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 IvanSkainet/arena-agent --skill cleanupgit clone --depth 1 https://github.com/IvanSkainet/arena-agentWrote 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/ivanskainet/arena-agent/cleanup)<a href="https://agentmods.dev/skills/ivanskainet/arena-agent/cleanup"><img src="https://agentmods.dev/badge/skills/ivanskainet/arena-agent/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.
<a href="https://agentmods.dev/skills/ivanskainet/arena-agent/cleanup"><img src="https://agentmods.dev/badge/skills/ivanskainet/arena-agent/cleanup.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 | $0.00000 | $0.00292 |
| Opus 5 | $0.00000 | $0.00146 |
| Sonnet 5 | $0.00000 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
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 10d 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.
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.
What it actually says
core/cleanup
Rotate / prune old data to keep the platform tidy.
Inputs (optional flags)
--days N— age threshold (default 30)--keep N— minimum items to keep per category (default 10)--dry-run— show what would be deleted, do nothing--apply— actually delete (default: dry-run)
Categories cleaned
~/arena-bridge/backups/— tarballs older than N days~/arena-bridge/memory/sessions/— JSONL sessions older than N days~/arena-bridge/reports/— generated digests/reports older than N days~/arena-bridge/queue/done/— completed tasks older than N days~/arena-bridge/queue/failed/— failed tasks older than N days~/arena-bridge/logs/— rotated log files (*.log.1,*.log.2,*.log.3,*.jsonl.1–*.jsonl.5)
Always keeps at least --keep newest items in each category, regardless of age.
Outputs
- Summary per category: kept / pruned / freed-bytes
- Exit 0 always (cleanup is best-effort)
What ships with it
3 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.
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.
- 10d ago First seen · 24 lines · 0 tokens per session scan A 6aa491b3919b
cleanup is a skill published in the GitHub repository IvanSkainet/arena-agent (6 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 292 tokens. 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 2026-08-31.
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