nap

Compress, prune, and archive agent context to reclaim memory.

3|3|Updated Apr 13, 2026
One-click install
npx skills add https://github.com/quaid-app/quaid --skill nap-quaid-app
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nap
Source: https://github.com/quaid-app/quaid/tree/main/.copilot/skills/nap
Command: npx skills add https://github.com/quaid-app/quaid --skill nap-quaid-app

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reclaims context window budget by compressing agent histories, pruning old logs, archiving stale decisions, and cleaning orphaned inbox files.

Core Features & Use Cases

  • Context hygiene: compress histories and prune inbox artifacts to maintain memory efficiency.
  • Archive stale items: move old decisions and inbox files to secondary storage to reduce active state.
  • Use Case: Run before heavy fan-out tasks or after long-running sessions to keep memory usage in check.

Quick Start

Run squad nap to compress histories and prune inbox artifacts to reclaim memory.

Frequently Asked Questions about nap

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

FAQPage Schema
How do I reclaim context window memory for agents with large histories?

Run context compression to compress agent histories and prune old logs. This reduces active memory usage during long-running campaigns or sessions with many agents using deterministic in-process cleanup operations.

What is context pruning and when should I archive agent state?

Context pruning compresses agent histories and archives stale decisions to reclaim memory. Run it before heavy fan-out tasks or after long-running sessions to maintain memory efficiency and reduce active state.

How do I clean up orphaned inbox files and stale agent decisions?

Clean up orphaned inbox files and stale decisions by archiving them to secondary storage. This reduces active state and reclaims memory budget using local, in-process threshold-based cleanup operations.

Does context archiving work for long-running multi-agent sessions?

Context archiving works for long-running multi-agent sessions by applying threshold-based cleanup to compress histories and move old decisions to secondary storage. Safe fallbacks ensure reliable in-process memory reclamation.

When should I not use deterministic context hygiene for memory reclamation?

Avoid deterministic context hygiene when you need to preserve full uncompressed agent histories or real-time access to every archived decision. The pruning process moves stale items to secondary storage, reducing active state availability.