disk-cleanup

name

disk-cleanup

description

Scan and clean macOS caches, package-manager data, crash dumps, and app caches to reclaim disk space. Deterministic — a config registry (targets.json) plus two scripts (survey.py read-only, clean.py executor) do all the measuring and deleting; the agent only relays a compressed summary and makes the few human-judgment calls. IMPORTANT — use this skill whenever the user's request on macOS involves: freeing disk space, cleaning/clearing caches, "disk is full", "clean up my Mac", "free up space", "what's eating my disk", "running low on disk", needing space for an install, or any low-storage complaint. Covers the whole workflow survey → choose → clean → empty Trash.

Disk Cleanup

Deterministic by design. All target knowledge lives in targets.json; all measuring and deleting lives in scripts/survey.py (read-only) and scripts/clean.py (executor, dry-run by default). They run headless with zero dependencies (stdlib only) — a user can run them in a terminal without any agent. The agent's job is small: run the scripts, relay the compressed output, and decide the handful of things that need human judgment.

The two scripts

python3 scripts/survey.py            # read-only: sizes, risk, flags, uncategorized. Touches nothing.
python3 scripts/survey.py --json     # same, machine-readable (preferred for the agent)

python3 scripts/clean.py --preset safe              # DRY-RUN plan (default — nothing deleted)
python3 scripts/clean.py --preset safe --go         # execute (safe risk only)
python3 scripts/clean.py --preset full --allow-medium --go --empty-trash   # safe+medium, then empty Trash
python3 scripts/clean.py --ids cargo-registry-cache,go-mod-cache --go       # specific targets
python3 scripts/clean.py --preset safe --skip ollama-models --go            # exclude one

trash is used for all file removal (never rm); freed space sits in Trash until emptied (--empty-trash, or the user empties it). Sizes are du estimates — approximate on APFS.

Safety model (enforced in code, not prose)

• Risk gating: safe runs automatically; medium needs --allow-medium; never is refused even if named by id. advisory targets only print guidance, never execute.
• Preflight on every trashed path: canonical realpath → must resolve under an allowed_roots entry → must not be a symlink → never $HOME or /. Anything failing is skipped and reported, not deleted.
• Dry-run by default: clean.py prints the plan and touches nothing unless --go.

Agent workflow

1. Run python3 scripts/survey.py --json. Relay the compressed summary: disk free, safe/medium recoverable totals, any flags (e.g. crash-loop), and the top targets. Do not dump the whole JSON.
2. Auto-path: for a plain "clean up safe stuff", show the safe total and run clean.py --preset safe --go (offer --empty-trash). Safe targets are regenerable.
3. Escalate to the user ONLY for (these are genuine judgment calls the scripts deliberately refuse to auto-decide):
   • medium targets (ML models, device support, project node_modules) — confirm before --allow-medium. ML-model targets (ollama-models, huggingface-models) carry a last_used_days field (newest file atime under the target, aggregate across all models in that store — not per-model) as a "how stale is this" signal; surface it before suggesting deletion.
   • uncategorized discoveries — unknown dirs >100 MB; ask or investigate before adding.
   • advisory notes — surface them (Telegram cache, simulators via simctl, uv/tools, Chrome whole-dir, Xcode Archives, mo clean deep-clean); never act on them automatically. For mole-deep-clean: suggest the user run mo clean themselves (interactive TUI, permanent deletes, sudo for system caches) — never invoke it from the agent. Never shell out to mo at all (not even --dry-run): it's TUI-only and blocks waiting for a real terminal even in dry-run mode — confirmed hanging under a piped subprocess, stdin=DEVNULL, and even a script(1)-allocated pty. A mole flag in survey.py's output only reads the mtime of mole's own leftover ~/.config/mole/clean-list.txt (last-run recency), never invokes it.
   • surgical Docker / simulator decisions (see below).
4. Run clean.py with the resolved selection. Relay the result (freed_human, disk before→after).

Maintaining the registry

Add or correct targets by editing targets.json — no code change needed. Each target: {id, category, risk, method, paths|find, regenerates, priority, note}. Methods:

• trash — trash literal paths (globs allowed).
• find-trash — exact-name dir sweep with a min_mb floor (crash dumps, project node_modules).
• command — run a CLI (npm cache clean…); set scope_path so freed bytes can be measured.
• simctl — xcrun simctl delete unavailable (removes only sims for uninstalled runtimes; safe).
• downloads-scan — config-driven (config.json → downloads_scan): files older than age_days whose name doesn't match exclude_patterns. The dry-run lists every file by name for review.
• advisory — never executes; only prints guidance.

Keep installed software at risk: never (learned the hard way: uv/tools, uv/python, ~/.rustup/toolchains, ~/.bun, ~/.deno are NOT caches). Every non-advisory target's paths must resolve under allowed_roots or preflight will (correctly) refuse them.

Customization & setup (per-machine, never committed)

config.json ships generic, public-safe defaults. Anything personal — names, family names, a non-English tax/legal/financial vocabulary — or machine-specific goes in config.local.json (gitignored). load_config() deep-merges it over config.json: lists are unioned (local terms only add protection to the Downloads exclude list), scalars override. See config.local.example.json for the shape.

Setup mode — when the user first uses the skill, asks to personalize it, or has sensitive files in ~/Downloads, offer to build config.local.json by asking (one short batch):

1. Names/keywords in Downloads filenames that must never be swept (own name, family names).
2. Their language's tax/legal/financial terms (e.g. German steuer, rechnung, vertrag).
3. Their projects directory (for the node_modules sweep) and any extra app caches. Then write config.local.json (copy config.local.example.json and fill it in). Confirm what was saved. Never commit it.

Per-machine paths in targets.json (allowed_roots, the node-modules-projects find root ~/ai_projects) are examples — adjust them to the user's layout. Targets whose paths don't exist on this machine simply measure 0 and are skipped.

Still agent-driven (only what genuinely can't be deterministic)

• Docker only — surgical and stateful: survey with docker images / docker ps -as / docker system df -v, let the user pick per-name (docker rm/rmi/volume rm/builder prune), or blunt docker system prune -a -f. A named volume removed = data gone; confirm by name. (Everything else — simulators via the simctl method, Downloads via downloads-scan, crash dumps, all caches — now runs through the scripts.)