Agent skill

Disk Cleaner

by majiayu000 in majiayu000/spellbook

当用户要扫描磁盘空间、找出可安全删除的缓存/编译产物/安装包、或交互式释放空间时使用. An agent skill from majiayu000/spellbook.

MITAuto-check: notesDevelopment

Install Disk Cleaner

skills CLI
$ npx skills add majiayu000/spellbook --skill disk-cleaner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/spellbook disk-cleaner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/spellbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/disk-cleaner .claude/skills/disk-cleaner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
disk-cleaner
GitHub stars
287
Token cost
~1.5k tokens
SKILL.md length
124 words
Files
1
Skills in repo
97
Repo updated
First seen
Licence
MIT

At a glance

当用户要扫描磁盘空间、找出可安全删除的缓存/编译产物/安装包、或交互式释放空间时使用. An agent skill from majiayu000/spellbook.

  • Works in 11 steps: 磁盘概况 + 主目录一级 → 隐藏目录占用 → Rust target 编译缓存(基于扫描根目录,用 -prune 避免递归进入) → …
  • Development work in your project
  • SKILL.md covers 扫描流程, 安全规则 and 注意事项
  • Calls docker, cargo and pnpm

What it does

Disk Cleaner is an agent skill from majiayu000/spellbook. 当用户要扫描磁盘空间、找出可安全删除的缓存/编译产物/安装包、或交互式释放空间时使用。

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with Git. The repository describes itself as: Cross-runtime skills for Claude Code, Codex, and multi-agent workflows. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/disk-cleaner”

Requirements

  • Docker
  • Pre-approved tools (allowed-tools): Bash

Workflow steps

11 steps, taken from the first numbered list in SKILL.md.

  1. 磁盘概况 + 主目录一级
  2. 隐藏目录占用
  3. Rust target 编译缓存(基于扫描根目录,用 -prune 避免递归进入)
  4. node_modules 依赖(基于扫描根目录)
  5. .next 构建缓存(基于扫描根目录)
  6. 包管理器缓存(uv/bun/gradle/npm/rod/pre-commit/huggingface/puppeteer/pnpm-store)
  7. Library/Caches 大户
  8. Application Support 大户
  9. Downloads 安装包 + 废纸篓
  10. 大的 .git 目录(基于扫描根目录,仅供参考)
  11. Docker 占用

What it can do on your machine

Read from SKILL.md and the folder at commit ed52af7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • docker
    • cargo
    • pnpm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use docker and pnpm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Disk Cleaner loads about 1.5k tokens when it runs. Until then it costs about 14 tokens; SKILL.md has 124 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~14
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/spellbook at commit ed52af7, republished under its MIT licence (© majiayu000). 124 words, ~1,473 tokens.

Download SKILL.mdSave it as .claude/skills/disk-cleaner/SKILL.md (or your agent's skills folder).
name
disk-cleaner
description
当用户要扫描磁盘空间、找出可安全删除的缓存/编译产物/安装包、或交互式释放空间时使用。
allowed-tools
Bash
metadata.argument-hint
[扫描路径,默认 ~]

磁盘空间清理工具

你是一个磁盘空间管理专家,帮助用户找出可以安全删除的文件和目录,释放磁盘空间。

用户传入的参数(如有):$ARGUMENTS

将 $ARGUMENTS 视为用户指定的扫描范围,不要忽略。用户没有传入参数时,不要假设代码一定在某个固定目录;先从当前工作目录和用户主目录做有边界的探索,找出真实存在的项目根目录,再基于这些目录扫描。

扫描流程

第一步:解析扫描范围

先确定本次扫描根目录,后续所有代码相关扫描都必须基于这些根目录。

规则:

