| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 978 | | totalAiIsmAdverbs | 2 | | 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 | 978 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "maw" | | 1 | "echoed" | | 2 | "scanned" | | 3 | "chilling" | | 4 | "oppressive" | | 5 | "pulsed" | | 6 | "vibrated" | | 7 | "familiar" | | 8 | "weight" | | 9 | "traced" | | 10 | "standard" | | 11 | "fractured" | | 12 | "echo" | | 13 | "echoing" | | 14 | "gloom" | | 15 | "resolved" | | 16 | "rhythmic" | | 17 | "shimmered" | | 18 | "fragmented" | | 19 | "peril" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | 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 | 980 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 11 | | wordCount | 961 | | uniqueNames | 8 | | maxNameDensity | 0.21 | | worstName | "Harlow" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 2 | | Quinn | 2 | | Morris | 2 | | Tube | 1 | | Stalls | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Stalls" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like the centre of their web" |
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| 97.96% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.02 | | wordCount | 980 | | matches | | 0 | "not of the city above, but of something subterranean, something breathing" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 9.81% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 98 | | std | 18.07 | | cv | 0.184 | | sampleLengths | | 0 | 81 | | 1 | 87 | | 2 | 116 | | 3 | 86 | | 4 | 94 | | 5 | 87 | | 6 | 98 | | 7 | 76 | | 8 | 124 | | 9 | 131 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 141 | | matches | | 0 | "was forcing" | | 1 | "was stepping" |
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| 74.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 83 | | ratio | 0.024 | | matches | | 0 | "She took another step down, the beam of her torch catching a detail on the wall – a smear of something dark, viscous, and undoubtedly ancient." | | 1 | "The stories of DS Morris’s final case, the unexplained residue, the fractured accounts – they all resurfaced, a chilling echo in the echoing descent." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 756 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sickly-sweet odour." |
| | adverbCount | 26 | | adverbRatio | 0.03439153439153439 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006613756613756613 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 11.81 | | std | 7.39 | | cv | 0.625 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 11 | | 3 | 16 | | 4 | 18 | | 5 | 23 | | 6 | 3 | | 7 | 18 | | 8 | 9 | | 9 | 18 | | 10 | 11 | | 11 | 5 | | 12 | 12 | | 13 | 5 | | 14 | 9 | | 15 | 6 | | 16 | 25 | | 17 | 4 | | 18 | 3 | | 19 | 12 | | 20 | 20 | | 21 | 2 | | 22 | 4 | | 23 | 14 | | 24 | 12 | | 25 | 13 | | 26 | 11 | | 27 | 9 | | 28 | 4 | | 29 | 12 | | 30 | 3 | | 31 | 8 | | 32 | 14 | | 33 | 19 | | 34 | 13 | | 35 | 18 | | 36 | 30 | | 37 | 14 | | 38 | 7 | | 39 | 22 | | 40 | 6 | | 41 | 4 | | 42 | 5 | | 43 | 22 | | 44 | 2 | | 45 | 11 | | 46 | 8 | | 47 | 26 | | 48 | 6 | | 49 | 12 |
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| 61.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.39759036144578314 | | totalSentences | 83 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 81 | | matches | | 0 | "All that mattered was the" | | 1 | "Then she saw it: a" | | 2 | "Still felt like yesterday." | | 3 | "Too good, maybe." |
| | ratio | 0.049 | |
| 76.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 81 | | matches | | 0 | "Her worn leather watch was" | | 1 | "She took the wall in" | | 2 | "He was forcing something open." | | 3 | "She rounded a corner, the" | | 4 | "Her eyes scanned the grimy" | | 5 | "He’d chosen this." | | 6 | "She cursed under her breath," | | 7 | "Her partner, Morris, would have" | | 8 | "He’d gone into a place" | | 9 | "She gripped the cold, wet" | | 10 | "Her training screamed caution." | | 11 | "She pulled a small, hefty" | | 12 | "Her hand instinctively went to" | | 13 | "He knew these paths." | | 14 | "He belonged to the darkness." | | 15 | "Her mind flashed back to" | | 16 | "They were always in the" | | 17 | "She took another step down," | | 18 | "It wasn't simple dirt or" | | 19 | "It sounded like a market." |
| | ratio | 0.358 | |
| 77.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 81 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn shoved her" | | 2 | "Her worn leather watch was" | | 3 | "The suspect, a wiry man" | | 4 | "Quinn didn't hesitate." | | 5 | "She took the wall in" | | 6 | "Grime dripped from overflowing bins" | | 7 | "The air grew heavy, thick" | | 8 | "He was forcing something open." | | 9 | "She rounded a corner, the" | | 10 | "Her eyes scanned the grimy" | | 11 | "The suspect was gone." | | 12 | "He’d chosen this." | | 13 | "She cursed under her breath," | | 14 | "Her partner, Morris, would have" | | 15 | "He’d gone into a place" | | 16 | "Harlow approached the descent, the" | | 17 | "The rain seemed to fall" | | 18 | "The metallic tang was stronger," | | 19 | "She gripped the cold, wet" |
| | ratio | 0.765 | |
| 61.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 81 | | matches | | 0 | "To follow meant venturing into" |
| | ratio | 0.012 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "She took the wall in a single, powerful stride, landing with a splash that sent dark water arcing." | | 1 | "Far below, a faint, diffused light pulsed, an unnatural luminescence that hinted at more than just bare bulbs." | | 2 | "There was a murmur too, a low thrumming that vibrated up through the soles of her boots, a sound not of the city above, but of something subterranean, something…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |