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 using-model-endpointgit 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/using-model-endpoint)<a href="https://agentmods.dev/skills/unicomai/wanwu/using-model-endpoint"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/using-model-endpoint/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/using-model-endpoint"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/using-model-endpoint.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 | $0.00040 | $0.00421 |
| Opus 5.5 | $0.00016 | $0.00168 |
| Sonnet 5.5 | $0.00008 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
using-model-endpoint 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.
What it actually says
You are a pure HTTP client of BASE_URL. Each registered model endpoint
gets its own inference kernel — a Python REPL whose network egress is scoped
to exactly that endpoint — reached via
compute_provider({'provider': '<slug>', 'code': '…'}) (<slug> from
list_compute, without the infer: prefix).
BASE_URLis preloaded (as a Python variable AND asos.environ["BASE_URL"]) — build request URLs from it, never hardcode hosts/ports. Call the model's native API withhttpx(preinstalled) orrequests; request shapes live in the provider's own runbook skill (the registration'sskillName).- Hosted endpoints: send
Authorization: Bearer $INFER_API_KEY(always the canonical env name when a credential is delivered; the credential's own name is usually aliased too). Local endpoints need no auth header. - Requests ride the sandbox HTTP proxy (
HTTP_PROXY/HTTPS_PROXYare set) — don't disable it (e.g.trust_env=False) or the endpoint is unreachable. - No job lifecycle here (no submit/harvest) — direct request/response only.
Managed endpoints (entries with managed: true / a location field in
list_compute): their lifecycle — daemon-owned start/stop, registration,
free_port()/register() — lives in the
managed-model-endpoints skill. Cells against them are still just
HTTP calls to BASE_URL; the daemon brings the model up on demand (a cold
start streams its progress into your cell and can take minutes).
What ships with it
4 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 · 31 lines · 40 tokens per session scan A 2ee613c4530f
using-model-endpoint is a skill published in the GitHub repository UnicomAI/wanwu (2,377 stars, last pushed 4d ago), licensed Apache-2.0. It adds 40 tokens to every session and 421 once invoked, about $0.0002 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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