feat(skills): install deepeval skill from confident-ai/deepeval

Adds the deepeval eval-loop skill via `npx skills add` with skills-lock.json
for reproducible reinstalls. Symlinked to Claude Code via .claude/skills/.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016z2ZFYHQCex8yZAMVMTZzZ

.agents/skills/deepeval/references/datasets.md

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+# Datasets
+
+Use documented `EvaluationDataset` APIs directly. Do not invent wrapper helpers
+for dataset loading in templates.
+
+Dataset source order is strict:
+
+1. Ask whether the user already has a dataset.
+2. If they do, load it with the documented `EvaluationDataset` API.
+3. If they do not, read `synthetic-data.md` and generate one with
+   `deepeval generate`.
+
+Do not hand-create or make up goldens. For a useful first generated eval
+dataset, target about 30-50 goldens. If the user insists on manual goldens,
+warn that generated goldens are usually less biased and more reproducible, then
+recommend augmenting any manual seed set with `deepeval generate --method
+goldens`.
+
+If the user has a dataset, check its size before accepting it as sufficient.
+Fewer than 10 goldens is very likely too small. If the dataset is small or the
+user is unhappy with it, read `synthetic-data.md` and consider augmenting from
+existing goldens with `deepeval generate`.
+
+## Local JSON
+
+```python
+from deepeval.dataset import EvaluationDataset
+
+dataset = EvaluationDataset()
+dataset.add_goldens_from_json_file(file_path="tests/evals/.dataset.json")
+```
+
+## Local JSONL
+
+```python
+dataset = EvaluationDataset()
+dataset.add_goldens_from_jsonl_file(file_path="tests/evals/.dataset.jsonl")
+```
+
+## Local CSV
+
+```python
+dataset = EvaluationDataset()
+dataset.add_goldens_from_csv_file(file_path="tests/evals/.dataset.csv")
+```
+
+If the CSV uses custom column names, set the documented column arguments when
+adapting the template.
+
+## Confident AI
+
+```python
+dataset = EvaluationDataset()
+dataset.pull(alias="My Evals Dataset")
+```
+
+Use this when the user says the dataset is on Confident AI and credentials or
+MCP/API access are available.
+
+## Pytest Convention
+
+Load the dataset directly in the test file immediately before parametrization.
+Do not hide dataset loading in `conftest.py` or custom fixture wrappers:
+
+```python
+dataset = EvaluationDataset()
+dataset.add_goldens_from_json_file(file_path="tests/evals/.dataset.json")
+
+@pytest.mark.parametrize("golden", dataset.goldens)
+def test_llm_app(golden):
+    ...
+```
+
+For end-to-end test cases that are built before assertion, add them back to the
+dataset with `dataset.add_test_case(...)`, then parametrize over
+`dataset.test_cases` if that better matches the app.
+
+Datasets are either single-turn or multi-turn once loaded. Do not mix `Golden`
+and `ConversationalGolden` items in one dataset.
+
+For chatbot / multi-turn agent evals, the loaded dataset contains
+`ConversationalGolden`s. After loading, pass `dataset.goldens` to
+`ConversationSimulator.simulate(...)` to create `ConversationalTestCase`s for
+pytest.