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.
npx skills add andalabx/skills --skill cleangit clone --depth 1 https://github.com/andalabx/skillsWrote 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.
[](https://agentmods.dev/skills/andalabx/skills/clean)<a href="https://agentmods.dev/skills/andalabx/skills/clean"><img src="https://agentmods.dev/badge/skills/andalabx/skills/clean/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.
<a href="https://agentmods.dev/skills/andalabx/skills/clean"><img src="https://agentmods.dev/badge/skills/andalabx/skills/clean.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.04005 |
| Opus 5.5 | $0.00031 | $0.01602 |
| Sonnet 5.5 | $0.00015 | $0.00801 |
| Haiku 4.5 | $0.00008 | $0.00400 |
Grade C, and why
clean scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$TMPDIR/claude/"* 2>/dev/null How it starts
The opening of the file, as written. The whole thing — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/clean — Dev Environment Cleanup
Frees disk space across your dev environment. Works on Windows, macOS, and Linux.
Arguments: $ARGUMENTS
- (none) — ask the user whether they want Quick or Super mode
quick— full standard cleanup, no questions askedsuper— interactive deep-clean: discovers everything, user approves before anything is deleted
No Argument — Ask First
If no argument is given, ask the user before doing anything:
How would you like to clean?
quick — fast cleanup of known junk (caches, temp, node_modules, builds)
no questions, runs immediately
super — deep scan of everything on your system, ranked by size
you decide what goes and what stays before anything is deleted
Type "quick" or "super":
Wait for their answer, then proceed to the matching mode below.
Quick Mode (/clean quick)
Rule: always measure size BEFORE deleting. Never delete first and report after — sizes will be wrong or unavailable.
Step 0 — Detect environment
Run these two commands and record the values for use in every step below:
uname -s 2>/dev/null || echo "Windows"
echo $HOME
Resolve OS-specific variables:
- Windows:
HOME=$USERPROFILE,TMPDIR=$LOCALAPPDATA/Temp,APPDATA,LOCALAPPDATA - macOS:
HOME=$HOME,TMPDIR=$TMPDIR - Linux:
HOME=$HOME,TMPDIR=/tmp
Project roots to scan (use whichever exist):
$HOME/Documents, $HOME/Projects, $HOME/code, $HOME/dev, current directory
Record baseline disk free now — use this exact command, once, and store the result:
df -h / 2>/dev/null || df -h "$HOME"
Step 1 — Claude temp dir
Measure size first using du, then delete contents:
# measure
du -sh "$TMPDIR/claude" 2>/dev/null || echo "0"
# delete contents only, not the directory itself
rm -rf "$TMPDIR/claude/"* 2>/dev/null
Use the du result as the reported size — do not try to read output after deletion, the file may be gone.
What ships with it
1 file 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.
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.
- 2mo ago First seen · 466 lines · 77 tokens per session scan C 7cb34cae4812
clean is a skill published in the GitHub repository andalabx/skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 4,005 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
adhd
Formats output for ADHD-friendly action — lead with the next step, number tasks, restate state, cut fluff. Use when named (adhd), when the user mentions ADHD or focus/executive-function constraints, or when they ask for steps they can execute immediately.
peon-ping-toggle
Toggle peon-ping sound notifications on/off. Use when user wants to mute, unmute, pause, or resume peon sounds during a Claude Code session. Also handles config changes like volume, pack rotation, categories — any peon-ping setting.
peon-ping-log
Log exercise reps for the Peon Trainer. Use when user says they did pushups, squats, or wants to log reps. Examples - "/peon-ping-log 25 pushups", "/peon-ping-log 30 squats", "log 50 pushups".
resume-builder
Build resume and LinkedIn profile through guided interview.
getting-started
Interactive post-onboarding tour that adapts to whatever data exists (calendar, Granola, or none). Use right after onboarding, or when the user says 'show me around', 'how do I start'. Also use proactively when the vault is < 7 days old. Not for the initial setup itself; use setup.
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps taskplan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and…