compaction

Summarize and prune context to manage token limits in long AI sessions.

1|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/Kuass/kiro-gateway-plus --skill compaction
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: compaction
Source: https://github.com/Kuass/kiro-gateway-plus/tree/main/.opencode/skill/compaction
Command: npx skills add https://github.com/Kuass/kiro-gateway-plus --skill compaction

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of context window limitations in long-running AI sessions by providing strategies to manage and reduce accumulated context, ensuring continued effective operation.

Core Features & Use Cases

  • Context Budget Awareness: Monitors token usage and provides clear thresholds for action.
  • Compaction Strategies: Offers multiple methods like proactive distillation, phase compression, and noise pruning.
  • Session Handoff: Provides a protocol for seamlessly transferring work to a new session when context limits are reached.
  • Use Case: In a complex coding project spanning multiple days, this Skill helps maintain the AI's focus and efficiency by summarizing completed research phases and pruning irrelevant tool outputs, preventing the context from becoming unmanageable.

Quick Start

Use the compaction skill to distill the tool outputs that you are done with.

Frequently Asked Questions about compaction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage context window limitations in long-running AI sessions?

To manage context window limitations, apply proactive summarization, distillation, and noise pruning to preserve critical information while reducing accumulated tokens in long-running AI sessions.

What is the best way to maintain session continuity when reaching token limits?

The best way to maintain session continuity is using a session handoff protocol that seamlessly transfers distilled context and work progress to a new AI session before limits are reached.

How do I summarize completed research phases in complex coding projects?

To summarize completed research phases in coding projects, apply phase compression and custom summarization patterns to distill tool outputs and prune irrelevant noise from the AI context.

Does this compaction approach work for debugging and code exploration phases?

Yes, compaction works for debugging and code exploration by offering custom summarization patterns specifically designed to distill context and maintain AI efficiency during these implementation phases.

When should I use proactive distillation over noise pruning for context management?

Use proactive distillation when you need to compress entire phases of work, and use noise pruning when you specifically need to remove irrelevant tool outputs to free up context budget.

How do I monitor token usage thresholds to trigger AI context compaction?

You can monitor token usage thresholds through context budget awareness features that track accumulated tokens and provide clear action thresholds to trigger compaction strategies automatically.