  • 如果用户传入路径参数,逐个解析为绝对路径;只扫描这些路径及其子目录。
  • 如果用户没有传入参数,以当前工作目录和用户主目录为起点做探索。
  • 不要硬编码 ~/Desktop/code、~/Developer、~/Projects 等目录;只有探索结果中真实出现的目录才可作为扫描根目录。
  • 代码根目录通过项目标记发现,例如 .git、Cargo.toml、package.json、pyproject.toml、go.mod、pnpm-workspace.yaml、bun.lockb。
  • 探索时跳过明显不该递归的大目录:Library、.Trash、node_modules、target、.git、应用数据缓存目录。
  • 输出去重后的绝对路径列表,命名为“扫描根目录”,并在报告里展示。
  • 后续命令中先把扫描根目录写入 scan_roots=(...) 数组;不要原样执行模板里的占位路径。

可用的探索命令:

bash
pwd
printf '%s\n' "$HOME"

用户没有传入参数时,用下面的方式探索项目根目录:

bash
find "$HOME" -maxdepth 5 \
  \( -path "$HOME/Library" -o -path "$HOME/.Trash" -o -path "*/node_modules" -o -path "*/target" \) -prune -o \
  \( \( -name ".git" -type d -prune \) -o -name "Cargo.toml" -o -name "package.json" -o -name "pyproject.toml" -o -name "go.mod" -o -name "pnpm-workspace.yaml" -o -name "bun.lockb" \) -print 2>/dev/null \
| awk '{ if ($0 ~ /\/\.git$/) sub(/\/\.git$/, "", $0); else sub(/\/[^\/]+$/, "", $0); print }' \
| sort -u | head -80

如果探索结果过多,优先选择:

  • 当前工作目录所在项目
  • 占用明显较大的项目父目录
  • 最近用户提到或传入的目录
第二步:全量并行扫描

一次性并行执行以下所有扫描(每个一个 Bash 调用):

  1. 磁盘概况 + 主目录一级
bash
df -h / && echo "---" && du -d1 -h "$HOME" 2>/dev/null | sort -rh | head -30
  1. 隐藏目录占用
bash
du -sh ~/.[!.]* 2>/dev/null | sort -rh | head -20
  1. Rust target 编译缓存(基于扫描根目录,用 -prune 避免递归进入)
bash
# 将 /absolute/root1 /absolute/root2 替换为第一步解析出的扫描根目录
scan_roots=(/absolute/root1 /absolute/root2)
for root in "${scan_roots[@]}"; do
  find "$root" -maxdepth 5 -name "target" -type d -not -path "*/node_modules/*" -prune -exec du -sh {} \; 2>/dev/null
done | sort -rh
  1. node_modules 依赖(基于扫描根目录)
bash
# 将 /absolute/root1 /absolute/root2 替换为第一步解析出的扫描根目录
scan_roots=(/absolute/root1 /absolute/root2)
for root in "${scan_roots[@]}"; do
  find "$root" -maxdepth 5 -name "node_modules" -type d -prune -exec du -sh {} \; 2>/dev/null
done | sort -rh | head -15
  1. .next 构建缓存(基于扫描根目录)
bash
# 将 /absolute/root1 /absolute/root2 替换为第一步解析出的扫描根目录
scan_roots=(/absolute/root1 /absolute/root2)
for root in "${scan_roots[@]}"; do
  find "$root" -maxdepth 5 -name ".next" -type d -prune -exec du -sh {} \; 2>/dev/null
done | sort -rh
  1. 包管理器缓存(uv/bun/gradle/npm/rod/pre-commit/huggingface/puppeteer/pnpm-store)
bash
du -sh ~/.cache/uv ~/.cache/huggingface ~/.cache/pre-commit ~/.cache/puppeteer ~/.cache/rod ~/.npm/_cacache ~/.pnpm-store ~/.bun ~/.gradle 2>/dev/null | sort -rh
  1. Library/Caches 大户
bash
du -d1 -h ~/Library/Caches 2>/dev/null | sort -rh | head -15
  1. Application Support 大户
bash
du -d1 -h ~/Library/Application\ Support/ 2>/dev/null | sort -rh | head -10
  1. Downloads 安装包 + 废纸篓
bash
du -sh ~/.Trash/ 2>/dev/null; echo "---"; find ~/Downloads -maxdepth 1 \( -name "*.dmg" -o -name "*.pkg" -o -name "*.app" -o -name "*.zip" \) -exec ls -lhS {} \; 2>/dev/null
  1. 大的 .git 目录(基于扫描根目录,仅供参考)
bash
# 将 /absolute/root1 /absolute/root2 替换为第一步解析出的扫描根目录
scan_roots=(/absolute/root1 /absolute/root2)
for root in "${scan_roots[@]}"; do
  find "$root" -maxdepth 4 -name ".git" -type d -prune -exec du -sh {} \; 2>/dev/null
done | sort -rh | head -10
  1. Docker 占用
bash
docker system df 2>/dev/null || true
第三步:生成清理报告 + 编号菜单

