memory-summarization
Conversation summarization for memory compression and context management
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/memory-summarization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How memory-summarization Compares
| Feature / Agent | memory-summarization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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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