Conciseness

Mostly lean with tables, formulas, and executable code rather than lectures on known concepts, though the bootstrap CI listing and Practical Guidance sections have a few lines that could be trimmed.

Actionability

Provides copy-paste-ready executable code (sklearn splits, confusion_matrix, bootstrap function, judgy usage) and concrete formulas covering the common calibration cases.

Workflow Clarity

Steps 1-8 are explicitly sequenced with validation checkpoints (disagreement inspection, TPR/TNR stopping criteria, test-set-run-once discipline) and an iterate-rerun feedback loop, so the batch-operation cap does not apply.

Progressive Disclosure

Well-organized into clear sections with no nested references and no navigation problems, but the ~150-line single file inlines the long bootstrap CI implementation that could reasonably live in a separate reference file.

Specificity

Names the domain and several concrete actions ('data splits, TPR/TNR, and bias correction'), but omits core calibration activities like prompt iteration and few-shot selection, leaving minor coverage gaps.

Completeness

Explicitly states both what ('Calibrate an LLM judge against human labels...') and when ('Use after writing a judge prompt... when you need to verify alignment'), plus a concrete negative boundary.

Trigger Term Quality

Includes natural technical triggers ('LLM judge', 'human labels', 'verify alignment', 'judge prompt') with good coverage, though a few common synonyms or phrasings a user might say are missing.

Distinctiveness Conflict Risk

Occupies a clear niche (LLM judge calibration) with distinct triggers and an explicit 'Do NOT use for code-based evaluators' boundary that minimizes conflict risk.