# validate-evaluator

This skill checks whether an LLM judge agrees with expert labels and measures TPR/TNR on a held-out set.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1100 installs · ★ 1557
- Author: ai-evals-course
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/ai-evals-course/evals-skills/tree/HEAD/skills/validate-evaluator/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/validate-evaluator

## What it does

Reach for it once the judge prompt already exists. The skill takes human-labeled Pass/Fail traces and splits them three ways: 10–20% for training few-shot examples, 40–45% for dev, 40–45% for test. You run the judge on dev repeatedly until TPR and TNR stop moving. The target is above 90% on both, the floor is above 80%.

Eight steps cover the whole loop, from `train_test_split` to a bootstrap confidence interval. Disagreements get sorted by type: a false Pass means the judge is too lenient, a false Fail means it is too strict. From there you tighten the wording, swap examples, or break the criterion into smaller checks. For an aggregate pass rate on production data there is the Rogan-Gladen correction, `theta_hat = (p_obs + TNR - 1) / (TPR + TNR - 1)`. It breaks down when TPR + TNR - 1 sits near zero, which means the judge is no better than a coin flip.

Budget roughly 100 traces with binary labels per failure mode, about half Pass and half Fail, labeled by a domain expert. Look at the test set once. Tune the prompt after that and your final number stops being unbiased. Code-based evaluators are out of scope here, since they are deterministic and belong in unit tests.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill validate-evaluator -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/validate-evaluator/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/validate-evaluator/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill validate-evaluator -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/validate-evaluator/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill validate-evaluator -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/validate-evaluator/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install ai-evals-course/evals-skills validate-evaluator
```

2. **Manually: put the files in place**
- File: `.github/skills/validate-evaluator/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill validate-evaluator -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/ai-evals-course/evals-skills --path skills/validate-evaluator
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/validate-evaluator/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill validate-evaluator -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/ai-evals-course/evals-skills/tree/HEAD/skills/validate-evaluator/SKILL.md

## When to use it
- check whether an LLM judge matches expert labels
- measure judge TPR and TNR on a held-out set
- find which examples the judge gets wrong
- correct a raw production pass rate for known judge errors
- attach a confidence interval to a success-rate estimate

## Example prompts
- split my labeled traces into train, dev and test for judge calibration
- compute TPR and TNR on the dev set and show me every disagreement
- apply the Rogan-Gladen correction to the raw production scores

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 01d8a52e925aa894.

BEGIN THIRD-PARTY FILE 01d8a52e925aa894
````text
---
name: validate-evaluator
description: >
  Calibrate an LLM judge against human labels using data splits, TPR/TNR, and
  bias correction. Use after writing a judge prompt (write-judge-prompt) when you
  need to verify alignment before trusting its outputs. Do NOT use for code-based
  evaluators (those are deterministic; test with unit tests per `write-code-eval`).
---

# Validate Evaluator

Calibrate an LLM judge against human judgment.

## Overview

1. Split human-labeled data into train (10-20%), dev (40-45%), test (40-45%)
2. Run judge on dev set and measure TPR/TNR
3. Iterate on the judge until TPR and TNR > 90% on dev set
4. Run once on held-out test set for final TPR/TNR
5. Apply bias correction formula to production data

## Prerequisites

- A built LLM judge prompt (from write-judge-prompt)
- Human-labeled data: ~100 traces with binary Pass/Fail labels per failure mode
  - Aim for ~50 Pass and ~50 Fail (balanced, even if real distribution is skewed)
  - Labels must come from a domain expert, not outsourced annotators
- Candidate few-shot examples from your labeled data

## Core Instructions

### Step 1: Create Data Splits

Split human-labeled data into three disjoint sets:

| Split | Size | Purpose | Rules |
|-------|------|---------|-------|
| **Training** | 10-20% (~10-20 examples) | Source of few-shot examples for the judge prompt | Only clear-cut Pass and Fail cases. Used directly in the prompt. |
| **Dev** | 40-45% (~40-45 examples) | Iterative evaluator refinement | Never include in the prompt. Evaluate against repeatedly. |
| **Test** | 40-45% (~40-45 examples) | Final unbiased accuracy measurement | Do NOT look at during development. Used once at the end. |

Target: 30-50 examples of each class (Pass and Fail) across dev and test combined. Use balanced splits even if real-world prevalence is skewed — you need enough Fail examples to measure TNR reliably.

```python
from sklearn.model_selection import train_test_split

# First split: separate test set
train_dev, test = train_test_split(
    labeled_data, test_size=0.4, stratify=labeled_data['label'], random_state=42
)
# Second split: separate training examples from dev set
train, dev = train_test_split(
    train_dev, test_size=0.75, stratify=train_dev['label'], random_state=42
)
# Result: ~15% train, ~45% dev, ~40% test
```

### Step 2: Run Evaluator on Dev Set

Run the judge on every example in the dev set. Compare predictions to human labels.
````
END THIRD-PARTY FILE 01d8a52e925aa894

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