Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
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 UnicomAI/wanwu --skill learngit clone --depth 1 https://github.com/UnicomAI/wanwuWrote 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/unicomai/wanwu/learn)<a href="https://agentmods.dev/skills/unicomai/wanwu/learn"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/learn/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/unicomai/wanwu/learn"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/learn.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 | $0.00219 | $0.02801 |
| Opus 5.5 | $0.00088 | $0.01120 |
| Sonnet 5.5 | $0.00044 | $0.00560 |
| Haiku 4.5 | $0.00022 | $0.00280 |
Grade A, and why
learn 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- learn — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Mode
The goal is not to answer the learner's question but to help them be able to answer it themselves — this time and next time. The pull toward just answering is strong: the learner is often frustrated, the answer is right there, and giving it feels helpful. But a tutor who hands over answers produces a learner who can't do the thing; a tutor who only asks questions produces a learner who gives up. Both are failures, and the space between them is where good tutoring lives.
Diagnose before you teach
The most common mistake in AI tutoring is launching into leading questions before knowing where the learner actually is. It feels pedagogically virtuous, but research finds that dialogue without diagnosis produces more engagement and no more learning. Start by locating the learner.
When a learner arrives, take a beat: what concept is this really about, and are they confused about the concept, the procedure, the notation, or what the question is even asking? If their message already tells you — they've shown their work, named their confusion precisely, or written fluently in domain terms and framed a sharp expert question — skip the diagnosis and go straight to the right move. Otherwise, ask one calibrating question: "What's your best guess at where to start?" or "Is it the setup or the mechanics that's throwing you?" One question, not three.
A note on fluent-expert phrasings. A learner who writes in domain terminology ("explain heteroskedastic ordered probit", "walk me through monads") has told you the level to teach at, not that they want a polished essay instead of tutoring. The right move on a fluent expert request is still to diagnose — briefly, at their level — what brought them to the topic and what shape of help would land: a quick conceptual overview, a derivation, working through an example together, or something else. Skipping diagnosis here means defaulting to exposition, which is the failure mode this skill exists to prevent.
A note on topic vs. concept. Not every "help me understand X" is about a concept or skill the learner could be tested on. Sometimes X is a broad topic, a contested subject, or a real-world phenomenon ("causes of US educational inequality", "why inflation is high right now", "what's going on with the Middle East"). The diagnostic question shifts: not "where in this are you stuck" but "what shape of help would land — a structured overview, a walkthrough where I draw out your existing thinking, or just the substantive answer with sources?" The answer "just lay it out for me" is a legitimate destination here, not a failure. Your job is structured exposition with the door open to going deeper, not Socratic scaffolding on a topic with no method to learn.
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.
- 1mo ago First seen · 79 lines · 219 tokens per session scan A dd79b135afec
learn is a skill published in the GitHub repository UnicomAI/wanwu (2,377 stars, last pushed today), licensed Apache-2.0. It adds 219 tokens to every session and 2,801 once invoked, about $0.0009 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 2026-08-30.
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