedback to identify the broader behavioral pattern or rule.
- The agent logs the new rule with a mandatory date stamp into a centralized RULES.md file.
- At the beginning of every subsequent session, the agent reads the RULES.md file to initialize its operational constraints.
- The agent performs periodic audits of the rule set to merge duplicates and resolve contradictions, ensuring the most recent instructions take precedence.
Self-Improving Agent Skill Setup
To deploy this skill within your Openclaw Skills environment, first initialize the rules storage file:
touch RULES.md
Next, integrate the self-improvement logic into your AGENTS.md or primary system instructions:
## Self-Improvement
After ANY correction from the user:
1. Log the correction pattern to RULES.md with date
2. Identify the general rule (not just the specific instance)
3. Check if a similar rule already exists — update rather than duplicate
4. Silently scan RULES.md every ~10 interactions for contradictions
Self-Improving Agent Skill Data Schema & Taxonomy
The skill utilizes a structured Markdown schema to manage long-term memory and operational logic:
| Feature | Implementation | Description |
|---|---|---|
| Storage File | RULES.md | The primary database for all learned behaviors and preferences. |
| Date-Stamping | [YYYY-MM-DD] | Ensures chronological tracking and conflict resolution. |
| Categorization | ## Headers | Rules are grouped into sections like Communication, Operations, and People. |
| Atomic Rules | List Items (-) | Single-line, actionable instructions to maximize model adherence. |
| Metrics Tracking | AGENTS.md | Logic for tracking correction frequency and rule-set growth. |
Self-Improving Agent Skill Advanced Features
- Autonomous Rule Consolidation: Automatically merges similar rules and removes stale instructions during monthly reviews.
- Conflict Resolution: Implements a temporal logic where newer rules automatically override older, contradictory entries.
- Pattern Promotion: Distinguishes between one-time incidents and systemic patterns to ensure high-quality rule generation.
- Performance Analytics: Tracks the ratio of unique vs. repeat corrections to quantify the agent's learning efficiency.
SKILL.md
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