汇总所有扫描结果,按以下格式输出:

## 磁盘概况
总容量: XXX | 已用: XXX | 可用: XXX

## 扫描根目录
- /absolute/root1
- /absolute/root2

## 可清理项目(按释放空间排序)

### 高价值(可安全删除,重新构建/下载即可恢复)
| # | 类别 | 大小 | 说明 |
|---|------|------|------|
| 1 | Rust target 编译缓存 | XXG | cargo build 恢复 |
| 2 | 包管理器缓存 | XXG | 按需自动重新下载 |
| 3 | Library/Caches | XXG | playwright/go-build/VSCode 更新等 |
| ... | ... | ... | ... |

### 中等价值(按需清理)
| # | 类别 | 大小 | 说明 |
|---|------|------|------|
| 5 | node_modules(不活跃项目) | XXG | bun/pnpm install 恢复 |
| 6 | Downloads 安装包 | XXXM | 已安装的 .dmg/.pkg 可删 |
| ... | ... | ... | ... |

### 仅供参考(不建议删除)
| 类别 | 大小 | 说明 |
|------|------|------|
| .git 大仓库 | XXG | 删除即丢失历史 |
| .rustup | XXG | 工具链,删除需重装 |
| ... | ... | ... |

## 预计可释放: XXG

---
选择要清理的编号(如 1,2,3 或 "全部"):
第四步:执行清理

用户选择编号后,按类别并行执行删除。

关键:删除命令必须使用绝对路径(/Users/xxx/...),不要用 ~ 或 $HOME。

缓存目录删除必须精确到子目录(避免 hook 拦截顶层隐藏目录):

bash
# ✅ 正确 — 精确子目录
rm -rf /Users/xxx/.cache/uv/cache /Users/xxx/.cache/uv/sdists-v9
rm -rf /Users/xxx/.gradle/caches /Users/xxx/.gradle/wrapper /Users/xxx/.gradle/daemon
rm -rf /Users/xxx/.npm/_cacache
rm -rf /Users/xxx/.bun/install/cache
rm -rf /Users/xxx/.cache/pre-commit
rm -rf /Users/xxx/.cache/rod/browser

# ❌ 错误 — 会被 hook 拦截
rm -rf ~/.cache/uv ~/.gradle ~/.bun

Library/Caches 常见可清理项(按扫描结果选择性清理):

bash
rm -rf /Users/xxx/Library/Caches/ms-playwright
rm -rf /Users/xxx/Library/Caches/go-build
rm -rf /Users/xxx/Library/Caches/com.microsoft.VSCode.ShipIt
rm -rf /Users/xxx/Library/Caches/camoufox
rm -rf /Users/xxx/Library/Caches/notion.id.ShipIt
rm -rf /Users/xxx/Library/Caches/pnpm

node_modules 清理:列出所有 node_modules 路径,一条 rm -rf 命令删除。

Docker 清理(如用户选择):

bash
docker system prune -af --volumes
第五步:验证
bash
df -h /

输出清理前后对比表:

| 指标 | 清理前 | 清理后 |
|------|--------|--------|
| 可用空间 | XXG | XXG |
| 使用率 | XX% | XX% |

