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Llm As Judge

  • 71 installs
  • 1 repo stars
  • Updated March 16, 2026
  • pixel-process-ug/superkit-agents

Helps with ai & agent building tasks.

About

llm-as-judge is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • llm-as-judge
  • AI & Agent Building
  • AI-coding skill

Llm As Judge by the numbers

  • 71 all-time installs (skills.sh)
  • +2 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #5,673 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/pixel-process-ug/superkit-agents --skill llm-as-judge

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Installs71
repo stars1
Last updatedMarch 16, 2026
Repositorypixel-process-ug/superkit-agents

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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 TypeMethodExample
Objective, measurableDeterministic test"Response time < 200ms"
Structural, verifiableDeterministic test"Returns valid JSON"
Subjective, qualitativeLLM-as-judge"Error messages are friendly and helpful"
Aesthetic, perceptualLLM-as-judge"UI feels clean and modern"
Linguistic, tonalLLM-as-judge"Documentation is clear for beginners"
Holistic, experientialLLM-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

1. Define 3-5 evaluation dimensions 2. Assign weights (must sum to 1.0) 3. Define anchor points for each dimension (1=worst, 5=adequate, 10=best) 4. Set passing threshold

Rubric Structure

DimensionWeightScaleAnchor: 1Anchor: 5Anchor: 10
[Name]0.X1-10[Worst case][Adequate][Excellent]

Threshold Selection Table

Quality LevelThresholdUse For
Minimum viable5.0Internal docs, draft content
Production quality7.0User-facing content, public APIs
Excellence8.5Marketing, critical UX flows

STOP — Do NOT proceed to Phase 3 until:

  • [ ] 3-5 dimensions are defined with clear descriptions
  • [ ] Weights sum to exactly 1.0
  • [ ] Anchor points are specific (not vague)
  • [ ] Passing threshold is set before evaluation

---

Phase 3: Evaluate

Goal: Submit the artifact with rubric to an LLM reviewer.

Review Request Structure

{
  criteria: "Description of what to evaluate and the quality standard",
  artifact: "The content to be evaluated (code, text, UI markup, etc.)",
  rubric: [
    { dimension: "Clarity", weight: 0.3, description: "Is the content easy to understand?" },
    { dimension: "Tone", weight: 0.3, description: "Is the tone appropriate for the audience?" },
    { dimension: "Completeness", weight: 0.2, description: "Does it cover all necessary points?" },
    { dimension: "Engagement", weight: 0.2, description: "Does it hold the reader's interest?" }
  ],
  passing_threshold: 7.0,
  intelligence: "opus"
}

Review Response Structure

{
  scores: [
    { dimension: "Clarity", score: 8, reasoning: "Well-structured with clear headings..." },
    { dimension: "Tone", score: 7, reasoning: "Professional but occasionally too formal..." },
    { dimension: "Completeness", score: 9, reasoning: "Covers all key topics..." },
    { dimension: "Engagement", score: 6, reasoning: "Could use more examples..." }
  ],
  weighted_score: 7.4,
  pass: true,
  summary: "Overall good quality with minor tone and engagement improvements suggested.",
  suggestions: [
    "Add a real-world example in section 3",
    "Use more conversational language in the introduction"
  ]
}

STOP — Do NOT proceed to Phase 4 until:

  • [ ] Artifact has been submitted with full rubric
  • [ ] Each dimension has been scored independently
  • [ ] Reasoning is provided for every score
  • [ ] Weighted total is calculated

---

Phase 4: Iterate or Accept

Goal: Act on the evaluation results.

