Aar Loop — AI skill for Claude Code

AAR Loop

A human-run version of Reflexion for AI agents, built on a military method that's been standard practice for fifty years.

Run `/aar-loop` after any task or session. It does three things:

1. Reviews the session with the US Army's 4-question After Action Review.
2. Finds what's worth fixing and writes the improvement straight into your skills or rules files, so the fix is there before you need it again, not just written down somewhere.
3. Saves the lessons to a persistent LESSONS.md file that your AI loads next time.

Point the next session at that file (and at whatever skill or rule got patched) and the agent stops repeating the same mistake. Run it every session and the agent compounds instead of restarting from zero each time.

Where the method comes from

The US Army formalized the After Action Review in the mid-1970s, in the years after Vietnam, as a structured debrief for training exercises. It became official doctrine in 1993 with field manual TC 25-20, and it's still run after every rotation at the National Training Center, Fort Irwin, one of the most realistic combat training environments in the world. Fifty years of continuous use on one method, because the mechanism is simple and hard to argue with: separate the debrief from blame, ask what was supposed to happen versus what actually happened, and the gap tells you exactly what to fix before the next fight.

The 4 questions, verbatim:

1. What was supposed to happen?
2. What actually happened?
3. Why was there a difference?
4. What do we do the same or differently next time?

The research parallel

This is also, functionally, a manual version of **Reflexion** (Shinn et al., 2023, [arXiv:2303.11366](https://arxiv.org/abs/2303.11366)): an agent framework where a model generates a verbal self-reflection after a failed attempt, stores it in an episodic memory buffer, and carries it into the next attempt. Reflexion agents that keep their reflections outperform agents that start ev