Agent skill

Output Eval Judge Prompt

by growthxai in growthxai/output

Design effective LLM judge .prompt files for evaluators. An agent skill from growthxai/output.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Output Eval Judge Prompt

skills CLI
$ npx skills add growthxai/output --skill output-eval-judge-prompt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install growthxai/output output-eval-judge-prompt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/growthxai/output.git skills-src && mkdir -p .claude/skills && cp -r skills-src/coding_assistants/claude/plugins/outputai/skills/output-eval-judge-prompt .claude/skills/output-eval-judge-prompt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
output-eval-judge-prompt
GitHub stars
442
Token cost
~3.2k tokens
SKILL.md length
800 words
Files
1
Skills in repo
50
Repo updated
First seen
Licence
Apache-2.0

At a glance

Design effective LLM judge .prompt files for evaluators. An agent skill from growthxai/output.

  • Works in 4 steps: Task and Criterion → Pass/Fail Definitions → Few-Shot Examples → …
  • Creating judgeVerdict/judgeScore/judgeLabel prompts
  • SKILL.md covers Overview, Prerequisites, The Four Components and Full .prompt File Example, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Output Eval Judge Prompt is an agent skill from growthxai/output. Design effective LLM judge .prompt files for evaluators. Use when creating judgeVerdict/judgeScore/judgeLabel prompts, or when existing judges produce unreliable results.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering LLM evaluation. The repository describes itself as: The open-source TypeScript framework for building AI workflows and agents. Designed for Claude Code describe what you want, Claude builds it, with all the best practices already… The licence is Apache-2.0.

When your agent uses it

  • Creating judgeVerdict/judgeScore/judgeLabel prompts
  • Existing judges produce unreliable results

Example prompts

  • “/output-eval-judge-prompt”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Task and Criterion
  2. Pass/Fail Definitions
  3. Few-Shot Examples
  4. Structured Output

What it can do on your machine

Read from SKILL.md and the folder at commit 99ee298. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Output Eval Judge Prompt loads about 3.2k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 800 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from growthxai/output at commit 99ee298, republished under its Apache-2.0 licence (© growthxai). 800 words, ~3,209 tokens.

Download SKILL.mdSave it as .claude/skills/output-eval-judge-prompt/SKILL.md (or your agent's skills folder).
name
output-eval-judge-prompt
description
Design effective LLM judge .prompt files for evaluators. Use when creating judgeVerdict/judgeScore/judgeLabel prompts, or when existing judges produce unreliable results.
allowed-tools
Read, Write, Edit

Designing LLM Judge Prompts

Overview

An LLM judge evaluates workflow output for a single, specific failure mode identified during error analysis. This skill covers how to design the .prompt file that powers judgeVerdict(), judgeScore(), or judgeLabel() calls. For the file format basics, see output-dev-prompt-file. For error analysis, see output-eval-error-analysis.

Prerequisites

Before writing a judge prompt:

  1. Error analysis is complete — You have identified the specific failure mode this judge targets (from output-eval-error-analysis)
  2. 20+ labeled examples — At least 20 pass and 20 fail traces for this failure mode, with ground_truth labels in dataset YAML files
  3. Code-based check ruled out — Confirmed that Verdict.* helpers (contains, matches, gte, etc.) cannot reliably detect this failure

The Four Components

Every effective judge prompt has exactly four components.

1. Task and Criterion

State the single failure mode being evaluated. Be specific and observable.

Good criteria (specific, observable):

  • "Does the blog post maintain a formal tone throughout, or does it slip into casual language?"
  • "Does the output contain any URLs that are fabricated rather than drawn from the input?"
  • "Does the summary faithfully represent the source material without adding claims not present in the original?"

Bad criteria (vague, holistic):

  • "Is this output high quality?"
  • "Rate the overall effectiveness of this response"
  • "How good is this content?"
2. Pass/Fail Definitions

Define exactly what constitutes pass and fail. Always binary — no Likert scales, no 1-5 ratings, no "partially meets criteria."

