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 Pixel-Process-UG/superkit-agents --skill llm-as-judgegit clone --depth 1 https://github.com/Pixel-Process-UG/superkit-agentsWrote 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/pixel-process-ug/superkit-agents/llm-as-judge)<a href="https://agentmods.dev/skills/pixel-process-ug/superkit-agents/llm-as-judge"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/llm-as-judge/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/pixel-process-ug/superkit-agents/llm-as-judge"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/llm-as-judge.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 | $0.00067 | $0.02427 |
| Opus 5.5 | $0.00027 | $0.00971 |
| Sonnet 5.5 | $0.00013 | $0.00485 |
| Haiku 4.5 | $0.00007 | $0.00243 |
Grade A, and why
llm-as-judge 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 1mo 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM-as-Judge
Overview
Some quality criteria are inherently subjective — tone of voice, visual aesthetics, UX feel, documentation clarity, code readability. These cannot be verified by deterministic tests. The LLM-as-judge pattern provides structured, repeatable evaluation using an LLM reviewer with defined rubrics, ensuring subjective quality is measured consistently.
Announce at start: "I'm using the llm-as-judge skill to evaluate subjective quality."
Phase 1: Determine Evaluation Method
Goal: Decide whether LLM-as-judge is the right tool.
Decision Table: LLM-as-Judge vs Deterministic Tests
| Criterion Type | Method | Example |
|---|---|---|
| Objective, measurable | Deterministic test | "Response time < 200ms" |
| Structural, verifiable | Deterministic test | "Returns valid JSON" |
| Subjective, qualitative | LLM-as-judge | "Error messages are friendly and helpful" |
| Aesthetic, perceptual | LLM-as-judge | "UI feels clean and modern" |
| Linguistic, tonal | LLM-as-judge | "Documentation is clear for beginners" |
| Holistic, experiential | LLM-as-judge | "The onboarding flow feels intuitive" |
Rule of thumb: If you can write a boolean assertion, use a deterministic test. If evaluation requires judgment, use LLM-as-judge.
STOP — Do NOT proceed to Phase 2 until:
- Confirmed that criteria are genuinely subjective
- Deterministic testing has been ruled out
- Specific artifacts to evaluate are identified
Phase 2: Define Rubric
Goal: Create evaluation dimensions with weights and anchor points.
Actions
- Define 3-5 evaluation dimensions
- Assign weights (must sum to 1.0)
- Define anchor points for each dimension (1=worst, 5=adequate, 10=best)
- Set passing threshold
Rubric Structure
| Dimension | Weight | Scale | Anchor: 1 | Anchor: 5 | Anchor: 10 |
|---|---|---|---|---|---|
| [Name] | 0.X | 1-10 | [Worst case] | [Adequate] | [Excellent] |
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.
- 1mo ago First seen · 246 lines · 67 tokens per session scan A 2bf0cabf0636
llm-as-judge is a skill published in the GitHub repository Pixel-Process-UG/superkit-agents (3 stars, last pushed 7mo ago), licensed MIT. It adds 67 tokens to every session and 2,427 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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