---

name: design-ai-benchmarking description: Use when designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication. metadata: triggers: "AI benchmarking, AI vs human expert, reader study design, expert panel evaluation, LLM-as-judge, AI evaluation rubric, model benchmark design, human baseline comparison, AI-output rating, evaluation rubric design"

Design-AI-Benchmarking Skill

Standard Output

## AI-Benchmark Design Review
Evaluation question: ...
Arms / systems compared: ...
Reference (human-expert panel): ...
Unit of rating: (item / case / output)

### Rubric (decoupled dimensions)
- dimension -> construct -> anchors (1..k)

### Calibration probes (blinded, randomized)
- positive-control / known-bad / instability / mechanism-contradiction

### Reviewer panel
- n reviewers, metadata captured, per-reviewer randomized order

### Reliability plan
- IRR target on the anchor set of real items (ICC form + 95% CI) + control-item hit rate (a competence check, reported separately)

### Judge strategy
- human-as-judge / LLM-as-judge / both + adjudication rule

### Validity risks
1. ...

### Minimal fixes
- ...

### Decision
- Ready to collect / Needs rubric revision / Needs arm or judge redesign

Reviewer ratings, reference labels, probe outcomes and agreement statistics come only from collected rating records. Never invent them: a reported ICC, kappa or score with no underlying rating record is the failure this skill exists to prevent. Cite a reference only with a /search-lit-confirmed DOI or PMID; mark any other [UNVERIFIED - NEEDS MANUAL CHECK]. Flag an unconfirmed clinical definition, diagnostic criterion or guideline recommendation [VERIFY] and ask the user.

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Workflow

Phase 1: Define the evaluation question and arms

Pin down, in writing:

• the exact claim the benchmark must support (e.g., "system A's outputs are perceptually indistinguishable from expert outputs", not "system A is deployment-ready")
• every arm/system and what each receives as input (same items, same information access, same output format), so no arm has a hidden advantage
• the human-expert reference: who they are, and whether they set ground truth, form a comparison arm, or both
• the unit of rating (item, case, output) and how many units each reviewer sees

Gate: Present the reconstructed evaluation question, arms, and reference to the user and confirm before designing the rubric. A wrong reconstruction misdirects the entire benchmark.

Phase 2: Design a decoupled multi-dimensional rubric

• Decouple the axes. Each rated dimension measures one construct. Keep "is the output valid/correct" separate from "is it novel", "is it feasible/measurable", "does it add value over current tools", and "would it change action". A candidate can be high-validity yet low-added-value ("real but redundant"); a single blended score hides this.
• Anchor every scale point with a short verbal descriptor; pilot the anchors with at least one reviewer before locking.
• Pre-specify discriminant validity: hypothesize which dimensions should correlate vs be orthogonal, then report the full inter-dimension correlation matrix.
• Start from ${CLAUDE_SKILL_DIR}/references/elicitation_rubric_template.md (dimensions, anchors, probe flavors).

Phase 3: Insert and randomize calibration probes

Plant a few deliberate control items, blinded and randomized across raters (record who received which via a probe_arm flag), to anchor the scale, measure rater drift/fatigue, and audit the rubric and pipeline. Four flavors:

• Positive control / "too-good" item — near-tautological; tests whether raters equate "largest effect" with "best", and whether the construct-independence gate (Phase 7) works.
• Known-bad negative control — an engineered defect (fabricated reference, missing key statistic).
• Instability item — an estimate that reverses or fails to replicate on a holdout.
• Mechanism-contradiction item — an empirical direction that opposes the proposed mechanism.

Probes are planted or adjudicated, never fabricated to fit a hypothesis.

Phase 4: Construct the reviewer panel

• Recruit reviewers spanning the intended expertise gradient; pre-specify any stratification.
• Capture reviewer metadata (years of experience, prior AI-evaluation experience, subspecialty).
• Randomize item order per reviewer (not one global seed), record the order, and plan to analyze order and fatigue effects.
• Require each item to be judged standalone; cross-item references in free text signal non-independent rating.