PASS: The blog post uses formal language throughout. Professional vocabulary,
complete sentences, no slang, no contractions, no first-person casual asides.

FAIL: The blog post contains one or more instances of casual language: slang,
contractions ("don't", "can't"), informal asides ("pretty cool", "super important"),
or conversational filler ("honestly", "basically").

Why binary: Likert scales create ambiguous boundaries (what's the difference between a 3 and a 4?). Binary forces precise definitions that LLMs can apply consistently and that you can validate against human labels.

3. Few-Shot Examples

Include at least three labeled examples: one clear pass, one clear fail, and one borderline case. Borderline examples are the most valuable — they teach the judge where the decision boundary lies.

Draw examples from your training split only (see output-eval-validate-judge). Never use dev or test examples as few-shot — that's data leakage.

Each example must include:

  • The relevant input/output excerpt
  • A detailed critique explaining the reasoning
  • The verdict (pass or fail)
4. Structured Output

Request JSON output with critique before verdict. This forces the judge to reason before deciding, which improves accuracy.

json
{
  "critique": "Detailed analysis of the output against the criterion...",
  "verdict": "pass"
}

Always put critique first in the schema. If verdict comes first, the judge commits to a decision before reasoning.

Full .prompt File Example

A judge for the "tone mismatch" failure mode:

# tests/evals/judge_tone@v1.prompt
---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-haiku-4-5-20251001
temperature: 0
maxOutputTokens: 1500
---

<system>
You are an evaluation judge. Your task is to determine whether a blog post maintains the requested tone throughout.

## Criterion

Assess whether the blog post consistently uses the requested tone. A single paragraph that breaks tone is a failure.

## Definitions

PASS: The blog post maintains the requested tone in every paragraph. Word choice, sentence structure, and rhetorical style all align with the requested tone.

FAIL: The blog post contains one or more paragraphs where the tone shifts away from what was requested. Common failures include:
- Formal request but casual language appears ("pretty cool", "super important", contractions)
- Professional request but opinionated editorializing appears
- Technical request but oversimplified explanations appear

## Examples

### Example 1: PASS
Requested tone: formal
Blog excerpt: "The implications of quantum computing for cryptographic security are substantial. Current encryption standards rely on the computational infeasibility of factoring large prime numbers, a guarantee that quantum algorithms may undermine."
Critique: The excerpt uses professional vocabulary ("implications", "computational infeasibility"), complete sentences, no contractions, and maintains an academic register. Consistent formal tone throughout.
Verdict: pass

### Example 2: FAIL
Requested tone: formal
Blog excerpt: "Quantum computing is basically going to break all our encryption. It's pretty wild when you think about it — everything we thought was secure might not be."
Critique: The excerpt contains multiple casual markers: "basically", "pretty wild", contractions ("It's", "might not be"), and conversational filler ("when you think about it"). This directly violates the formal tone request.
Verdict: fail

### Example 3: BORDERLINE (fail)
Requested tone: formal
Blog excerpt: "Quantum computing represents a paradigm shift in computational capability. The technology is incredibly promising, though it's important to note the current limitations in qubit stability and error correction."
Critique: Mostly formal, but contains "incredibly promising" (informal intensifier) and "it's" (contraction). While the overall register is professional, these lapses break the formal tone requirement. Even minor inconsistencies constitute a failure.
Verdict: fail

## Output Format

Return a JSON object with exactly two fields:
- "critique": A detailed analysis (3-5 sentences) citing specific evidence from the blog post
- "verdict": Either "pass" or "fail"
</system>

<user>
Requested tone: {{ requested_tone }}

Blog title: {{ blog_title }}

Blog post:
{{ blog_post }}

Evaluate whether this blog post consistently maintains the requested tone.
</user>

Wiring to judgeVerdict()

