| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.565 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1436 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 65.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1436 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "glinting" | | 1 | "silence" | | 2 | "chill" | | 3 | "chilled" | | 4 | "predator" | | 5 | "trembled" | | 6 | "flickered" | | 7 | "weight" |
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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 | 80 | | matches | (empty) | |
| 35.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1435 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1317 | | uniqueNames | 19 | | maxNameDensity | 1.06 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Raven | 4 | | Nest | 4 | | Soho | 3 | | Harlow | 8 | | Quinn | 2 | | Morris | 3 | | London | 3 | | Herrera | 1 | | Saint | 3 | | Christopher | 3 | | Seville-born | 1 | | English | 1 | | Tomás | 14 | | Camden | 3 | | Lock | 1 | | Veil | 2 | | Market | 2 | | Seville | 2 | | Detective | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" | | 9 | "Market" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Seville-born" | | 3 | "Camden" | | 4 | "Seville" |
| | globalScore | 0.968 | | windowScore | 1 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like the dark between stars" | | 1 | "photographs that seemed to move when she was not looking, of the clique that moved through London’s bones like a second circulatory system" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 2.091 | | wordCount | 1435 | | matches | | 0 | "not just geography but thresholds" | | 1 | "not in a station platform but in a cavernous space" | | 2 | "not in invitation but in recognition of her choice" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 33.37 | | std | 27.54 | | cv | 0.825 | | sampleLengths | | 0 | 118 | | 1 | 76 | | 2 | 1 | | 3 | 40 | | 4 | 3 | | 5 | 13 | | 6 | 25 | | 7 | 37 | | 8 | 13 | | 9 | 2 | | 10 | 86 | | 11 | 16 | | 12 | 19 | | 13 | 60 | | 14 | 17 | | 15 | 40 | | 16 | 58 | | 17 | 10 | | 18 | 31 | | 19 | 66 | | 20 | 14 | | 21 | 2 | | 22 | 9 | | 23 | 6 | | 24 | 39 | | 25 | 44 | | 26 | 38 | | 27 | 39 | | 28 | 60 | | 29 | 11 | | 30 | 14 | | 31 | 5 | | 32 | 64 | | 33 | 85 | | 34 | 22 | | 35 | 6 | | 36 | 36 | | 37 | 33 | | 38 | 6 | | 39 | 54 | | 40 | 69 | | 41 | 4 | | 42 | 44 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 217 | | matches | | 0 | "was standing" | | 1 | "was not looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 90 | | ratio | 0.011 | | matches | | 0 | "“You need access.” He pulled a small object from his pocket—a bone token, pale and worn, carved with symbols she felt in her teeth rather than read." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1325 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.013584905660377358 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0037735849056603774 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 15.94 | | std | 9.64 | | cv | 0.604 | | sampleLengths | | 0 | 27 | | 1 | 39 | | 2 | 31 | | 3 | 21 | | 4 | 12 | | 5 | 28 | | 6 | 22 | | 7 | 14 | | 8 | 1 | | 9 | 7 | | 10 | 33 | | 11 | 3 | | 12 | 11 | | 13 | 2 | | 14 | 16 | | 15 | 9 | | 16 | 37 | | 17 | 13 | | 18 | 2 | | 19 | 6 | | 20 | 32 | | 21 | 14 | | 22 | 16 | | 23 | 18 | | 24 | 16 | | 25 | 19 | | 26 | 27 | | 27 | 13 | | 28 | 20 | | 29 | 17 | | 30 | 23 | | 31 | 17 | | 32 | 15 | | 33 | 17 | | 34 | 26 | | 35 | 10 | | 36 | 31 | | 37 | 3 | | 38 | 11 | | 39 | 22 | | 40 | 10 | | 41 | 20 | | 42 | 14 | | 43 | 2 | | 44 | 9 | | 45 | 6 | | 46 | 19 | | 47 | 20 | | 48 | 20 | | 49 | 18 |
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| 42.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.26666666666666666 | | totalSentences | 90 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 69.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 77 | | matches | | 0 | "She had lost DS Morris" | | 1 | "She moved before he finished" | | 2 | "Her jaw was sharp, her" | | 3 | "She tapped her watch strap" | | 4 | "He dug his fingers into" | | 5 | "She spoke his name like" | | 6 | "she shouted, her voice cutting" | | 7 | "he called back, ducking beneath" | | 8 | "She kept her distance close" | | 9 | "They weaved between parked cars," | | 10 | "She mirrored him, her shoulder" | | 11 | "she shouted over the downpour" | | 12 | "he shot back, heading north," | | 13 | "His pace was relentless, a" | | 14 | "she called, her voice strained" | | 15 | "He turned into a service" | | 16 | "His warm brown eyes held" | | 17 | "His scarred arm trembled." | | 18 | "She looked at her watch." | | 19 | "She thought of the hidden" |
| | ratio | 0.377 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 77 | | matches | | 0 | "The green neon sign of" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "She had lost DS Morris" | | 3 | "The bookshelf door inside The" | | 4 | "Tomás Herrera stepped out, his" | | 5 | "A scar ran along his" | | 6 | "She moved before he finished" | | 7 | "Her jaw was sharp, her" | | 8 | "Tomás smiled, a tight line" | | 9 | "She tapped her watch strap" | | 10 | "He dug his fingers into" | | 11 | "She spoke his name like" | | 12 | "The chase exploded into the" | | 13 | "Tomás exploded out from under" | | 14 | "Harlow followed, her boots splashing" | | 15 | "The streets of Soho were" | | 16 | "she shouted, her voice cutting" | | 17 | "he called back, ducking beneath" | | 18 | "She kept her distance close" | | 19 | "The air smelled of wet" |
| | ratio | 0.922 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 77 | | matches | | 0 | "Now she suspected the clique," | | 1 | "Now he offered her the" |
| | ratio | 0.026 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 11 | | matches | | 0 | "Detective Harlow Quinn stood beneath it, her salt-and-pepper hair slick against her skull, her worn leather watch glinting on her left wrist as she tracked the …" | | 1 | "She had lost DS Morris three years ago to something that did not fit within neat categories of police reports, something supernatural that left a silence where …" | | 2 | "Tomás exploded out from under the green neon, his feet finding the uneven stones with the instinct of a Seville-born man who had learned to move through crowds …" | | 3 | "She mirrored him, her shoulder brushing cold metal, her eyes tracking the flash of his scarred arm as he used it to balance against a turn." | | 4 | "Tomás led her past derelict cinemas with boarded windows, past the skeletal frame of an old market that had surrendered to time." | | 5 | "Harlow kept up, her military bearing holding her spine straight, her watch ticking a silent accusation against her left wrist." | | 6 | "At the end of the lane, a rusted iron gate sagged open, revealing a stairwell that descended into black earth." | | 7 | "She thought of the hidden back room, of black-and-white photographs that seemed to move when she was not looking, of the clique that moved through London’s bone…" | | 8 | "Water dripped from walls that arched like the inside of a skull, pooling at her feet, carrying a scent of burnt herbs and old magic." | | 9 | "The stairs ended not in a station platform but in a cavernous space where the ceiling arched overhead and the walls were lined with stalls that glowed with sick…" | | 10 | "Her partner’s silence was a weight in her chest, urging her not to vanish into this, urging her to stay where police meant something." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, her voice cutting through the thunder of rain on awnings" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 5 | | fancyTags | | 0 | "She spoke (speak)" | | 1 | "she shouted (shout)" | | 2 | "he called back (call back)" | | 3 | "Tomás yelled (yell)" | | 4 | "she shouted (shout)" |
| | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0.625 | | effectiveRatio | 0.435 | |