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debrief

Record team observations into role-specific LEARNING.md or project DEBRIEF.md files.

Updated Apr 5, 2026
Search Tags:#markdown#learning#memory#team#capture#observations
One-click install
npx skills add https://github.com/hilbertp/liberation-of-bajor --skill debrief-hilbertp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debrief
Source: https://github.com/hilbertp/liberation-of-bajor/tree/main/.claude/skills/debrief
Command: npx skills add https://github.com/hilbertp/liberation-of-bajor --skill debrief-hilbertp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Teams repeatedly lose valuable insights when each AI role starts a new session, leading to repeated mistakes and missed opportunities for improvement. This skill ensures that every useful observation is recorded at the moment it occurs, creating a shared institutional memory.

Core Features & Use Cases

  • Two destinations: role‑specific LEARNING.md for cross‑project knowledge, and project‑level DEBRIEF.md for raw observations awaiting triage.
  • Automatic capture triggers: friction points, platform constraints, corrections from leadership, successful or failed decisions, and end‑of‑deliverable reflections.
  • Use case example: When you discover a platform limitation while implementing a feature, you invoke the debrief skill to immediately append a learning to your role’s LEARNING.md; if you encounter a workflow bottleneck, you add it to the shared DEBRIEF.md for later review.

Quick Start

Ask the debrief skill to record a new learning or observation directly into your role's LEARNING.md or the project DEBRIEF.md.

Frequently Asked Questions about debrief

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I capture team learnings and observations in markdown?▼

To capture team learnings in markdown, you record friction points, platform constraints, or decisions directly into role-specific LEARNING.md or project-level DEBRIEF.md files without external services.

What is the best way to build institutional memory for AI team sessions?▼

Building institutional memory involves appending observations and corrections to markdown files at the moment they occur, ensuring repeated mistakes are avoided when any AI role starts a new session.

How do I record a workflow bottleneck for later triage?▼

You record a workflow bottleneck for triage by appending the raw observation to the shared project DEBRIEF.md file, separating immediate role learnings from broader project-level issues.

Can I use this to log cross-project knowledge for specific roles?▼

Yes, you can log cross-project knowledge by writing entries to a role-specific LEARNING.md file, capturing successful or failed decisions to maintain a continuous record for that role.

When should I trigger a learning capture in a repository?▼

You trigger learning capture when friction points, platform limitations, leadership corrections, or end-of-deliverable reflections occur, immediately writing the entry to markdown to preserve context.

Do I need external services to store team observations?▼

No, you do not need external services to store team observations; the process writes entries directly to markdown files in the repository using only the current environment.

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