Eval guide

This plugin splits eval work into skills, one per step. This skill only routes. Find the row that matches the user's situation, say which skill you are loading and why, then load it and follow its workflow. Do not improvise your own version of a step.

The user's situation	Skill
Has a product, PRD or prompt, and no written definition of good	eval-rubric (v1)
Has a rubric and needs test inputs, or wants to know what a dataset is missing	uig
Has traces to review but no good way to read them, or the team isn't reviewing	trace-review-app
Has read traces and written notes	trace-codes
Has trace codes and a v1 rubric	eval-rubric (v2)
Has a rubric line or trace code that a rule can check	eval-code
Has a rubric line or trace code that needs judgment	eval-llm-judge
Has an LLM judge and human labels, and wants to know if the judge can be trusted	llm-align
Has a support ticket, complaint or bad trace to turn into test data	ticket-to-eval

The order, for someone starting from nothing

1. eval-rubric: write v1 from the PRD.
2. uig: build a diverse set of inputs and run the agent on them.
3. The user reads the traces and writes notes. If the traces are hard to read, trace-review-app builds a review page first.
4. trace-codes: cluster the notes into codes, then eval-rubric again for v2.
5. eval-code and eval-llm-judge: one scorer per rubric line worth automating.
6. The user labels a sample pass or fail.
7. llm-align: measure each judge against those labels and fix it.
8. ticket-to-eval: keep the dataset growing from real feedback.

Two steps stay with a person

Reading and annotating traces (step 3) and labeling for calibration (step 6) are done by the user. Do not do them on the user's behalf, and do not treat your own judgment as a label. If the user asks to skip either one, explain what the later steps lose: codes that reflect the model's guess about quality, and judges nobody has checked.

When in doubt about which row fits, ask one question instead of guessing.