langwatch/skills
Compatibility router for LangWatch evaluation requests.
npx skills add langwatch/skills --skill evaluations
Related neighbors and high-traction skills in the same topics — useful to compare before installing.
Helps users discover and install agent skills when they ask questions like "how do I do X", "fi…
3.3M installsBrowser automation CLI for AI agents. Use when the user needs to interact with websites, includ…
810.4K installsReview UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "chec…
617.3K installsBuild, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and …
576.5K installsPrepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Te…
568.3K installsOther skills from langwatch/skills · top by installs.
npx skills add langwatch/skills
Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.
main
Parsed from SKILL.md frontmatter.
Files included with this skill beyond the listing page.
SKILL.md
2,537 B
SUMMARY.md
318 B
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:
experiments is missing, tell the user to install it with:``bash npx [email protected] add langwatch/skills/experiments ``
online-evaluations is missing, tell the user to install it with:``bash npx [email protected] 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.