Skill

Self-Improvement System for AI agents. Features: feedback collection, hint extraction, rule learning. Use for: learning from user corrections.

This skill appears to do what it says (collect feedback, learn rules, inject them), but most core logic is provided by an external Python package named 'claw-rl' — not by the scripts included here. Before installing or enabling autoInject/autoLearn you should: (1) verify the provenance of the 'claw-rl' package (check the GitHub repo, release tags, and PyPI owner), (2) audit the package for network calls, telemetry, or credential access, (3) avoid enabling autoInject in production until you trust the rules it will inject, (4) run the daemon and package in an isolated or sandboxed environment and back up ~/.openclaw/workspace, and (5) prefer a pinned package version or vendored, reviewed code if possible. If you cannot audit the external package, treat enabling this skill as higher risk.

Type: OpenClaw Skill Name: opensourceclaw-claw-rl Version: 2.0.3 The 'opensourceclaw-claw-rl' skill bundle implements a reinforcement learning framework designed to help AI agents improve through user feedback and rule extraction. The provided Python scripts (collect_feedback.py, daemon.py, get_rules.py, and status.py) act as wrappers for the 'claw-rl' library, managing a local learning workspace and background daemon. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found; the system's logic and documentation are entirely consistent with its stated purpose of agent self-improvement.