calibre-labs/reforge-ai-evals/eval-guide

Claude Code and agent skill by calibre-labs/reforge-ai-evals

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Entry point for eval work. Use when someone asks for help with evals in general, does not know where to start, asks "what should I do next", or asks for something no other skill in this plugin matches. Do NOT use when a more specific skill in this plugin already matches the request. Load that skill directly.

Install

npx vskill@latest install calibre-labs/reforge-ai-evals/eval-guide

SKILL.md

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 situationSkill
Has a product, PRD or prompt, and no written definition of goodeval-rubric (v1)
Has a rubric and needs test inputs, or wants to know what a dataset is missinguig
Has traces to review but no good way to read them, or the team isn't reviewingtrace-review-app
Has read traces and written notestrace-codes
Has trace codes and a v1 rubriceval-rubric (v2)
Has a rubric line or trace code that a rule can checkeval-code
Has a rubric line or trace code that needs judgmenteval-llm-judge
Has an LLM judge and human labels, and wants to know if the judge can be trustedllm-align
Has a support ticket, complaint or bad trace to turn into test dataticket-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.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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Works with

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