Skill: nap

> Context hygiene — compress, prune, archive .squad/ state

6 stars

Best use case

Skill: nap is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

> Context hygiene — compress, prune, archive .squad/ state

Teams using Skill: nap 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/nap/SKILL.md --create-dirs "https://raw.githubusercontent.com/cwoodruff/morespeakers-com/main/.copilot/skills/nap/SKILL.md"

Manual Installation

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

How Skill: nap Compares

Feature / AgentSkill: napStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

> Context hygiene — compress, prune, archive .squad/ state

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

# Skill: nap

> Context hygiene — compress, prune, archive .squad/ state

## What It Does

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

## When To Use

- Before heavy fan-out work (many agents will spawn)
- When history.md files exceed 15KB
- When .squad/ total size exceeds 1MB
- After long-running sessions or sprints

## Invocation

- CLI: `squad nap` / `squad nap --deep` / `squad nap --dry-run`
- REPL: `/nap` / `/nap --dry-run` / `/nap --deep`

## Confidence

medium — Confirmed by team vote (4-1) and initial implementation