| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.636 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1491 | | 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) | |
| 89.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1491 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "sense of" | | 1 | "eyebrow" | | 2 | "resolve" |
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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 | 1 | | narrationSentences | 88 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 73 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1497 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1393 | | uniqueNames | 24 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 2 | | Detective | 1 | | Harlow | 1 | | Quinn | 14 | | Raven | 1 | | Nest | 1 | | Herrera | 6 | | Seville | 1 | | Shouting | 1 | | Prius | 1 | | Soho | 1 | | Chinese | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Camden | 1 | | Morris | 4 | | July | 1 | | Saint | 1 | | Christopher | 1 | | Three | 2 | | Hendon | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Shouting" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Seville" | | 4 | "Soho" | | 5 | "Tottenham" | | 6 | "Court" | | 7 | "Road" | | 8 | "Camden" | | 9 | "July" |
| | globalScore | 0.997 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like something dissolving" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.668 | | wordCount | 1497 | | matches | | 0 | "not with fear exactly but with a terrible sort of patience, the way she imagined he'd" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 40.46 | | std | 31.93 | | cv | 0.789 | | sampleLengths | | 0 | 42 | | 1 | 31 | | 2 | 98 | | 3 | 34 | | 4 | 8 | | 5 | 2 | | 6 | 41 | | 7 | 60 | | 8 | 8 | | 9 | 69 | | 10 | 61 | | 11 | 35 | | 12 | 7 | | 13 | 56 | | 14 | 83 | | 15 | 7 | | 16 | 68 | | 17 | 62 | | 18 | 38 | | 19 | 16 | | 20 | 4 | | 21 | 75 | | 22 | 62 | | 23 | 3 | | 24 | 13 | | 25 | 54 | | 26 | 78 | | 27 | 2 | | 28 | 27 | | 29 | 7 | | 30 | 122 | | 31 | 12 | | 32 | 17 | | 33 | 49 | | 34 | 105 | | 35 | 7 | | 36 | 34 |
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| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "was glued" | | 1 | "been trained" | | 2 | "been sealed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 206 | | matches | | 0 | "was hanging" | | 1 | "was fishing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 92 | | ratio | 0.054 | | matches | | 0 | "Paramedic fast — the loping, economical stride of a man who had spent years carrying bags up stairwells." | | 1 | "The pavement threw back the neon in long bleeding streaks — red, gold, that particular sickly green — and the whole street looked like something dissolving." | | 2 | "And under the music, the smell — wet stone and mildew, yes, but also woodsmoke, and hot sugar, and a green herbal reek like a florist's bin, and something with iron in it." | | 3 | "She saw the chain come out, saw the flash of the Saint Christopher medallion, and then he pulled something else out from under it — a flat pale sliver on a leather cord." | | 4 | "He turned and put his palm against the tiled arch, and where the bone touched stone the darkness inside the tunnel came apart like a curtain going back — light spilling out of nowhere, warm and amber and crowded, a long gullet of a space lined with stalls and canvas and lanterns hung from cabling, and figures moving in it, dozens of them, and the music suddenly loud enough to feel in her sternum." |
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| 95.82% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 335 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.04477611940298507 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005970149253731343 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 16.27 | | std | 16.26 | | cv | 0.999 | | sampleLengths | | 0 | 42 | | 1 | 3 | | 2 | 10 | | 3 | 6 | | 4 | 12 | | 5 | 36 | | 6 | 2 | | 7 | 15 | | 8 | 8 | | 9 | 6 | | 10 | 31 | | 11 | 3 | | 12 | 3 | | 13 | 28 | | 14 | 3 | | 15 | 5 | | 16 | 2 | | 17 | 4 | | 18 | 37 | | 19 | 3 | | 20 | 18 | | 21 | 39 | | 22 | 5 | | 23 | 3 | | 24 | 2 | | 25 | 36 | | 26 | 5 | | 27 | 26 | | 28 | 3 | | 29 | 4 | | 30 | 33 | | 31 | 21 | | 32 | 35 | | 33 | 7 | | 34 | 30 | | 35 | 2 | | 36 | 24 | | 37 | 62 | | 38 | 4 | | 39 | 4 | | 40 | 13 | | 41 | 2 | | 42 | 5 | | 43 | 51 | | 44 | 5 | | 45 | 12 | | 46 | 5 | | 47 | 24 | | 48 | 1 | | 49 | 32 |
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| 83.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5543478260869565 | | totalSentences | 92 | | uniqueOpeners | 51 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 77 | | matches | | 0 | "All clean, all boring, all" | | 1 | "Of course he ran." |
| | ratio | 0.026 | |
| 69.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 77 | | matches | | 0 | "She had run the licence," | | 1 | "She had read the tribunal" | | 2 | "It was a masterpiece of" | | 3 | "He glanced left." | | 4 | "He glanced right." | | 5 | "They always ran, and Quinn" | | 6 | "He was fast." | | 7 | "He went east, dodging a" | | 8 | "she said, not shouting" | | 9 | "He knew Soho." | | 10 | "He took the alleys the" | | 11 | "She didn't call it in." | | 12 | "She went through after him." | | 13 | "Her jacket caught on a" | | 14 | "She saw white tiles in" | | 15 | "She could hear music." | | 16 | "He had stopped too." | | 17 | "He turned, breathing hard, and" | | 18 | "He was fishing in his" | | 19 | "She saw the chain come" |
| | ratio | 0.377 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 77 | | matches | | 0 | "The rain came sideways on" | | 1 | "The Raven's Nest." | | 2 | "She had run the licence," | | 3 | "Places that clean usually had" | | 4 | "She had read the tribunal" | | 5 | "It was a masterpiece of" | | 6 | "He glanced left." | | 7 | "He glanced right." | | 8 | "They always ran, and Quinn" | | 9 | "He was fast." | | 10 | "He went east, dodging a" | | 11 | "she said, not shouting" | | 12 | "Shouting wasted air." | | 13 | "Herrera cut across traffic and" | | 14 | "Water sheeted off the awnings." | | 15 | "The pavement threw back the" | | 16 | "He knew Soho." | | 17 | "That was the problem." | | 18 | "He took the alleys the" | | 19 | "Quinn lost sight of him" |
| | ratio | 0.701 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "Paramedic fast — the loping, economical stride of a man who had spent years carrying bags up stairwells." | | 1 | "She saw white tiles in the sweep of her torch, the curved shoulder of a tunnel, a strip of ancient enamel advertising a brand of cocoa that hadn't existed since…" | | 2 | "There were dozens of them under the city, sealed and forgotten, and this one had been sealed with a padlock the size of her fist, which was hanging open on its …" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not shouting" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |