| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1035 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1035 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1050 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 976 | | uniqueNames | 27 | | maxNameDensity | 0.61 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 2 | | Greek | 1 | | Street | 1 | | Herrera | 6 | | Raven | 1 | | Nest | 1 | | Thames | 1 | | Met | 1 | | Dispatch | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Playbill | 1 | | Quinn | 5 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Saint | 1 | | Christopher | 1 | | Charing | 1 | | Cross | 1 | | Euston | 1 | | Camden | 2 | | Morris | 3 | | Tube | 2 | | Underground | 1 | | Spanish | 1 | | London | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Met" | | 3 | "Dispatch" | | 4 | "Quinn" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Cross" | | 8 | "Morris" | | 9 | "Underground" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Thames" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Road" | | 9 | "Charing" | | 10 | "Euston" | | 11 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 1050 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 35 | | std | 29.76 | | cv | 0.85 | | sampleLengths | | 0 | 59 | | 1 | 46 | | 2 | 6 | | 3 | 4 | | 4 | 79 | | 5 | 68 | | 6 | 27 | | 7 | 15 | | 8 | 80 | | 9 | 84 | | 10 | 12 | | 11 | 69 | | 12 | 6 | | 13 | 90 | | 14 | 16 | | 15 | 2 | | 16 | 71 | | 17 | 8 | | 18 | 63 | | 19 | 16 | | 20 | 7 | | 21 | 6 | | 22 | 6 | | 23 | 67 | | 24 | 5 | | 25 | 51 | | 26 | 41 | | 27 | 17 | | 28 | 6 | | 29 | 23 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 79 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 162 | | matches | | 0 | "were going" | | 1 | "were thinning" | | 2 | "was heading" | | 3 | "was heading" | | 4 | "was threading" | | 5 | "was making" | | 6 | "was leaking" | | 7 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 85 | | ratio | 0.106 | | matches | | 0 | "Quinn was already three streets behind when she caught the flash of olive skin under a streetlamp — a man ducking into a doorway on Greek Street, one hand pressed to his ribs, the other clutching something small enough to hide in a fist." | | 1 | "Forty-one years old and she still ran like she had at twenty-three, the way the Met had taught her, the way the army had taught her before that — elbows tight, chin down, eyes fixed a half-second ahead of where her feet were going." | | 2 | "Fast and favoring his right side — that old knife scar, maybe, or something fresher she hadn't seen yet." | | 3 | "She lost him once — a bad second where the rain thickened and the crowds parted around some street performer and her heart climbed up into her throat — and then she caught him again, a pale flash of Saint Christopher catching streetlamp as he turned." | | 4 | "She felt the old ache in her left knee — that case three years ago, the case, the one where Morris had gone through a door and not come back." | | 5 | "He'd ducked down an iron-railed stairwell behind a shuttered kebab shop — one of those sealed-up Tube entrances the Underground had boarded over decades ago and the council had forgotten about." | | 6 | "His face — she'd only ever seen it in photographs." | | 7 | "He laughed — one short broken sound, almost a sob." | | 8 | "The door swung wider behind him, and Quinn saw — for an instant, before it began to close — a staircase spiraling down farther than any Tube station had any right to." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 971 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.029866117404737384 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0020597322348094747 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 12.35 | | std | 11.39 | | cv | 0.922 | | sampleLengths | | 0 | 15 | | 1 | 44 | | 2 | 2 | | 3 | 5 | | 4 | 8 | | 5 | 7 | | 6 | 24 | | 7 | 6 | | 8 | 4 | | 9 | 24 | | 10 | 44 | | 11 | 8 | | 12 | 3 | | 13 | 3 | | 14 | 19 | | 15 | 38 | | 16 | 8 | | 17 | 22 | | 18 | 5 | | 19 | 7 | | 20 | 8 | | 21 | 26 | | 22 | 25 | | 23 | 19 | | 24 | 6 | | 25 | 4 | | 26 | 2 | | 27 | 4 | | 28 | 11 | | 29 | 46 | | 30 | 8 | | 31 | 2 | | 32 | 3 | | 33 | 8 | | 34 | 5 | | 35 | 7 | | 36 | 20 | | 37 | 4 | | 38 | 19 | | 39 | 10 | | 40 | 16 | | 41 | 1 | | 42 | 5 | | 43 | 16 | | 44 | 30 | | 45 | 26 | | 46 | 2 | | 47 | 2 | | 48 | 14 | | 49 | 16 |
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| 73.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.47058823529411764 | | totalSentences | 85 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 66 | | matches | | 0 | "Of course he ran." | | 1 | "Just a smell like wet" |
| | ratio | 0.03 | |
| 50.30% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 66 | | matches | | 0 | "She'd recognize him anywhere now." | | 1 | "she shouted, and he ran" | | 2 | "She cut her stride long," | | 3 | "Her watch strap chafed at" | | 4 | "She barely noticed." | | 5 | "He vaulted a bollard without" | | 6 | "She pulled her radio up" | | 7 | "She squinted through the rain" | | 8 | "She let the radio drop" | | 9 | "She followed, raising a hand" | | 10 | "She didn't look back." | | 11 | "He was heading north." | | 12 | "She lost him once —" | | 13 | "He'd pulled the medallion out" | | 14 | "He was heading somewhere specific." | | 15 | "He knew these streets, these" | | 16 | "He was making for Camden." | | 17 | "She felt the old ache" | | 18 | "She had learned, that night," | | 19 | "He'd ducked down an iron-railed" |
| | ratio | 0.424 | |
| 66.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 66 | | matches | | 0 | "The rain came down in" | | 1 | "Quinn was already three streets" | | 2 | "She'd recognize him anywhere now." | | 3 | "The paramedic who wasn't a" | | 4 | "The man who'd been seen" | | 5 | "she shouted, and he ran" | | 6 | "She cut her stride long," | | 7 | "Her watch strap chafed at" | | 8 | "She barely noticed." | | 9 | "Herrera was fast." | | 10 | "He vaulted a bollard without" | | 11 | "She pulled her radio up" | | 12 | "She squinted through the rain" | | 13 | "Dispatch crackled something she couldn't" | | 14 | "She let the radio drop" | | 15 | "The alley spat them out" | | 16 | "Herrera threaded between a couple" | | 17 | "She followed, raising a hand" | | 18 | "The driver leaned on the" | | 19 | "She didn't look back." |
| | ratio | 0.788 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 66 | | matches | | 0 | "By the time they'd crossed" | | 1 | "Now they were a door," |
| | ratio | 0.03 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "He was looking up at her, rain running off the points of his hair, his chest heaving, the medallion out and shining against his jacket." | | 1 | "She stood at the top of the stairwell with the rain drumming on her shoulders and her radio squawking in her coat and her hand on the grip of a baton that, she …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 10 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.5 | | effectiveRatio | 0.2 | |