After creating the .prompt file, wire it to an evaluator using verify() and judgeVerdict():

typescript
// tests/evals/evaluators.ts
import { verify, judgeVerdict } from '@outputai/evals';
import { z } from '@outputai/core';
import { blogInput, blogOutput } from './schemas.js';

export const checkTone = verify(
  {
    name: 'check_tone',
    input: blogInput,
    output: blogOutput
  },
  async ({ input, output, context }) =>
    judgeVerdict({
      prompt: 'judge_tone@v1',
      variables: {
        requested_tone: String(context.ground_truth.expected_tone ?? input.tone ?? 'professional'),
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

Then add it to the eval workflow:

typescript
// tests/evals/workflow.ts
import { evalWorkflow } from '@outputai/evals';
import { checkTone } from './evaluators.js';

export default evalWorkflow({
  name: 'blog_generator_eval',
  evals: [
    {
      evaluator: checkTone,
      criticality: 'required',
      interpret: { type: 'verdict' }
    }
  ]
});

Choosing What Context to Pass

Feed the judge only what it needs to evaluate the criterion. Extra context adds noise and cost.

Failure ModeRequired VariablesNot Needed
Tone mismatchrequested_tone, blog_posttopic, input constraints
Off-topic drifttopic, blog_posttone, length requirements
Hallucinated claimsblog_post, source_materialtopic, tone
Faithfulnesssummary, original_documentformatting requirements
Missing requirementsrequirements_list, blog_posttopic (unless relevant)

Use context.ground_truth for expected values that vary per dataset. Use input.* for values from the workflow input. Use output.* for the workflow output being evaluated.

Show full SKILL.md (300 more words)Show less

judgeScore() Variant

Use judgeScore() when you need a numeric quality score rather than binary pass/fail. Apply the same four-component design.

.prompt file for scoring
# tests/evals/judge_quality@v1.prompt
---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-haiku-4-5-20251001
temperature: 0
maxOutputTokens: 1500
---

<system>
You are an evaluation judge. Score the overall writing quality of a blog post on a scale of 0.0 to 1.0.

## Scoring Criteria

- 0.0-0.3: Major issues — incoherent, riddled with errors, or completely off-topic
- 0.4-0.6: Mediocre — readable but has significant quality issues (poor structure, weak arguments, factual gaps)
- 0.7-0.8: Good — well-structured, clear, minor issues only
- 0.9-1.0: Excellent — polished, engaging, publication-ready

## Output Format

Return a JSON object with:
- "critique": Detailed analysis of quality strengths and weaknesses (3-5 sentences)
- "score": A number between 0.0 and 1.0
</system>

<user>
Topic: {{ topic }}

Blog title: {{ blog_title }}

Blog post:
{{ blog_post }}

Score the writing quality of this blog post.
</user>
Wiring to judgeScore()
typescript
export const checkQuality = verify(
  { name: 'check_quality', input: blogInput, output: blogOutput },
  async ({ input, output }) =>
    judgeScore({
      prompt: 'judge_quality@v1',
      variables: {
        topic: input.topic,
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

In the eval workflow, use interpret: { type: 'number' } with thresholds:

typescript
{
  evaluator: checkQuality,
  criticality: 'required',
  interpret: { type: 'number', pass: 0.7, partial: 0.4 }
}

judgeLabel() Variant

Use judgeLabel() when you need classification into named categories.

typescript
export const checkToneLabel = verify(
  { name: 'check_tone_label', input: blogInput, output: blogOutput },
  async ({ output }) =>
    judgeLabel({
      prompt: 'judge_tone_label@v1',
      variables: {
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

In the eval workflow, use interpret: { type: 'string' } with label lists:

typescript
{
  evaluator: checkToneLabel,
  criticality: 'informational',
  interpret: { type: 'string', pass: ['professional', 'formal'], partial: ['casual'] }
}

Model Selection

Run output-dev-model-selection to resolve each tier below to a current model ID.

