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/traced-evals.md

@@ -0,0 +1,80 @@
+# Traced Evals
+
+Tracing is the default single-turn eval path when the app can produce traces
+through a DeepEval integration or manual instrumentation. The trace is the
+end-to-end execution and spans are the components; component-level metrics are
+attached to specific spans inside the same single-turn tracing eval, not split
+into a separate test shape.
+
+This reference covers the **eval-coupled** side of tracing: attaching metrics
+to spans and the pytest/script shapes for traced evals. To **instrument** the
+app — add `@observe`, wire framework integrations, set span types, tags, and
+metadata — use the `deepeval-tracing` skill.
+
+## Component / Span Metrics
+
+When metrics belong to a specific component, keep them in the single-turn
+tracing eval and attach them to the exact span they evaluate.
+
+If a supported integration creates the span, stage metrics for the next span of
+that type:
+
+```python
+from deepeval.tracing import next_retriever_span
+
+from metrics import RETRIEVER_SPAN_METRICS
+
+
+with next_retriever_span(metrics=RETRIEVER_SPAN_METRICS):
+    run_ai_app_with_integration_tracing(golden.input)
+```
+
+If manual instrumentation or the integration supports observed component spans,
+attach metrics directly to `@observe`:
+
+```python
+from deepeval.tracing import observe
+
+from metrics import GENERATOR_LLM_SPAN_METRICS
+
+
+@observe(type="llm", metrics=GENERATOR_LLM_SPAN_METRICS)
+def call_model(messages):
+    ...
+```
+
+Name span metric lists after the component, such as
+`RETRIEVER_SPAN_METRICS`, `GENERATOR_LLM_SPAN_METRICS`, or
+`ORDER_LOOKUP_TOOL_SPAN_METRICS`. Do not create one global component metric
+list for the app. Use `next_agent_span`, `next_llm_span`, `next_tool_span`, or
+`next_retriever_span` to match the span type the integration creates.
+
+## Pytest vs Script Shapes
+
+For CI/CD, prefer the pytest shape shown in each integration doc — pass the
+`Golden` directly through the traced app and assert:
+
+```python
+@pytest.mark.parametrize("golden", dataset.goldens)
+def test_agent(golden: Golden):
+    run_ai_app_with_integration_tracing(golden.input)
+    assert_test(golden=golden, metrics=TRACE_METRICS)
+```
+
+For scripts or iteration loops, use `evals_iterator` and pass the `Golden`
+through the traced app:
+
+```python
+for golden in dataset.evals_iterator(metrics=TRACE_METRICS):
+    run_ai_app_with_integration_tracing(golden.input)
+```
+
+Do not convert a traced single-turn eval into a hand-built `LLMTestCase` unless
+the user explicitly chooses no tracing.
+
+## Confident AI
+
+If the user chooses Confident AI results, confirm either `deepeval login` has
+been run or `CONFIDENT_API_KEY` is exported. Prefer `CONFIDENT_API_KEY` for CI
+and other non-interactive runs. After evals, use `deepeval view` to open the
+latest hosted report when appropriate.