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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/nanocoai/nanoclawnpx agentmods add skills/nanocoai/nanoclaw/update-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/nanocoai/nanoclaw/update-skills)<a href="https://agentmods.dev/skills/nanocoai/nanoclaw/update-skills"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/update-skills/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/update-skills"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/update-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 73 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00045 | $0.00711 |
| Opus 5 | $0.00023 | $0.00356 |
| Sonnet 5 | $0.00009 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
update-skills 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 11d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Update installed skills
Refresh the code carried by installed channel and provider skills. This does
not re-run credential setup, change .env, alter wiring, or restart services.
Run from the NanoClaw project root. Use ordinary conversation for any choice; do not depend on a provider-specific question or skill-invocation tool.
1. Preflight and selection
Require a clean working tree:
git status --porcelain
If it is dirty, stop. Never mix a refresh with unrelated changes.
The default is every installed channel/provider. If the user asked for a
subset, pass its comma-separated names. Otherwise use all:
pnpm exec tsx scripts/update-skills.ts --skills all
# Example subset: --skills slack,opencode
The helper detects installed skills from the real channel/provider barrels. It
resolves each registry branch by checking configured remotes, so a fork whose
origin is the user's repo and whose official source is upstream works
without special handling. NANOCLAW_REGISTRY_REMOTE=<name> is an explicit
override and is validated before use.
2. Treat the JSON result as a gate
The command prints nanoclaw-skill-refresh/v1-shaped JSON fields with one
result per selected skill and exits nonzero unless every selected skill was
fully refreshed.
- Continue only when
successistrueand every status isrefreshed. - A missing skill, missing structured apply contract, unresolved input, agent fallback, fetch error, or dependency error is blocking.
- Never record a failed skill and continue toward an upgrade completion stamp.
- Preserve the full report in the update summary.
The refresh engine overwrites skill-owned registry files and advances exact
dependency/CLI-manifest pins. It skips prompts, operator walkthroughs, .env
writes, wiring, restarts, and ordinary build/test directives.
3. Validate the composed checkout
After a successful refresh:
pnpm run build
pnpm test
If files under container/agent-runner/src/ changed, also run:
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.
- 11d ago First seen · 90 lines · 45 tokens per session scan A 7f403caa07d9
update-skills is a skill published in the GitHub repository nanocoai/nanoclaw (30,736 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 711 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 2026-08-30.
Other skills, from other repositories
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.
Documentation Manager
Use when writing or reviewing pynchy documentation, deciding where to document things, updating the docs, checking doc consistency, or fixing broken links. Covers information architecture, writing philosophy, tree-shaped navigation, doc-code coupling, no hard-coded usernames, extensibility framing for pluggable…
python-heredoc
When running multi-line Python code or code with quotes, apostrophes, or f-strings via Bash, always use heredoc syntax instead of python -c to avoid shell quoting issues.