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 add-gchatgit 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/add-gchat)<a href="https://agentmods.dev/skills/nanocoai/nanoclaw/add-gchat"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/add-gchat/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/add-gchat"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/add-gchat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00013 | $0.01453 |
| Opus 5 | $0.00006 | $0.00727 |
| Sonnet 5 | $0.00003 | $0.00291 |
| Haiku 4.5 | $0.00001 | $0.00145 |
Grade A, and why
add-gchat 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.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Google Chat Channel
Adds Google Chat support via the Chat SDK bridge. NanoClaw doesn't ship channels
in trunk — this skill copies the Google Chat adapter in from the channels
branch.
The mechanical steps under Apply carry nc: directive fences: an agent
reads the prose and applies them, and a parser can apply them deterministically
from the same document. Every directive is idempotent, so the whole skill is
safe to re-run; anything a parser can't apply falls back to the prose beside it.
Apply
1. Copy the adapter and its registration test
Fetch the channels branch and copy the Google Chat adapter and its
registration test into src/channels/ (overwrite — the branch is canonical):
src/channels/gchat.ts
src/channels/gchat-registration.test.ts
2. Register the adapter
Append the self-registration import to the channel barrel (skipped if the line is already present). This one line is the skill's only reach-in into core:
import './gchat.js';
3. Install the adapter package
Pinned to an exact version — the supply-chain policy rejects ranges and latest:
@chat-adapter/[email protected]
4. Build and validate
Build first: it guards the typed createChatSdkBridge(...) core call and proves
the dependency is installed. Then run the one integration test.
pnpm run build
pnpm exec vitest run src/channels/gchat-registration.test.ts
gchat-registration.test.ts imports the real channel barrel and asserts the
registry contains gchat. It goes red if the import line is deleted or drifts,
if the barrel fails to evaluate, or if @chat-adapter/gchat isn't installed (the
import throws) — so it also covers the dependency from step 3. End-to-end
delivery against a real Google Chat space is verified manually once the service
runs — see Credentials and Next Steps.
Credentials
Google Cloud setup is human and interactive — these steps are prose, not directives (no parser can click through the Google Cloud Console). A recipe rebuild produces a compiling, registered adapter that cannot receive a message until they're done.
What ships with it
2 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 · 128 lines · 13 tokens per session scan A 9bf62928c95a
add-gchat is a skill published in the GitHub repository nanocoai/nanoclaw (30,727 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 1,453 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
Pynchy Ops
Use when managing the pynchy service on the server — deploying changes, observing logs, checking service status, restarting the service, setting up GitHub auth, rebuilding the agent container, or running commands on the live Pynchy host. Also use when interacting with the LiteLLM proxy — investigating failed requests…
webhook-subscriptions
Create and manage webhook subscriptions for event-driven agent activation. Use when the user wants external services (GitHub, GitLab, Stripe, Linear, PagerDuty, Sentry, or any generic source) to trigger agent runs by POSTing events to a URL.
Pynchy Development
Use when running pynchy locally — running the app, tests, linting, formatting, prek hooks, or rebuilding the agent container. Also use when determining whether you're on the live Pynchy host or a local machine, and for debugging agent behavior-- session transcript branching, inspecting message history and agent traces…
Pynchy Plugin Authoring
Use when creating, scaffolding, or updating a pynchy plugin, including channels, MCP servers, skills, agent cores, workspace specs, and container runtime plugins. Also use when users ask how to register plugins via config.toml, add entry points, or validate plugin hook wiring.
slack-token-extractor
Refresh expired Slack browser tokens (xoxc/xoxd) using persistent browser sessions. Use when Slack MCP tools fail with authentication errors.
x-integration
Post tweets, like, reply, retweet, and quote on X (Twitter) using browser automation. Use when the user asks you to interact with X/Twitter.