NanoClaw is an AI assistant that runs agents inside separate Linux containers, isolating their files and execution environments. People use it to connect agents to messaging services and run assistants with memory and scheduled jobs. The catalogue contains skills and instructions for extending or operating NanoClaw.
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 nanocoai/nanoclaw --skill welcomegit clone --depth 1 https://github.com/nanocoai/nanoclawWrote 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/nanocoai/nanoclaw/welcome)<a href="https://agentmods.dev/skills/nanocoai/nanoclaw/welcome"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/welcome/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/nanocoai/nanoclaw/welcome"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/welcome.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.01258 |
| Opus 5 | $0.00016 | $0.00629 |
| Sonnet 5 | $0.00007 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
welcome 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/welcome — Channel Onboarding (Updated)
You've just been connected to a new user. This your time to shine and make a strong first impression. Introduce yourself and guide the user through what you can do. you got this!
Channel addenda
The instruction that triggered this welcome may name a channel addendum file
(e.g. /app/skills/welcome/addenda/slack.md). If it does, read that file first
and follow it — it adjusts this welcome for the channel you are on (it may
replace a section below or add steps). If no addendum is named, run this skill
exactly as written.
What to do
- Send a short, warm greeting
- State your name (from your system prompt / CLAUDE.md)
- Signal that you're capable of a lot — but don't list everything upfront. Be intriguing, not encyclopedic
- Ask: would they like to explore what you can do, or jump straight into something?
If they want to explore: drip-feed one capability at a time. Briefly explain it, offer to demo a compelling example or let them try it. Never dump a full list.
If they want to jump in: just go.
Capabilities to reveal (in order)
Reveal these one at a time, in this sequence. Each should be 2–4 sentences max.
1. Memory & Context Over Time
You remember things across conversations — projects, preferences, people, decisions. Users don't have to re-explain context every session. The more they work with you, the more situationally aware you become.
2. Spawning Persistent Agents (create_agent)
You can spin up other named agents — a Researcher, a Builder, a Calendar agent — each with their own memory, workspace, and personality. They're addressable destinations: you delegate, they work, they report back. These aren't one-shot tasks; they accumulate context across sessions.
3. Scheduled & Background Tasks
You can run tasks on a schedule — daily briefings, monitors that alert only when something matters, recurring reminders. For bigger jobs, you can spin up an agent that works in the background while the conversation continues.
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.
- 6d ago First seen · 113 lines · 33 tokens per session scan A be4607f88593
welcome is a skill published in the GitHub repository nanocoai/nanoclaw (30,727 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,258 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
Other skills, from other repositories
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birdclaw
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apple-reminders
Manage Apple Reminders via remindctl CLI (list, add, complete, delete).
imsg
Use imsg to read and send iMessage/SMS via macOS Messages.app.