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memory-cleanup

memory-cleanup is a skill for Claude Code from serejaris/personal-corp-os. It costs 77 tokens per session (1,269 once invoked), scanned A, original, MIT.

A cleanup procedure for finding and stopping leftover processes that consume computer memory. It focuses on idle desktop sessions, workers, and background processes left behind after applications close.

In plain words
What is it for?
It helps investigate a slow Mac, rising swap use, zombie processes, and memory held by closed apps or idle coding-agent sessions.
Why use it?
It helps distinguish real memory pressure from normal macOS caching and compressed memory. The process list is checked before anything is stopped, so active work is less likely to be interrupted.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code; mentions Codex.

Part of the personal-corp-os plugin — 32 skills shipped together

not rated 230repo +1 today A scan 77 tokens original MIT

Good fit It helps investigate a slow Mac, rising swap use, zombie processes, and memory held by closed apps or idle coding-agent sessions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/serejaris/personal-corp-os/memory-cleanup
View source ↗ serejaris/personal-corp-os
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add serejaris/personal-corp-os --skill memory-cleanup
Clone the repo
git clone --depth 1 https://github.com/serejaris/personal-corp-os

Made for: Claude Code.

Or install personal-corp-os, the plugin that ships this one along with the rest of its 32 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for memory-cleanup

README.md
[![agentmods](https://agentmods.dev/badge/skills/serejaris/personal-corp-os/memory-cleanup/github.svg)](https://agentmods.dev/skills/serejaris/personal-corp-os/memory-cleanup)
Your own site
<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/memory-cleanup"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/memory-cleanup/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for memory-cleanup

Your own site · 80×15
<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/memory-cleanup"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/memory-cleanup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,269 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.01269
Opus 5.5 $0.00031 $0.00508
Sonnet 5.5 $0.00015 $0.00254
Haiku 4.5 $0.00008 $0.00127

Measured yesterday against content hash e027e511c446, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

memory-cleanup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/tails-linux.sh, scripts/tails-macos.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/memory-cleanup/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

memory-cleanup

An active skill: it kills processes, so the snapshot and the kill are separate steps. The snapshot script only reads.

Typical picture on a 16 GB Mac: a few gigabytes held by Claude Code desktop sessions idle for days (30–400 MB each, dozens of them), a daemon of a closed app still running its workers, duplicate worker brokers, a Chrome for Testing left by puppeteer. A background npm run dev started inside a session dies together with that session.

When memory is really short

"13 of 16 GB used" and the Swap Used size mean nothing by themselves: macOS keeps memory busy with cache and compression, and swap does not shrink after memory is freed until a reboot. Shortage shows by three signs:

  • pressure is yellow or red: sysctl kern.memorystatus_vm_pressure_level above 1, memory_pressure shows less than 25–30% free;
  • Swapouts in vm_stat grow before your eyes (two readings a minute apart);
  • swap grows three readings in a row.

Measure a process by top -o mem or footprint: ps (RSS) does not see the compressed part and understates it. Look at the top of top -o mem before cleaning: one live job (a local whisper transcription, a model load) can hold more than all the tails together, and the script does not list children of active sessions.

Procedure

  1. Snapshot. From the skill root:

    bash scripts/tails-macos.sh                                                     # local
ssh -o BatchMode=yes -o ConnectTimeout=8 <alias> 'bash -s' < scripts/tails-<os>.sh   # remote

    The script reads only, never calls sudo, kills nothing. It prints a table of candidates and a summary: total RSS, swap, memory pressure. Keep the summary: it is the "before" for step 5. It skips launchd gui-domain services (on Linux, processes in .service units), system processes, Claude.app, the current shell and its ancestors; the current claude session is named on a separate line. It prints no process environment, only session ids. Output is in Russian; the class labels are:

Read the full file on GitHub · 65 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 65 lines · 77 tokens per session scan A e027e511c446

Subscribe to this mod's changes

memory-cleanup is a skill published in the GitHub repository serejaris/personal-corp-os (230 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 1,269 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-19.

Claude CodeDebuggingProductivityagentic-workflowsai-agentsclaude-code-skillsopen-sourceskills
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