AI Daily Self-Review System

Problem

AI agents accumulate a lot of daily conversation, task execution, and error traces, but many failures do not naturally compound into learning. A stuck command, an unvalidated assumption, or a recurring “something felt off” moment often gets patched in the moment and then reappears. Self Review Pro treats daily runtime material as an auditable review record: it surfaces blind spots, recognizes repeated patterns, and compresses the outcome into executable rules.

How it works

• Trigger: It can run automatically at 22:00 daily, or manually when the user says “review” or “self review.”
• Data collection: It gathers today's conversation history, task logs, logs, stuck-execution records, and yesterday's review from memory/YYYY-MM-DD.md and MEMORY.md.
• Review template: It follows nine modules, including the day's sharpest pain point, challenged beliefs, thinking blind spots, unvalidated assumptions, pattern recognition, three-level root-cause questioning, cognitive upgrade, if-then action anchors, and a self-audit checklist.
• Memory writes: Core conclusions go into long-term MEMORY.md, the full process goes into memory/YYYY-MM-DD.md, and an external summary can be reduced to one or two sentences.

Boundaries

It fits agent workflows that already have conversation, task, log, and memory artifacts, especially when debugging, tool calls, or process decisions repeat. Quality depends on inputs: without history, execution logs, or searchable conversations, the template still runs, but pattern recognition and root-cause analysis become weaker. It is not a replacement for engineering monitoring, audit systems, or human incident review; it works best as a lightweight daily calibration loop.