using-model-endpoint
Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint.
git clone --depth 1 https://github.com/UnicomAI/wanwu /tmp/using-model-endpoint && cp -r /tmp/using-model-endpoint/configs/microservice/bff-service/configs/agent-skills/claude-science/using-model-endpoint ~/.claude/skills/using-model-endpointSKILL.md
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_URL` is preloaded (as a Python variable AND as
`os.environ["BASE_URL"]`) — build request URLs from it, never hardcode
hosts/ports. Call the model's **native API** with `httpx` (preinstalled)
or `requests`; request shapes live in the provider's own runbook skill
(the registration's `skillName`).
- 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_PROXY` are 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).万悟平台 SSE 子会话递归嵌套与三明治序列渲染架构指南。涵盖 parentId 领养、order 绝对排序、动静 Chunk 分层及 Vue 2 响应式引用协议。
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