/memory-summarization

name: memory-summarization
description: Conversation summarization for memory compression and context management
allowed-tools:
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graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:context-management, skill-area:llm-summarization-qa]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:feature-development, workflow:ml-model-lifecycle]

Memory Summarization Skill

Capabilities

• Implement conversation summarization strategies
• Configure rolling summary updates
• Design hierarchical summarization
• Implement token-aware summarization
• Create extractive and abstractive summaries
• Design summary quality evaluation

Target Processes

• conversational-memory-system
• long-term-memory-management

Implementation Details

Summarization Strategies

1. **Rolling Summary**: Update summary with new messages 2. **Hierarchical**: Multi-level summarization 3. **Token-Budget**: Fit within token limits 4. **Extractive**: Key message selection 5. **Abstractive**: LLM-generated summaries

Configuration Options

• LLM for summarization
• Summary token budget
• Update frequency
• Summary template
• Quality thresholds

Best Practices

• Balance detail vs compression
• Preserve key information
• Monitor summary quality
• Test with long conversations
• Handle context window limits

Dependencies

• langchain-core
• LLM provider