Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/growthxai/outputnpx agentmods add skills/growthxai/output/output-eval-judge-promptWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/growthxai/output/output-eval-judge-prompt)<a href="https://agentmods.dev/skills/growthxai/output/output-eval-judge-prompt"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-eval-judge-prompt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/growthxai/output/output-eval-judge-prompt"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-eval-judge-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.02951 |
| Opus 5 | $0.00021 | $0.01476 |
| Sonnet 5 | $0.00008 | $0.00590 |
| Haiku 4.5 | $0.00004 | $0.00295 |
Grade A, and why
output-eval-judge-prompt scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Error analysis is complete — You have identified the specific failure mode this judge targets (from
output-eval-error-analysis) - 20+ labeled examples — At least 20 pass and 20 fail traces for this failure mode, with
ground_truthlabels in dataset YAML files - 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").
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago Changed bf5873a33889
- 13d ago First seen · 340 lines · 42 tokens per session scan A a242e05ac18c
output-eval-judge-prompt is a skill published in the GitHub repository growthxai/output (435 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 2,951 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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