Can I manually trigger a learning extraction?

Yes, you can use the /session-learnings command to manually scan the current session or use flags like --all-recent to review historical patterns.

What is a Session Learning reflection?

A reflection is a codified document (REFL-XXX) that stores a specific lesson, anti-pattern, and fix discovered during a coding session to prevent the same mistake from happening again.

How are learning candidates scored?

Learnings are scored based on factors like financial calculation impact (+3), repeated patterns (+3), production impact (+2), and security implications (+2).

When does the Session Learnings skill automatically activate?

It activates after completing work sessions, before closing Pull Requests, or when findings.md files are updated with new key learnings.

Where are the learning reports stored?

Reports are archived in the docs/session-learning-reports/ directory, while specific reflections are stored in docs/skills/.

Session Learnings & Reflections

Name: Session Learnings & Reflections
Author: nikhillinit

bynikhillinit

학습 및 문서화

Extracts and codifies architectural insights and error patterns from work sessions into a persistent knowledge base.

소개

The Session Learnings skill automates the process of institutionalizing developer knowledge by scanning session artifacts, planning files, and terminal outputs for critical discoveries. It intelligently identifies anti-patterns, root causes, and successful resolutions, scoring them to determine if they warrant a permanent 'reflection' document. By transforming transient troubleshooting sessions into structured documentation (REFL-XXX), this skill prevents the recurrence of bugs and improves long-term development efficiency through automated pattern recognition.

주요 기능

Historical pattern tracking across multi-session activity windows
Conversation pattern analysis to identify user corrections and repeated failures
Automated scanning of planning-with-files outputs and findings.md
Intelligent learning candidate scoring based on impact and repetition
Automatic generation of REFL-XXX reflection documents
0 GitHub stars

사용 사례

Post-mortem analysis of complex bug fixes to prevent regression
Automated documentation of architectural decisions during active development
Capturing environment-specific configuration patterns for team onboarding

소개

주요 기능

Historical pattern tracking across multi-session activity windows
Conversation pattern analysis to identify user corrections and repeated failures
Automated scanning of planning-with-files outputs and findings.md
Intelligent learning candidate scoring based on impact and repetition
Automatic generation of REFL-XXX reflection documents
0 GitHub stars

사용 사례

Post-mortem analysis of complex bug fixes to prevent regression
Automated documentation of architectural decisions during active development
Capturing environment-specific configuration patterns for team onboarding