Overview

Implements conversation summarization to compress dialogue and manage context for memory systems. It supports rolling, hierarchical, token-aware strategies, and both extractive and abstractive summaries to keep essential details while staying within token limits.

How This Skill Works

The skill applies rolling summaries that update with new messages and uses hierarchical levels to organize context. It enforces a token budget, selects extractive key ideas, and can generate abstractive summaries via an LLM, guided by a configurable template and quality thresholds.

When to Use It

• During long-running conversations where the dialogue exceeds token limits.
• When updating memory with new messages via rolling summaries.
• When organizing summaries across multiple levels (hierarchical) for fast retrieval.
• When enforcing a token budget to fit within model limits.
• When evaluating and refining summary quality against thresholds.

Quick Start

1. Step 1: Configure the skill with your LLM, token budget, and update frequency.
2. Step 2: Choose a strategy (rolling, hierarchical, extractive/abstractive) and set a summary template.
3. Step 3: Run a test with long conversations and monitor summary quality, adjusting thresholds as needed.

Best Practices

• Balance detail vs compression to keep essential context.
• Preserve key information while removing redundancy.
• Monitor summary quality and adjust LLM parameters.
• Test with long conversations to validate performance.
• Handle context window limits by applying hierarchical or rolling summaries.

Example Use Cases

• A customer support chatbot that summarizes chats to fit a memory budget.
• A personal AI assistant that maintains long-term memory across sessions.
• A research assistant that condenses multi-hour interviews for later analysis.
• A meeting assistant that compresses agendas and decisions for quick recall.
• A tutoring AI that tracks student progress over weeks and summarizes sessions.

Frequently Asked Questions