Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add marchatton/agent-skills --skill write-judge-promptgit clone --depth 1 https://github.com/marchatton/agent-skillsWrote 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/marchatton/agent-skills/write-judge-prompt)<a href="https://agentmods.dev/skills/marchatton/agent-skills/write-judge-prompt"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/write-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/marchatton/agent-skills/write-judge-prompt"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/write-judge-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00082 | $0.01608 |
| Opus 5 | $0.00041 | $0.00804 |
| Sonnet 5 | $0.00016 | $0.00322 |
| Haiku 4.5 | $0.00008 | $0.00161 |
Grade A, and why
write-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.
This is a copy
100% identical to write-judge-prompt — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write LLM-as-Judge Prompt
Design a binary Pass/Fail LLM-as-Judge evaluator for one specific failure mode. Each judge checks exactly one thing.
Prerequisites
- Error analysis is complete. The failure mode is identified.
- You have human-labeled traces for this failure mode (at least 20 Pass and 20 Fail examples).
- A code-based evaluator cannot check this failure mode. Exhaust code-based options before reaching for a judge — many failure modes that seem subjective reduce to keyword checks, regex, or API calls when you understand the domain. Example: detecting whether an AI interviewing coach suggests "general" questions (asking about typical behavior instead of a specific past event) seems to require semantic understanding, but in practice a keyword check for words like "usually," "typical," and "normally" could work quite well.
The Four Components
Every judge prompt requires exactly four components:
1. Task and Evaluation Criterion
State what the judge evaluates. One failure mode per judge.
You are an evaluator assessing whether a real estate assistant's email
uses the appropriate tone for the client's persona.
Not: "Evaluate whether the email is good" or "Rate the email quality from 1-5."
2. Pass/Fail Definitions
Outcomes are strictly binary: Pass or Fail. No Likert scales, no letter grades, no partial credit. Define exactly what constitutes Pass and Fail. These definitions come from your error analysis failure mode descriptions.
## Definitions
PASS: The email matches the expected communication style for the client persona:
- Luxury Buyers: formal language, emphasis on exclusive features, premium
market positioning, no casual slang
- First-Time Homebuyers: warm and encouraging tone, educational explanations,
avoids jargon, patient and supportive
- Investors: data-driven language, ROI-focused, market analytics, concise
and professional
FAIL: The email uses a tone mismatched to the client persona. Examples:
- Using casual slang ("hey, check out this pad!") for a luxury buyer
- Using heavy financial jargon for a first-time homebuyer
- Using overly emotional language for an investor
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 First seen · 145 lines · 82 tokens per session scan A aa28d26c731e
write-judge-prompt is a skill published in the GitHub repository marchatton/agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 82 tokens to every session and 1,608 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to write-judge-prompt, differing in 0 lines, and is treated as a copy.
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