释放了约 XXG

安全规则

  • 绝不删除用户文档、照片、代码源文件
  • 绝不删除 .git 目录(只报告大小供参考)
  • 绝不删除当前工作目录下的 target/ 或 node_modules/
  • 只删除缓存、编译产物、安装包等可恢复的内容
  • 删除后运行 df -h / 报告释放了多少空间
  • 删除命令使用绝对路径,缓存目录精确到子目录级别
  • 代码相关扫描和删除只能使用第一步解析出的扫描根目录;不要临时编造常见代码目录

注意事项

  • 用中文输出所有信息
  • 扫描时最大化并行执行(所有扫描一步完成),减少等待时间
  • 如果遇到权限问题,先用 chmod -R u+w 尝试,不要用 sudo
  • du 对大目录可能很慢,给所有 Bash 调用设置 timeout: 120000

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/disk-cleaner of majiayu000/spellbook.

Open the folder on GitHubat commit ed52af7

Compare with similar skills

Disk Cleaner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Disk Cleaner compared with similar skills
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Disk Cleaner this skillmajiayu000/spellbook287—~1.5kAutomated safety check: NotesMIT
Finishing a Development Branchobra/superpowers297k5 repos~1.9kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k4 repos~1.1kAutomated safety check: PassMIT
Code Design Rationale Investigatorcursor/plugins11k9 repos~2.6kAutomated safety check: PassNone
Contributor-First PR MergeHKUDS/OpenHarness16k1 repos~847Automated safety check: PassMIT
Finishing A Development Branchfarm-fe/farm5.6k34 repos~1.8kAutomated safety check: PassMIT

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Works with

Categories

Questions about Disk Cleaner

What does Disk Cleaner do?

当用户要扫描磁盘空间、找出可安全删除的缓存/编译产物/安装包、或交互式释放空间时使用. An agent skill from majiayu000/spellbook. Disk Cleaner is an agent skill from majiayu000/spellbook.

When should I use Disk Cleaner?

Disk Cleaner fits situations like: development work in your project.

How do I install Disk Cleaner in Claude Code?

Run `npx skills add majiayu000/spellbook --skill disk-cleaner -a claude-code`. Or copy the skill folder (skills/disk-cleaner in majiayu000/spellbook) into .claude/skills/disk-cleaner in your project. Claude Code loads it when a task matches its description.

How do I install Disk Cleaner in Codex?

Run `npx skills add majiayu000/spellbook --skill disk-cleaner -a codex`. Or copy the skill folder (skills/disk-cleaner in majiayu000/spellbook) into .agents/skills/disk-cleaner in your project. Codex loads it when a task matches its description.

Can I use Disk Cleaner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/spellbook --skill disk-cleaner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/disk-cleaner, .gemini/skills/disk-cleaner, .github/skills/disk-cleaner and .opencode/skills/disk-cleaner in your project.

What does Disk Cleaner need to run?

Going by SKILL.md and its folder, Disk Cleaner needs the command-line tools its instructions call (docker, cargo and pnpm). Our summary lists: Docker. Its frontmatter pre-approves these tools: Bash.

Does Disk Cleaner access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Disk Cleaner safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Disk Cleaner use?

Disk Cleaner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Disk Cleaner use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Disk Cleaner?

Skills that share tags, products or a category with Disk Cleaner: Finishing a Development Branch (obra/superpowers, 297k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Code Design Rationale Investigator (cursor/plugins, 11k stars) and Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Disk Cleaner?

majiayu000 (a GitHub user) maintains it in majiayu000/spellbook, which has 287 GitHub stars. The repository holds 97 skills in this directory. The repository was last updated on October 8, 2026.

Source: majiayu000/spellbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.