| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Ives scoffed lightly [lightly]" |
| | dialogueSentences | 40 | | tagDensity | 0.475 | | leniency | 0.95 | | rawRatio | 0.053 | | effectiveRatio | 0.05 | |
| 96.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1312 | | 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) | |
| 61.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1312 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "gloom" | | 1 | "maw" | | 2 | "perfect" | | 3 | "etched" | | 4 | "pristine" | | 5 | "echoed" | | 6 | "profound" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 97 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1312 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 807 | | uniqueNames | 6 | | maxNameDensity | 1.61 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Tube | 2 | | Constable | 1 | | Ives | 8 | | Victorian | 1 |
| | persons | | | places | (empty) | | globalScore | 0.695 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1312 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 41 | | std | 24.84 | | cv | 0.606 | | sampleLengths | | 0 | 75 | | 1 | 21 | | 2 | 42 | | 3 | 61 | | 4 | 17 | | 5 | 34 | | 6 | 3 | | 7 | 55 | | 8 | 52 | | 9 | 39 | | 10 | 47 | | 11 | 16 | | 12 | 12 | | 13 | 57 | | 14 | 34 | | 15 | 57 | | 16 | 65 | | 17 | 9 | | 18 | 16 | | 19 | 84 | | 20 | 32 | | 21 | 6 | | 22 | 42 | | 23 | 59 | | 24 | 42 | | 25 | 12 | | 26 | 67 | | 27 | 97 | | 28 | 40 | | 29 | 2 | | 30 | 38 | | 31 | 79 |
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| 90.79% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 97 | | matches | | 0 | "been sealed" | | 1 | "were curled" | | 2 | "been murdered" | | 3 | "was drawn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 129 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 811 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.02836004932182491 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007398273736128237 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 11.12 | | std | 9.27 | | cv | 0.834 | | sampleLengths | | 0 | 7 | | 1 | 20 | | 2 | 16 | | 3 | 9 | | 4 | 23 | | 5 | 17 | | 6 | 4 | | 7 | 11 | | 8 | 16 | | 9 | 4 | | 10 | 11 | | 11 | 20 | | 12 | 15 | | 13 | 12 | | 14 | 14 | | 15 | 8 | | 16 | 9 | | 17 | 2 | | 18 | 8 | | 19 | 5 | | 20 | 7 | | 21 | 7 | | 22 | 5 | | 23 | 3 | | 24 | 41 | | 25 | 14 | | 26 | 8 | | 27 | 5 | | 28 | 9 | | 29 | 3 | | 30 | 10 | | 31 | 7 | | 32 | 8 | | 33 | 1 | | 34 | 1 | | 35 | 7 | | 36 | 21 | | 37 | 11 | | 38 | 4 | | 39 | 17 | | 40 | 10 | | 41 | 16 | | 42 | 5 | | 43 | 11 | | 44 | 12 | | 45 | 7 | | 46 | 15 | | 47 | 5 | | 48 | 22 | | 49 | 1 |
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| 65.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.423728813559322 | | totalSentences | 118 | | uniqueOpeners | 50 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Somewhere in the dark, water" | | 1 | "Too relaxed for a man" |
| | ratio | 0.023 | |
| 45.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 87 | | matches | | 0 | "Her torch beam cut a" | | 1 | "He was supine, arms outstretched," | | 2 | "His skin held a waxy," | | 3 | "She swept her torch across" | | 4 | "She leaned closer." | | 5 | "She looked at the chalk" | | 6 | "It was drawn with a" | | 7 | "He pointed to a small" | | 8 | "It pointed, resolutely, toward the" | | 9 | "She had no obvious cause" | | 10 | "She didn't believe in cults." | | 11 | "She believed in evidence." | | 12 | "She believed in the ugly," | | 13 | "She’d seen enough of it." | | 14 | "She walked the perimeter of" | | 15 | "She looked at the wall" | | 16 | "She turned back to the" | | 17 | "She walked over to the" | | 18 | "She stepped through the hole" | | 19 | "It was a short passage," |
| | ratio | 0.437 | |
| 17.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 87 | | matches | | 0 | "The air tasted of rust" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "Her torch beam cut a" | | 3 | "Dust motes danced in the" | | 4 | "Detective Constable Ives stood near" | | 5 | "Quinn’s boots crunched on grit" | | 6 | "The station had been sealed" | | 7 | "The walls sweated damp." | | 8 | "The body lay in the" | | 9 | "A man, mid-thirties, dressed in" | | 10 | "He was supine, arms outstretched," | | 11 | "A woman, one of the" | | 12 | "Ives supplied, his voice tight" | | 13 | "The man’s eyes were wide," | | 14 | "His skin held a waxy," | | 15 | "She swept her torch across" | | 16 | "The chalk was perfect, unbroken." | | 17 | "Ives gestured to the far" | | 18 | "Quinn pointed her torch at" | | 19 | "The fingers were curled, relaxed." |
| | ratio | 0.885 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | 0 | "To her left, the platform" |
| | ratio | 0.011 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "The body lay in the centre of the old ticket hall, beneath a vaulted ceiling that swallowed her light whole." | | 1 | "The compass had a verdigris patina, its face etched with symbols that weren't any language she knew." |
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| 19.74% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 4 | | matches | | 0 | "Detective Constable Ives stood, his face pale in the torchlight" | | 1 | "Ives supplied, his voice tight" | | 2 | "Quinn repeated, her tone flat" | | 3 | "Quinn said, the pieces clicking into place" |
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| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "she ordered (order)" | | 1 | "Quinn repeated (repeat)" | | 2 | "Quinn murmured (murmur)" |
| | dialogueSentences | 40 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.375 | | effectiveRatio | 0.15 | |