| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 939 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 939 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "reminder" | | 1 | "sanctuary" | | 2 | "weight" | | 3 | "scanned" | | 4 | "pulsed" | | 5 | "silk" | | 6 | "vibrated" | | 7 | "silence" | | 8 | "glint" | | 9 | "raced" | | 10 | "etched" | | 11 | "whisper" | | 12 | "velvet" | | 13 | "ensure" | | 14 | "racing" | | 15 | "intensity" | | 16 | "chilling" | | 17 | "furrowed" | | 18 | "flicked" | | 19 | "flicker" | | 20 | "predator" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 69 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 931 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.27% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 741 | | uniqueNames | 9 | | maxNameDensity | 1.21 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Silas | 3 | | Moreau | 2 | | Evan | 1 | | Ptolemy | 2 | | Escape | 1 | | Thames | 1 | | Aurora | 9 | | Lucien | 6 |
| | persons | | 0 | "Carter" | | 1 | "Silas" | | 2 | "Moreau" | | 3 | "Evan" | | 4 | "Ptolemy" | | 5 | "Aurora" | | 6 | "Lucien" |
| | places | | | globalScore | 0.893 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 931 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 81 | | matches | | |
| 94.98% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 46.55 | | std | 22.46 | | cv | 0.482 | | sampleLengths | | 0 | 91 | | 1 | 84 | | 2 | 30 | | 3 | 80 | | 4 | 32 | | 5 | 49 | | 6 | 36 | | 7 | 68 | | 8 | 34 | | 9 | 66 | | 10 | 18 | | 11 | 65 | | 12 | 64 | | 13 | 15 | | 14 | 26 | | 15 | 29 | | 16 | 32 | | 17 | 38 | | 18 | 24 | | 19 | 50 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 113 | | matches | | |
| 37.04% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 81 | | ratio | 0.037 | | matches | | 0 | "The flat above Silas’s bar was a cramped sanctuary—bookshelves groaning under the weight of occult texts and legal tomes, the air thick with the tang of copper and something else, something ancient." | | 1 | "His heterochromatic gaze—one amber, one black—scanned the room with unsettling precision." | | 2 | "The scar on her wrist was a constant reminder—a souvenir from a childhood accident, yes, but also a symbol of the cage he’d tried to build around her." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 749 | | adjectiveStacks | 2 | | stackExamples | | 0 | "primal, washed over her." | | 1 | "small, velvet-lined box." |
| | adverbCount | 15 | | adverbRatio | 0.020026702269692925 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00801068090787717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 11.49 | | std | 6.51 | | cv | 0.566 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 9 | | 3 | 32 | | 4 | 17 | | 5 | 25 | | 6 | 11 | | 7 | 3 | | 8 | 17 | | 9 | 9 | | 10 | 9 | | 11 | 10 | | 12 | 13 | | 13 | 12 | | 14 | 5 | | 15 | 5 | | 16 | 12 | | 17 | 8 | | 18 | 19 | | 19 | 8 | | 20 | 28 | | 21 | 12 | | 22 | 8 | | 23 | 12 | | 24 | 9 | | 25 | 21 | | 26 | 15 | | 27 | 4 | | 28 | 16 | | 29 | 18 | | 30 | 2 | | 31 | 9 | | 32 | 3 | | 33 | 12 | | 34 | 7 | | 35 | 5 | | 36 | 20 | | 37 | 12 | | 38 | 7 | | 39 | 12 | | 40 | 15 | | 41 | 2 | | 42 | 8 | | 43 | 3 | | 44 | 2 | | 45 | 17 | | 46 | 13 | | 47 | 9 | | 48 | 12 | | 49 | 16 |
| |
| 56.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.38271604938271603 | | totalSentences | 81 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 62.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 66 | | matches | | 0 | "She’d been expecting the landlord’s" | | 1 | "His heterochromatic gaze—one amber, one" | | 2 | "She’d fled here, to this" | | 3 | "he said, his voice a" | | 4 | "She didn’t move, didn’t speak." | | 5 | "She remembered the nights they’d" | | 6 | "he continued, stepping further into" | | 7 | "His polished shoes clicked on" | | 8 | "Her voice was cool, controlled," | | 9 | "He leaned against the doorframe," | | 10 | "His eyes, the amber one" | | 11 | "she lied, her gaze flicking" | | 12 | "he murmured, stepping closer" | | 13 | "She’d found it, buried in" | | 14 | "She’d hidden it in the" | | 15 | "He’d never understood that some" | | 16 | "she asked, her voice barely" | | 17 | "he replied, his voice dropping" | | 18 | "He reached into his tailored" | | 19 | "she asked, her voice steady" |
| | ratio | 0.394 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 66 | | matches | | 0 | "The brass knocker hammered a" | | 1 | "Aurora Carter, half-asleep on the" | | 2 | "She’d been expecting the landlord’s" | | 3 | "The flat above Silas’s bar" | | 4 | "Ptolemy, the tabby cat, flattened" | | 5 | "Lucien Moreau stood framed in" | | 6 | "His heterochromatic gaze—one amber, one" | | 7 | "Aurora’s breath hitched." | | 8 | "The scar on her left" | | 9 | "She’d fled here, to this" | | 10 | "he said, his voice a" | | 11 | "She didn’t move, didn’t speak." | | 12 | "The silence stretched, thick and" | | 13 | "The flat felt smaller, the" | | 14 | "She remembered the nights they’d" | | 15 | "The promises he’d made, the" | | 16 | "The scar on her wrist" | | 17 | "he continued, stepping further into" | | 18 | "His polished shoes clicked on" | | 19 | "Aurora’s fingers tightened on the" |
| | ratio | 0.924 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | 0 | "Now, the past had returned," |
| | ratio | 0.015 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "She’d found it, buried in silt, its surface etched with symbols that made her blood run cold." | | 1 | "Lucien’s eyes held hers, the amber one burning with an intensity that made her skin flush." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 7 | | matches | | 0 | "he said, his voice a low rumble that vibrated through the floorboards" | | 1 | "she asked, her voice barely a whisper" | | 2 | "he replied, his voice dropping to a conspiratorial murmur" | | 3 | "she asked, her voice steady despite the tremor in her hands" | | 4 | "he said, his voice smooth as silk" | | 5 | "she murmured, her voice low and dangerous" | | 6 | "he whispered, his breath warm against her ear" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 5 | | fancyTags | | 0 | "he continued (continue)" | | 1 | "she lied (lie)" | | 2 | "he murmured (murmur)" | | 3 | "she murmured (murmur)" | | 4 | "he whispered (whisper)" |
| | dialogueSentences | 26 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0.5 | | effectiveRatio | 0.385 | |