aj-self-improving-agents

What This Skill Does

The aj-self-improving-agents skill provides a structured framework for continuous learning and adaptation within your OpenClaw environment. It transforms every session into a feedback loop by systematically logging errors, user corrections, knowledge gaps, and process improvements into a dedicated .learnings directory. This mechanism allows the AI agent to move beyond transient interactions, building a persistent repository of institutional knowledge. By tracking failures, recording successful resolutions, and promoting recurring patterns into core documentation files like CLAUDE.md or SOUL.md, this skill ensures the agent becomes more efficient, accurate, and aligned with user preferences over time. It essentially creates a long-term memory buffer for behavioral and technical refinements.

Installation

To integrate this skill into your workflow, you can use the OpenClaw management tool:

1. Via ClawdHub (Recommended): Run clawdhub install self-improving-agent in your terminal.
2. Manual Installation: Clone the repository manually into your local skills directory using git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent. Ensure that your workspace structure includes the required .learnings/ directory to facilitate log tracking.

Use Cases

• Error Resolution: Automatically capture stack traces or command failures in ERRORS.md to analyze patterns and prevent recurrence.
• Knowledge Refinement: When a user corrects the agent, use LEARNINGS.md to document the specific correction, ensuring the mistake is not repeated.
• Feature Gap Analysis: Track missing capabilities in FEATURE_REQUESTS.md to help prioritize future development.
• Process Hardening: Identify better approaches to recurring tasks and formalize these into standard operating procedures by promoting them to AGENTS.md or TOOLS.md.

Example Prompts

1. "I corrected your previous output because the syntax was invalid; please log this as a learning under category correction in the .learnings folder."
2. "The last API call failed with a timeout; capture these integration details in ERRORS.md so we can debug the connection pattern later."
3. "I've discovered a much faster way to deploy these containers. Please log this as a best_practice in LEARNINGS.md and update our AGENTS.md workflow."

Tips & Limitations

• Review Cycles: The skill is most effective when the agent is prompted to review its logs before starting a major task.
• Maintenance: Periodically audit your .learnings files to prune stale entries and promote high-value insights to permanent documentation (SOUL.md or CLAUDE.md).
• Scope: This skill is focused on text-based documentation and reflection; it does not automatically execute fixes to code unless paired with a secondary execution agent.