Log errors, corrections, and feature requests for AI development.
npx skills add https://github.com/zbl91555/openclaw --skill self-improving-agent-zbl91555
Please help me install this Agent Skill. Skill: self-improving-agent Source: https://github.com/zbl91555/openclaw/tree/main/skills/self-improving-agent Command: npx skills add https://github.com/zbl91555/openclaw --skill self-improving-agent-zbl91555
This Skill enables AI systems and developers to log errors, corrections, and learnings, facilitating continuous improvement and knowledge sharing.
Use the self-improvement skill to log a command failure or correction immediately after it occurs, ensuring your learnings are captured for review.
High-intent search queries and answers about installing and using this skill.
To log AI errors and corrections for system refinement, record failures, unexpected behaviors, and user corrections immediately after they occur. This captures learnings during interactions to track recurring issues and improve future performance.
The best way to track recurring issues in AI development workflows is to maintain a structured log of system failures, knowledge gaps, and user feedback. This facilitates ongoing improvement by ensuring rapid documentation of incidents.
Logging knowledge gaps improves machine learning interactions by documenting outdated or missing information for updates. This ensures that system failures and user corrections are captured to refine responses and guide future feature development.
Yes, you can use markdown and yamlfrontmatter to document feature requests for AI systems. This approach provides a structured method to log new feature ideas, corrections, and errors to guide ongoing development and knowledge sharing.
quiznatIngest sources into a persistent interlinked Markdown knowledge base with YAML frontmatter.
opencodosAggregate and analyze Claude Code errors from JSONL logs and system logs.
handsomelong922Log learnings and corrections to .learnings markdown files for future sessions.
inbharataiBuild machine learning pipelines with MLflow experiment tracking and artifact management.
