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 mirkobozzetto/arsenal --skill napgit clone --depth 1 https://github.com/mirkobozzetto/arsenalWrote 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/mirkobozzetto/arsenal/nap)<a href="https://agentmods.dev/skills/mirkobozzetto/arsenal/nap"><img src="https://agentmods.dev/badge/skills/mirkobozzetto/arsenal/nap/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/mirkobozzetto/arsenal/nap"><img src="https://agentmods.dev/badge/skills/mirkobozzetto/arsenal/nap.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 | $0.00034 | $0.00323 |
| Opus 5.5 | $0.00014 | $0.00129 |
| Sonnet 5.5 | $0.00007 | $0.00065 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
nap 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 3d 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
Nap
The session saves where it stands, empties its context and wakes up fresh on
the same work. Ask nothing at any step. Resolve scripts/wake.cjs from this
skill's directory.
- Save with the trace skill (
trace:trace), save mode, without its questions and without its closing lines. Text given after/napis the--nextfield, word for word. Without it, write the next step yourself. - Run
node scripts/wake.cjs --detect(a normal Bash call). - On
none: end with « Tu peux /clear. » in the conversation language, then/next:nextas the last line. Stop. - Otherwise, as the last tool call of the turn, run
node scripts/wake.cjswith Bashrun_in_background: true: only a background Bash sees the session's own terminal. Then end the turn with one line, in the conversation language: « Sieste : je reprends ensuite . »
Do nothing after that line. Once the turn ends, the wake-up types /clear,
runs next and answers « oui » to its single resume question, in any
language. A list from next waits for the user.
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.
- 3d ago First seen · 27 lines · 34 tokens per session scan A 525059fb2e83
nap is a skill published in the GitHub repository mirkobozzetto/arsenal (24 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 323 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-11-02.
Other skills, from other repositories
context-bankruptcy
Declare bankruptcy on a long-lived AI agent's accumulated memory — audit what it currently believes, separate ground truth from stale and wrong, purge deliberately, restate the truths that survive, and log what was lost. Use when an agent keeps acting on outdated facts, contradicts itself across sessions, 'remembers'…
run-memory
Shared step of the marketing playbooks (analyze-performance, run-paid-campaigns, research-competitors, email-lifecycle): reads the last run's record, checks whether its recommendations were done and whether they worked, and saves a short record of this run for the next one. Loaded by those playbooks; not for direct…
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain — personal notes go there, not here. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/)…
second-brain
When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Vault defaults to…
give-me-tips
Explains any senpi tip in depth, including Tip: lines in the TUI. Use when the user asks about a tip, what a tipped feature does, or which tips they can see.
context-engineering-review
Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and…