Gate: Present the panel composition, stratification, and randomization plan for user review before recruitment is finalized.

Phase 5: Set inter-rater reliability targets

• Pre-specify the reliability statistic and its form — for several raters, an ICC with the model stated (e.g. ICC(2,1): two-way random, absolute agreement, single rater) or Krippendorff's α (ordinal); weighted (Cohen's) kappa is a two-rater statistic — and a justified target. Size the number of items for the CI width you need: ICC precision is driven mainly by the item count (Koo & Li, J Chiropr Med 2016).
• Planted control items are a rater-competence check, not reliability evidence. Report the hit rate on known-good vs known-bad controls. Do not present an ICC computed on the controls as evidence of rubric or scale validity: ICC scales with the spread between items, controls are extreme by construction, so their ICC is high whatever the raters' reliability on real items (same five raters, same noise: ICC(2,1) 0.89 on four planted controls, 0.16 on 40 real items), and it rests on a handful of items.
• Report reliability on the representative anchor set of real items — ICC with its 95% CI and stated form — with an agreement measure alongside (exact agreement, SEM), since reliability depends on how heterogeneous the rated items are (de Vet et al., J Clin Epidemiol 2006). A low ICC on real items with a good control hit rate points to little true spread among the items or a rubric that does not separate them, not to careless raters.
• Plan the minimum ratings-per-item for a stable agreement estimate (the math goes to /analyze-stats).

Phase 5b: Reader allocation under burden constraints (anchor-and-rotate)

When the item pool exceeds what one reader can rate in a session, do not make every reader rate every item (that caps the pool at the per-reader limit). Use anchor-and-rotate (an anchor set plus rotating incomplete blocks — not a balanced incomplete block design, since reader pairs are not co-rated equally often): all readers rate a shared anchor set of real items (which carries the inter-rater reliability; the planted controls carry only the competence check) plus a rotating unique block each. The binding constraint is usually the number of available expert readers, so solve the reverse problem (largest pool for R readers) to size the must-rate set. Pre-specify anchor membership, raters-per-item, and the rotation seed before rating. Read ${CLAUDE_SKILL_DIR}/references/anchor_rotate_reader_allocation.md for the formulas, trade-offs, and a stdlib implementation.

Phase 6: Choose the judge strategy and adjudication

• Decide human-as-judge, LLM-as-judge, or both. An LLM judge is one more arm whose ratings must be validated against the human panel on the anchor set of real items, not only on the extreme control items.
• Pre-specify the adjudication rule for disagreement (majority, a third senior reviewer, consensus discussion) and who adjudicates.
• Blind judges to arm identity wherever feasible; record any unavoidable unblinding.

Phase 7: Construct-independence and leakage guards

• Exclude any predictor or input that is a definitional component of the outcome, and flag near-tautological composites built from the outcome's defining components — they produce an inflated, near-circular result and belong as labeled probes, not discoveries.
• Verify no arm sees post-decision or outcome-derived information the others do not.
• Confirm the reference labels were not derived from the same model output being evaluated.

Phase 8: Lock a structured export schema

Write the machine-readable rating record as a JSON schema, starting from ${CLAUDE_SKILL_DIR}/references/benchmark_export_schema.json: per-item ratings on every rubric dimension, free-text justifications, follow-up flags, the probe_arm flag, reviewer id and metadata, item order, and timing.

Gate: Present the final rubric, probe set, panel plan, judge strategy, and export schema together; collect explicit user approval before any rating begins, because changes after collection starts compromise the comparison.

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Handoff Rules

• route to /analyze-stats for ICC (stated form) / Krippendorff's α / kappa (two raters) / DeLong, agreement sample size, and effect-size real-world translation of the benchmark results
• route to /check-reporting for STARD-AI, CLAIM, or TRIPOD+AI item-level reporting once the design is locked
• route to /design-study when the broader study around the benchmark (cohort logic, analysis unit, comparator) also needs review
• route to /peer-review or /self-review only after ratings exist and a manuscript is being assessed