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 agentmods add skills/mathclaw-ruc/mathclaw/clawhubnpx skills add MathClaw-ruc/MathClaw --skill clawhubgit clone --depth 1 https://github.com/MathClaw-ruc/MathClawWrote 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/mathclaw-ruc/mathclaw/clawhub)<a href="https://agentmods.dev/skills/mathclaw-ruc/mathclaw/clawhub"><img src="https://agentmods.dev/badge/skills/mathclaw-ruc/mathclaw/clawhub.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.00358 |
| Opus 5 | $0.00010 | $0.00179 |
| Sonnet 5 | $0.00004 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
clawhub 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 4d 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.
This is a copy
100% identical to clawhub — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
ClawHub
Public skill registry for AI agents. Search by natural language (vector search).
When to use
Use this skill when the user asks any of:
- "find a skill for …"
- "search for skills"
- "install a skill"
- "what skills are available?"
- "update my skills"
Search
npx --yes clawhub@latest search "web scraping" --limit 5
Install
npx --yes clawhub@latest install <slug> --workdir ~/.nanobot/workspace
Replace <slug> with the skill name from search results. This places the skill into ~/.nanobot/workspace/skills/, where nanobot loads workspace skills from. Always include --workdir.
Update
npx --yes clawhub@latest update --all --workdir ~/.nanobot/workspace
List installed
npx --yes clawhub@latest list --workdir ~/.nanobot/workspace
Notes
- Requires Node.js (
npxcomes with it). - No API key needed for search and install.
- Login (
npx --yes clawhub@latest login) is only required for publishing. --workdir ~/.nanobot/workspaceis critical — without it, skills install to the current directory instead of the nanobot workspace.- After install, remind the user to start a new session to load the skill.
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.
- 4d ago First seen · 54 lines · 19 tokens per session scan A 623a6e31e97a
clawhub is a skill published in the GitHub repository MathClaw-ruc/MathClaw (372 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 358 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to clawhub, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…