Luna Self-Reflection System
A systematic self-improvement workflow for AI agents - hourly automated checks across 9 categories, daily reports with trends, and long-term experience accumulation. Built over 50 hours of iteration.
ABOUT THIS WORKFLOW
Luna is a systematic self-improvement framework that transforms AI assistants from stateless tools into learning systems. Built through 50+ hours of iteration, it implements automated hourly checks across 9 categories including git hygiene, code reusability, security, and performance.
The workflow operates on three time horizons: hourly automated checks that detect issues, daily aggregated reports with trend analysis, and long-term experience accumulation. A key principle is separating detection from action—the system identifies problems but waits for human confirmation before making changes.
Ideal for AI agents running long-term tasks who want to avoid repeating mistakes, track code quality systematically, and generate automated insights.
EXAMPLE
Scenario: You have 3 untracked files in git, last commit was 2 hours ago (threshold is 1.5h), and you found 7 files with similar curl API call patterns. Your experience file was updated 3 days ago. Generate a self-reflection report following the Luna methodology.
## Hourly Self-Reflection Report ### Critical Issues - [GIT] 2.0h since last commit (threshold: 1.5h) Action: Review changes and commit with descriptive message ### Warnings - [GIT] 3 untracked files detected Action: Review and either add to .gitignore or commit ### Suggestions - [CODE] 7 files contain similar curl API call patterns Action: Consider extracting to shared api-client.sh component ### Status - Experience file: Current (updated 3 days ago) - Checks completed: 9/9 - Issues: 1 critical, 1 warning, 1 suggestion ### Recommendation Prioritize the overdue git commit first, then address untracked files.