Человеческая версия Reflexion для агентов ИИ, построенная на военном методе, который был стандартной практикой в течение пятидесяти лет.

Запуск /aar-loop после любой задачи или сессии. Он делает три вещи:

1. ** Пересмотр сессии** с 4-м вопросом армии США после обзора действий.
2. ** Найдите то, что стоит исправить , и запишите улучшение прямо в свои файлы навыков или правил, поэтому исправление есть, прежде чем оно вам понадобится снова, а не просто записано где-то. 3. Сохраняет уроки** в постоянном файле LESSONS.md, который ваш ИИ загружает в следующий раз.

Направьте следующую сессию на этот файл (и на любой навык или правило, которое было исправлено), и агент перестанет повторять ту же ошибку. Запускайте его каждый сеанс и соединения агента вместо перезапуска с нуля каждый раз.

Откуда берется метод

Армия США формализовала «Обзор действий после» в середине 1970-х годов, в годы после Вьетнама, как структурированный отчет для учебных учений. Он стал официальной доктриной в 1993 году с полевым руководством TC 25-20, и он по-прежнему работает после каждой ротации в Национальном учебном центре, Форт-Ирвин, одной из самых реалистичных сред боевой подготовки в мире.

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): 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 every attempt from scratch.

Nobody’s wired that loop into your AI harness by default. This skill is the manual version: you run the reflection step yourself, once per session, and LESSONS.md is the buffer. Same idea, no automation, and it costs you five minutes.

Install

Paste this into Claude Code, Codex, or your AI harness:

Install this skill globally: https://github.com/coopersimson96/aar-loop

Use

/aar-loop

or just say “run an aar” after finishing something. Sample output:

AAR · production deploy

1. What was supposed to happen?
   Deploy the new build and have all three services (web, worker,
   scheduler) come up on the new version.

2. What actually happened?
   The deploy script reported success and exited 0. Web and worker were on
   the new version, but the scheduler was still running the old one; it had
   never actually restarted.

3. Why was there a difference?
   The deploy script only checks the exit code of the restart command, not