memory-summarization

Conversation summarization for memory compression and context management

509 stars

Best use case

memory-summarization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Conversation summarization for memory compression and context management

Teams using memory-summarization should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/memory-summarization/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/ai-agents-conversational/skills/memory-summarization/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/memory-summarization/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How memory-summarization Compares

Feature / Agentmemory-summarizationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Conversation summarization for memory compression and context management

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

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