agentleFS
MasihMoafiMasihMoafi/skills/clean-system/SKILL.md

clean-system

Skill8 starsChanged 9 days agoMITView on GitHub

Reclaim disk space or clear the clutter Claude Code, Codex and Elpis leave (old chat logs, file history, logs, backups), or name or remove single sessions. Agent clutter goes without asking; caches and Docker need a yes.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 1 is distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run python3 clean.py from this folder (dry run), show the per-category totals, then run it with --apply.

    line 10
  2. Very specific:

    One-off targets: add them with --also PATH ....

    line 12
  3. Very specific:

    If an auto-mode classifier blocks --apply, don't work around it.

    line 18
  4. Very specific:

    Docker: run docker system df. Images, containers and build cache are separate approvals.

    line 29
  5. Distinctive:

    Never target a home directory, workspace root, unresolved variable or broad glob.

    line 30

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

System clean-up

1. Agent clutter: clear it without asking (rule 27)

Run python3 clean.py from this folder (dry run), show the per-category totals, then run it with --apply.

  • What it clears: old Claude, Codex and Elpis chat logs, Claude file history and scratch, Codex and Elpis logs, Codex per-run configs, and backups (*.bak, *.pre-*, *.before-*, *backup*) in the agent config folders.
  • One-off targets: add them with --also PATH ....
  • What it always spares:
    • anything touched in the last 24 hours, or open in a running process;
    • memory folders, credentials, live config.toml and settings.json, databases, prompt history;
    • skills, plugins, jobs, plans, ES.md.
  • Everything goes to the trash (gio trash).
  • If an auto-mode classifier blocks --apply, don't work around it. Give the user the exact ! python3 … --apply command to run.

2. Everything else: audit, then ask

Package caches and Docker hold other projects' state, so list the targets with sizes and get the user's yes per category.

  • Caches:
    • uv: du -sh ~/.cache/uv, cleaned by uv cache clean.
    • pip: ~/.cache/pip, cleaned by pip cache purge.
    • conda: ~/miniconda3/pkgs, cleaned by conda clean --all -y.
    • npm: ~/.npm/_cacache, cleaned by npm cache clean --force.
    • pnpm: pnpm store prune. Never delete the store folder.
  • Docker: run docker system df. Images, containers and build cache are separate approvals. Volumes need their own explicit yes because they may hold irreplaceable data.
  • Never target a home directory, workspace root, unresolved variable or broad glob.

3. Verify

Rerun the dry run and df -h /. Report what moved and how much space was freed. Note that it all sits in the trash until the user empties it.

4. Single sessions: name or remove one at a time

For a recent session the user wants named or judged individually, read only its product's own history root (Claude Code ~/.claude/projects/, Codex ~/.codex/sessions/, Elpis ~/.elpis-next/sessions/), list id, short summary, last activity and message count in one table, and propose a 3 to 6 word name from the first meaningful user message. Rename or remove only through the product's own command, never by editing raw logs or indexes, and never the current session or one with a live writer.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.