Agent Skills › advaitpaliwal/feynman › using-model-endpoint

using-model-endpoint

GitHub

该技能用于调用已配置的模型端点并解析响应，适用于推理任务。流程包括确认端点配置、构建请求、保存元数据与响应、验证结果及区分预测与事实，确保操作安全规范。

skills/using-model-endpoint/SKILL.md advaitpaliwal/feynman

触发场景

需要调用远程或本地模型进行推理时使用自定义连接器或注册端点获取预测结果时

安装

npx skills add advaitpaliwal/feynman --skill using-model-endpoint -g -y

更多选项

不安装直接使用

npx skills use advaitpaliwal/feynman@using-model-endpoint

指定 Agent (Claude Code)

npx skills add advaitpaliwal/feynman --skill using-model-endpoint -a claude-code -g -y

安装 repo 全部 skill

npx skills add advaitpaliwal/feynman --all -g -y

预览 repo 内 skill

npx skills add advaitpaliwal/feynman --list

SKILL.md

Frontmatter

{
    "name": "using-model-endpoint",
    "description": "Call a configured Feynman model endpoint and interpret its response. Use when a task needs inference from a registered endpoint, remote model API, local model service, or custom connector-backed predictor."
}

Using Model Endpoint

Use this skill after a model endpoint is already configured or explicitly chosen.

Workflow:

Confirm endpoint identity, auth status, input schema, output schema, model version, and rate/size limits.
Build a minimal request with explicit inputs and no hidden context.
Save request metadata, response, latency, status, and parsing code as artifacts without exposing secrets.
Validate response shape and handle model errors as evidence, not as missing work to hide.
Interpret predictions separately from source-backed facts.

When the endpoint is not configured, switch to managed-model-endpoints or compute-env-setup instead of pretending inference ran.