Community Команды Продуктивность

Команда session-learning-capture

Claude Code

Установка

npx claude-code-templates@latest --command team/session-learning-capture

Описание

session-learning-capture — slash-команда сохранения опыта: фиксация — документирование выводов сессии, интеграция знаний — автоматическое внесение в базу, обновление памяти — перенос в долгосрочное хранилище. Для команд, желающих, чтобы каждая сессия делала систему умнее.

Что умеет:
— документировать выводы сессии;
— интегрировать знания автоматически;
— обновлять память системы;
— запускаться одной командой.

Session Learning Capture

Capture and integrate session learnings into project memory and knowledge base: $ARGUMENTS

Current Learning Context

Session duration: Current Claude Code session learning opportunities
Memory files: !find . -name "CLAUDE*.md" | wc -l available memory files for knowledge integration
Project complexity: Assessment of project structure and documentation completeness
Learning patterns: Identification of knowledge gaps and correction opportunities

Task

Execute comprehensive learning capture with automatic knowledge integration:

Capture Type: Use $ARGUMENTS to focus on project learnings, implementation corrections, structure insights, or workflow improvements

Learning Capture Framework:

Learning Identification – Detect new project knowledge, identify implementation corrections, recognize structural insights, note workflow discoveries
Knowledge Classification – Categorize learning type, assess importance level, determine integration location, evaluate reusability potential
Context Analysis – Analyze session context, identify triggering conditions, assess knowledge applicability, determine documentation needs
Integration Planning – Select appropriate memory files, determine update strategy, maintain consistency, preserve existing knowledge
Memory Updates – Update CLAUDE.md files, enhance documentation, improve workflows, strengthen knowledge base
Validation Process – Verify accuracy of captured knowledge, ensure integration quality, validate accessibility, confirm usefulness

Advanced Features: Automated learning detection, intelligent categorization, context-aware integration, knowledge graph enhancement, version control integration.

Quality Assurance: Learning accuracy validation, integration consistency, accessibility optimization, knowledge retrieval efficiency.

Output: Comprehensive learning integration with updated memory files, enhanced documentation, improved workflows, and validated knowledge base.