When should I run an After-Action Review?

You should run an AAR after a team has completed its tasks but before deleting the team configuration to ensure all metadata and agent contexts are preserved.

Does this skill work with any agent team?

Yes, the AAR skill is designed to work with any team type or configuration within the functional-claude ecosystem.

What is the difference between AAR and /evaluate-spawn?

AAR evaluates the team's process and effectiveness (how they worked), while /evaluate-spawn focuses specifically on the quality of the generated output.

Where are the AAR reports stored?

Reports are automatically written to your project's 'docs/retrospectives/' directory in a standardized Markdown format for long-term tracking.

Why does it collect input from individual agents?

Following the FM 7-0 participant-first principle, collecting individual agent perspectives ensures a comprehensive synthesis of what worked and what didn't from every role's unique viewpoint.

After-Action Review (AAR)

Name: After-Action Review (AAR)
Author: nthplusio

bynthplusio

•

협업 도구

Conducts structured process evaluations for AI agent teams to improve collaboration and workflow efficiency.

The After-Action Review (AAR) skill facilitates a post-task evaluation for multi-agent teams, applying military-grade retrospective protocols to Claude Code workflows. It captures individual agent perspectives, compares planned versus actual outcomes, and identifies specific process improvements, distinct from code quality evaluations. By documenting these insights into standardized retrospective files, it enables developers to optimize team compositions, model assignments, and task delegation strategies for future iterations.

주요 기능

01Detailed usage and model assignment summaries

02Multi-agent participant perspective collection

03Direct integration with GitHub for process improvements

04Structured 4-question military-grade AAR protocol

051 GitHub stars

06Automatic retrospective documentation generation

사용 사례

01Analyzing why a multi-agent workflow failed or succeeded

02Creating a historical record of team performance and process evolution

03Optimizing agent team compositions for complex development tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nthplusio/functional-claude after-action-review

For use in Claude.ai and ChatGPT

주요 기능

01Detailed usage and model assignment summaries

02Multi-agent participant perspective collection

03Direct integration with GitHub for process improvements

04Structured 4-question military-grade AAR protocol

051 GitHub stars

06Automatic retrospective documentation generation

사용 사례

01Analyzing why a multi-agent workflow failed or succeeded

02Creating a historical record of team performance and process evolution

03Optimizing agent team compositions for complex development tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nthplusio/functional-claude after-action-review

For use in Claude.ai and ChatGPT