Agent Skills › langwatch/langwatch › evaluations

evaluations

GitHub

LangWatch评估请求的路由技能，用于区分离线实验与在线评估。通过意图分类将请求分发至experiments或online-evaluations技能，模糊时询问用户确认，确保调用正确的专用工作流。

skills/_compiled/native/evaluations/SKILL.md langwatch/langwatch

Trigger Scenarios

用户提出评估需求但意图不明确需要路由到具体评估技能的场景

Install

npx skills add langwatch/langwatch --skill evaluations -g -y

More Options

Non-standard path

npx skills add https://github.com/langwatch/langwatch/tree/main/skills/_compiled/native/evaluations -g -y

Use without installing

npx skills use langwatch/langwatch@evaluations

指定 Agent (Claude Code)

npx skills add langwatch/langwatch --skill evaluations -a claude-code -g -y

安装 repo 全部 skill

npx skills add langwatch/langwatch --all -g -y

预览 repo 内 skill

npx skills add langwatch/langwatch --list

SKILL.md

Frontmatter

{
    "name": "evaluations",
    "license": "MIT",
    "description": "Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.",
    "user-prompt": "Help me evaluate my agent",
    "compatibility": "Works with Claude Code and similar AI assistants. The `langwatch` CLI is the only interface for platform operations and documentation."
}

Route an Evaluation Request

This is a compatibility skill. Do not build an experiment, monitor, or guardrail from this skill.

Classify the user's intent:

Intent	Correct skill
Batch test a dataset, compare prompts or models, benchmark, create a CI quality gate	`experiments`
Score live traces or threads, monitor production quality, create a guardrail	`online-evaluations`

If the request remains ambiguous after inspecting context (a bare "make me an eval" that names neither a dataset nor live traffic), do not create anything yet. This choice picks what gets tested, so it is the user's to make, not a default's. Ask it as a question card and stop; the answer arrives as the next message.

In Langy, ask it with the question tool and stop; the options are the concrete alternatives, for example "A dataset, before deployment" and "Live production traffic". Elsewhere, ask the same question as one short line of prose.

A rejected field value is not this kind of choice. If a create later fails with a validation_error whose reason names the field and an expected list, correct that exact field from the list and retry once. Never turn a fixable slug into a question for the user.

Then hand off:

If the correct companion skill is available, load it and follow it instead of continuing here.
If experiments is missing, tell the user to install it with:
```
npx skills@1.5.19 add langwatch/skills/experiments
```
If online-evaluations is missing, tell the user to install it with:
```
npx skills@1.5.19 add langwatch/skills/online-evaluations
```

Do not recreate the companion skill's instructions from memory. Load the focused skill so its current workflow, safety checks, and verification steps are used.