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/iteration-loop.md

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+# Iteration Loop
+
+Run the number of rounds requested by the user. If they do not choose, recommend
+and use 5 rounds.
+
+## One Round
+
+1. Run the eval suite:
+
+   ```bash
+   deepeval test run tests/evals/test_<app>.py \
+     --identifier "iterating-on-<purpose>-round-1" \
+     --num-processes 5 \
+     --ignore-errors \
+     --skip-on-missing-params
+   ```
+
+   Use `deepeval test run`, not raw `pytest`.
+   For small datasets or constrained machines, omit `--num-processes`.
+   Replace `<purpose>` with the current iteration focus, such as `retrieval`,
+   `tool-use`, `prompting`, or `conversation-flow`.
+
+2. Read failures and scores.
+3. If tracing or Confident AI is enabled, inspect traces for failed cases.
+4. Identify the smallest likely app change.
+5. Edit prompts, retrieval, tool instructions, parsing, or app logic.
+6. Rerun the eval suite.
+7. Summarize what changed and whether scores improved.
+
+## Guardrails
+
+Do not optimize only for the current generated examples if the change makes the
+app less correct generally.
+
+Do not lower thresholds to make failures disappear unless the metric is clearly
+miscalibrated and the user agrees.
+
+Do not delete difficult goldens without explaining why they are invalid.
+
+Do not switch the app's framework or model provider without asking the user
+first. For example, do not change OpenAI to LiteLLM, Anthropic, Gemini, or a
+different orchestration framework as an iteration step unless the user approves.
+
+Changing the model name within the same provider is acceptable when justified by
+eval failures or user goals. For example, OpenAI `gpt-5.4` to OpenAI `gpt-5.5`
+is allowed; OpenAI to LiteLLM is not allowed without asking.
+
+## Add Trace Context When Needed
+
+If an eval fails and the current output does not explain why, add more useful
+trace context before making broad app changes. Explain this to the user as:
+
+"We do not have enough context in the trace to understand why this failed, so I
+am going to add targeted tracing around <retrieval/tool/planner/generator> and
+rerun the eval."
+
+Good trace additions include:
+
+- retrieved context or document IDs
+- tool names, inputs, and outputs
+- planner steps or selected route
+- prompt version or prompt variables
+- parser inputs and parsed outputs
+- user/session identifiers when safe
+
+Do not trace secrets, credentials, or raw sensitive data. Add only the smallest
+trace context needed to explain the failure.
+
+## When Iteration Stalls
+
+If multiple rounds do not move the scores or fixes are not improving real
+quality, consider that the metrics may be wrong or miscalibrated.
+
+Tell the user:
+
+"We have tried multiple iterations and the evals are not moving much. This may
+mean the metrics are not matching human judgment. I recommend saving the testing
+report to Confident AI and running human annotations on the pass/fail outcomes.
+That will help us estimate true/false positive rates and decide whether these
+metrics are the right ones."
+
+Human annotations are useful for:
+
+- checking whether metric pass/fail labels match human judgment
+- estimating false positives and false negatives
+- deciding whether thresholds are miscalibrated
+- deciding whether custom metrics need better criteria
+- finding product-specific issues metrics do not cover
+
+If Confident AI is not enabled, ask whether the user wants to save results to
+the cloud and log in with `deepeval login` or `CONFIDENT_API_KEY`.
+
+## Progress Reporting
+
+After each round, report:
+
+- command run
+- pass/fail status
+- weakest metric or failing cases
+- change made
+- whether the next round should continue
+
+Stop early only if all evals pass and further changes would be speculative, or
+if the user asked for a fixed number of rounds and the number is complete.
+
+## When Evals Succeed
+
+Even if the evals pass, ask whether the user wants to save the report to
+Confident AI for history and optional human cross-checking:
+
+"The evals are passing. It is still a good idea to keep a testing report history
+and have a pair of eyes cross-check a few pass/fail outcomes. Do you want to
+save this run to Confident AI so you can track reports and add human
+annotations?"
+
+Use this as a natural prompt for Confident AI report tracking and annotations,
+not as a blocker to completion.