Result Action Table

ResultActionMax Iterations
Pass (score >= threshold)Accept the artifact, proceedDone
Marginal fail (within 1 point)Apply suggestions, re-evaluate once1
Clear fail (> 1 point below)Significant revision, apply all suggestions2
Repeated fail (3+ attempts)Escalate — rubric or approach may need adjustmentEscalate

STOP — Evaluation complete when:

  • [ ] Artifact passes threshold, OR
  • [ ] 3 iterations completed and escalation decision made

---

Common Rubric Templates

Documentation Quality

Clarity (0.3): Is the content easy to understand for the target audience?
  1=incomprehensible  5=adequate but requires re-reading  10=crystal clear
Accuracy (0.3): Is the information technically correct?
  1=factually wrong  5=mostly correct  10=perfectly accurate
Completeness (0.2): Does it cover all necessary topics?
  1=missing critical info  5=covers basics  10=comprehensive
Examples (0.2): Are there sufficient, relevant code examples?
  1=no examples  5=some examples  10=rich, varied examples
Threshold: 7.0

Error Message Quality

Helpfulness (0.4): Does the message help the user fix the problem?
  1=no help at all  5=vague direction  10=exact fix steps
Clarity (0.3): Is the message easy to understand?
  1=cryptic  5=understandable  10=immediately clear
Tone (0.2): Is the tone empathetic and non-blaming?
  1=hostile/blaming  5=neutral  10=empathetic and supportive
Actionability (0.1): Does it suggest a concrete next step?
  1=no suggestion  5=vague suggestion  10=specific actionable step
Threshold: 7.5

Code Readability

Naming (0.3): Are variable/function names descriptive and consistent?
  1=single letters everywhere  5=adequate  10=self-documenting
Structure (0.3): Is the code logically organized?
  1=spaghetti  5=functional  10=elegant and clear
Simplicity (0.2): Is the code as simple as possible (but not simpler)?
  1=over-engineered  5=reasonable  10=minimal and clear
Documentation (0.2): Are complex sections adequately commented?
  1=no comments where needed  5=some comments  10=well-documented why
Threshold: 7.0

UX Copy

Clarity (0.3): Is the copy easy to understand?
  1=confusing  5=understandable  10=immediately clear
Brevity (0.2): Is it concise without losing meaning?
  1=verbose  5=adequate length  10=perfectly concise
Tone (0.2): Does it match the brand voice?
  1=off-brand  5=neutral  10=perfectly on-brand
Actionability (0.2): Do CTAs clearly communicate what happens next?
  1=unclear  5=adequate  10=crystal clear action
Accessibility (0.1): Is the language inclusive and jargon-free?
  1=exclusionary  5=neutral  10=fully inclusive
Threshold: 7.5

---

Anti-Patterns / Common Mistakes

Anti-PatternWhy It Is WrongCorrect Approach
Using LLM-as-judge for measurable criteriaWastes tokens, less reliable than assertionsUse deterministic tests for anything quantifiable
Vague rubric dimensions ("is it good?")Produces unreliable, inconsistent scoresSpecific dimensions with anchored examples
No passing threshold definedNo way to determine pass/fail objectivelyAlways set threshold before evaluation
Adjusting rubric to pass failing contentDefeats the purpose of quality gatesFix the content, not the rubric
Single evaluation without reasoningCannot improve without understanding whyAlways require per-dimension reasoning
Using weaker model for evaluationLower quality judgmentsUse strongest available model (Opus)
Skipping re-evaluation after changesNo verification that changes improved qualityAlways re-evaluate after revisions

---

Integration Points

SkillRelationship
acceptance-testingLLM-as-judge handles subjective acceptance criteria
spec-writingSpecs may include subjective quality criteria
code-reviewReadability evaluation during code review
verification-before-completionSubjective validation gate before completion
senior-prompt-engineerPrompt quality evaluation uses LLM-as-judge
tech-docs-generatorDocumentation quality evaluation

---

Downstream Steering Pattern

+----------+     +----------+     +----------+     +----------+
|  SPECS   |---->|   CODE   |---->|  TESTS   |---->| LLM-AS-  |
|          |     |          |     |(determin)|     |  JUDGE   |
|          |     |          |     |          |     |(subject) |
+----------+     +----------+     +----------+     +----+-----+
                      ^                                  |
                      |          backpressure             |
                      +----------------------------------+

Deterministic tests validate objective criteria. LLM-as-judge validates subjective criteria. Both must pass.

---

Skill Type

FLEXIBLE — Adapt rubric dimensions and thresholds to context. The pattern structure (define rubric, evaluate, score, iterate) is fixed. Always set the threshold before evaluation, never after.

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