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 compute-env-setupgit 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/compute-env-setup)<a href="https://agentmods.dev/skills/unicomai/wanwu/compute-env-setup"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/compute-env-setup/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/compute-env-setup"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/compute-env-setup.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 | $0.00134 | $0.04189 |
| Opus 5.5 | $0.00054 | $0.01676 |
| Sonnet 5.5 | $0.00027 | $0.00838 |
| Haiku 4.5 | $0.00013 | $0.00419 |
Grade A, and why
compute-env-setup 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.
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.
Setting up compute environments on a remote provider
Overview
What's invariant across every backend is what a job needs: a software stack (specific package versions, often with load-bearing install ordering), possibly some large model weights placed where the tool will find them, and a resource shape. What varies — a lot — is how a given provider materialises those three things and how an env name gets resolved to them at submit time. This skill is about keeping one declarative spec that says what the environment is, and treating how it gets built and addressed on this provider as something you figure out once per provider. The work ranges from minutes (another conda env on a host that has ten) to days (GPU image + weight cache + egress on a fresh backend), and most of the long tail is diagnosing why the documented invocation doesn't work even though every import succeeds.
Before building anything: compute_details({provider, mode:"read"}). The env, or a near-match you can extend, may already exist. The doc you get back is also where you'll record what you set up.
Provider shapes
These are not exhaustive and they blend at the edges (a Slurm node can run Apptainer containers built from the same Dockerfile a cloud backend uses). The point is to recognise which shape you're in, because that determines what "build", "register", and "resolve" mean.
Direct SSH host (conda or venv). You have a shell on the machine. There is no image, no runner script, no renderer — you are the renderer: read the spec's pip_phases and run them in order after conda create -n <name> python=<X>. (When the spec's base is a Docker image string, treat it as documentation of the python + CUDA versions you need, not something to pull.) Weights live in scratch or home; download once, point the tool's cache env var there. The env name is the conda env name — name it exactly what you want --env to accept, there's no aliasing layer. submit_job activates it (conda run -n <name> …) and runs. "Registering" is implicit: once the host is added as a Claude Science SSH provider, its probe lists conda envs, and you append a ### env: block to compute_details so the next agent knows what's there without re-probing. Lowest ceremony; often right for a personal GPU box.
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 · 128 lines · 134 tokens per session scan A 83a2e8ae7e29
compute-env-setup is a skill published in the GitHub repository UnicomAI/wanwu (2,371 stars, last pushed 2d ago), licensed Apache-2.0. It adds 134 tokens to every session and 4,189 once invoked, about $0.0005 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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