TierWhen to UseCost
Smallest in family (speed/cost priority)Default for most judges. Fast, cheap, good at following structured instructions.Low
Mid-tier (balance priority)Complex reasoning required (faithfulness checking, multi-step logical analysis).Medium
Top-tier (reasoning priority)Only if mid-tier fails validation. Rarely needed.High

Always set temperature: 0 for judges. Reproducibility matters more than creativity.

Escalation strategy: start with the smallest tier. If the judge fails validation (TPR/TNR below 80%), move up one tier before rewriting the prompt — the model upgrade alone often fixes it.

Anti-Patterns

  • Vague criteria ("Is this good?") — Target one specific, observable failure mode
  • Holistic judges ("Rate overall quality on 5 dimensions") — One judge per failure mode
  • No few-shot examples — Always include pass, fail, and borderline examples
  • Likert scales (1-5 ratings) — Use binary pass/fail for verdict judges; use 0.0-1.0 with defined bands for score judges
  • Verdict before critique — Put critique first in the JSON schema to force reasoning
  • Skipping validation — Always validate judges against human labels (output-eval-validate-judge)
  • Kitchen-sink context — Pass only the variables the judge needs for its criterion
  • Few-shot from dev/test set — Only use training-split examples to avoid data leakage
  • output-eval-error-analysis — Identify the failure mode this judge targets
  • output-dev-eval-testing — Implementation reference for verify(), judgeVerdict(), evalWorkflow()
  • output-dev-prompt-file — .prompt file format, Liquid.js templating, provider configuration
  • output-eval-validate-judge — Validate this judge against human labels after writing it
  • output-eval-dataset-design — Generate diverse datasets for judge validation

© growthxai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in coding_assistants/claude/plugins/outputai/skills/output-eval-judge-prompt of growthxai/output.

Open the folder on GitHubat commit 99ee298

Compare with similar skills

Output Eval Judge Prompt next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Output Eval Judge Prompt

What does Output Eval Judge Prompt do?

Design effective LLM judge .prompt files for evaluators. An agent skill from growthxai/output. Output Eval Judge Prompt is an agent skill from growthxai/output.prompt files for evaluators.

When should I use Output Eval Judge Prompt?

Output Eval Judge Prompt fits situations like: creating judgeVerdict/judgeScore/judgeLabel prompts; existing judges produce unreliable results.

How do I install Output Eval Judge Prompt in Claude Code?

Run `npx skills add growthxai/output --skill output-eval-judge-prompt -a claude-code`. Or copy the skill folder (coding_assistants/claude/plugins/outputai/skills/output-eval-judge-prompt in growthxai/output) into .claude/skills/output-eval-judge-prompt in your project. Claude Code loads it when a task matches its description.

How do I install Output Eval Judge Prompt in Codex?

Run `npx skills add growthxai/output --skill output-eval-judge-prompt -a codex`. Or copy the skill folder (coding_assistants/claude/plugins/outputai/skills/output-eval-judge-prompt in growthxai/output) into .agents/skills/output-eval-judge-prompt in your project. Codex loads it when a task matches its description.

Can I use Output Eval Judge Prompt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add growthxai/output --skill output-eval-judge-prompt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/output-eval-judge-prompt, .gemini/skills/output-eval-judge-prompt, .github/skills/output-eval-judge-prompt and .opencode/skills/output-eval-judge-prompt in your project.

What does Output Eval Judge Prompt need to run?

SKILL.md names no scripts, command-line tools or credentials: Output Eval Judge Prompt is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit.

Does Output Eval Judge Prompt access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Output Eval Judge Prompt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Output Eval Judge Prompt use?

Output Eval Judge Prompt is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Output Eval Judge Prompt use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Output Eval Judge Prompt?

Skills that share tags, products or a category with Output Eval Judge Prompt: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars), Looper (ksimback/looper, 710 stars) and Agent Eval Engineering (langchain-ai/langchain-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Output Eval Judge Prompt?

growthxai (a GitHub organization) maintains it in growthxai/output, which has 442 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 9, 2026.

Source: growthxai/output on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.