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Agent Skills that teach coding assistants how to add DeepEval evaluations,
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TypeScript and ESLint rules that MUST be followed when creating, modifying, or reviewing any file under apps/frontend/, including .ts, .tsx, .js, and .jsx files. Also apply when discussing frontend linting, type safety, or ESLint configuration.
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Agent Skills that teach coding assistants how to add DeepEval evaluations, generate datasets, instrument applications with tracing, and iterate on AI applications using eval results.
| Skill | Description |
|---|---|
| deepeval | Main DeepEval skill for adding evals to AI apps, generating or reusing datasets, creating pytest eval suites, enabling tracing, sending results to Confident AI, and iterating on failures. |
| deepeval-otel | Instrument any app with raw OpenTelemetry so traces export to Confident AI's Observatory — no deepeval package required. Covers the confident.* span/trace attributes and the OTLP endpoint. |
| deepeval-tracing | Instrument an AI app with DeepEval's native tracing — @observe, span types, tags/metadata, and the framework / model / vector-DB integration index — so traces reach Confident AI. |
skills/deepeval folder from this repository.Download or clone the skills/deepeval folder inside the skills folder and place it directly into your local project's skills directory:
mkdir -p .claude/skills/
cp -r path/to/downloaded/deepeval .claude/skills/
This repository includes a Cursor plugin manifest that points to ./skills/.
When installed as a plugin, Cursor can discover the deepeval skill directly.
Install the skill with a skills-compatible installer:
npx skills add confident-ai/deepeval --skill "deepeval"
Copy or symlink skills/deepeval into your agent's skills directory.
For local evals, install DeepEval in the target project:
pip install -U deepeval
For hosted reports, traces, production monitoring, or online evals, connect DeepEval to Confident AI:
deepeval login