| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1385 | | 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) | |
| 56.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1385 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "scanned" | | 1 | "perfect" | | 2 | "standard" | | 3 | "etched" | | 4 | "quivered" | | 5 | "silk" | | 6 | "structure" | | 7 | "crystalline" | | 8 | "shattered" | | 9 | "pulsed" | | 10 | "racing" |
| |
| 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 | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | 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 | 1385 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1010 | | uniqueNames | 15 | | maxNameDensity | 1.19 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 12 | | Quinn | 1 | | Callahan | 11 | | Veil | 2 | | Market | 1 | | Tube | 1 | | Camden | 1 | | Shade-crafted | 1 | | Morris | 1 | | British | 1 | | Museum | 1 | | Metropolitan | 1 | | Compass | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Callahan" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Museum" | | 6 | "Compass" |
| | places | | | globalScore | 0.906 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | 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 | 1385 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 35.51 | | std | 28.22 | | cv | 0.795 | | sampleLengths | | 0 | 29 | | 1 | 21 | | 2 | 5 | | 3 | 54 | | 4 | 29 | | 5 | 106 | | 6 | 5 | | 7 | 14 | | 8 | 41 | | 9 | 35 | | 10 | 32 | | 11 | 5 | | 12 | 17 | | 13 | 41 | | 14 | 29 | | 15 | 123 | | 16 | 4 | | 17 | 2 | | 18 | 55 | | 19 | 13 | | 20 | 77 | | 21 | 25 | | 22 | 54 | | 23 | 4 | | 24 | 1 | | 25 | 51 | | 26 | 78 | | 27 | 45 | | 28 | 18 | | 29 | 47 | | 30 | 23 | | 31 | 53 | | 32 | 24 | | 33 | 40 | | 34 | 26 | | 35 | 14 | | 36 | 73 | | 37 | 8 | | 38 | 64 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1018 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.029469548133595286 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007858546168958742 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 12.26 | | std | 8.51 | | cv | 0.694 | | sampleLengths | | 0 | 19 | | 1 | 10 | | 2 | 7 | | 3 | 14 | | 4 | 5 | | 5 | 11 | | 6 | 27 | | 7 | 16 | | 8 | 6 | | 9 | 5 | | 10 | 18 | | 11 | 11 | | 12 | 16 | | 13 | 28 | | 14 | 16 | | 15 | 9 | | 16 | 26 | | 17 | 5 | | 18 | 3 | | 19 | 11 | | 20 | 11 | | 21 | 4 | | 22 | 26 | | 23 | 2 | | 24 | 10 | | 25 | 23 | | 26 | 8 | | 27 | 6 | | 28 | 18 | | 29 | 5 | | 30 | 17 | | 31 | 14 | | 32 | 27 | | 33 | 7 | | 34 | 22 | | 35 | 5 | | 36 | 10 | | 37 | 15 | | 38 | 19 | | 39 | 3 | | 40 | 2 | | 41 | 23 | | 42 | 3 | | 43 | 43 | | 44 | 4 | | 45 | 2 | | 46 | 55 | | 47 | 4 | | 48 | 9 | | 49 | 15 |
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| 88.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5486725663716814 | | totalSentences | 113 | | uniqueOpeners | 62 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 88 | | matches | | 0 | "She scooped it up with" | | 1 | "Her worn leather watch caught" | | 2 | "He jerked a thumb at" | | 3 | "She let the name hang" | | 4 | "Her sharp jaw tightened a" | | 5 | "She stepped over a tray" | | 6 | "Her finger hovered above the" | | 7 | "She rose again and paced" | | 8 | "It had settled into a" | | 9 | "She sniffed once." | | 10 | "She had already spotted the" | | 11 | "Its needle quivered against logic." | | 12 | "She crossed the rails." | | 13 | "She edged past a cabinet" | | 14 | "She drew it aside." | | 15 | "she said, voice low" | | 16 | "It pulsed once against her" |
| | ratio | 0.193 | |
| 39.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 88 | | matches | | 0 | "The blood crusted black under" | | 1 | "A bone token fell free" | | 2 | "She scooped it up with" | | 3 | "Her worn leather watch caught" | | 4 | "DS Callahan snorted from where" | | 5 | "He jerked a thumb at" | | 6 | "Harlow straightened to her full" | | 7 | "Brown eyes scanned the platform." | | 8 | "Salt-and-pepper hair stayed ordered under" | | 9 | "The Veil Market sprawled through" | | 10 | "Stalls of warped wood lined" | | 11 | "The air reeked of ozone," | | 12 | "Every full moon the whole" | | 13 | "Something failed to add up." | | 14 | "Callahan pointed again." | | 15 | "Harlow held up a dusty" | | 16 | "The surface stayed clean." | | 17 | "Shadows cut across his freckled" | | 18 | "She let the name hang" | | 19 | "Her sharp jaw tightened a" |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 5 | | matches | | 0 | "Permanent dusk hung thick, woven by spells that swallowed mobile signals and street lamps from above." | | 1 | "Beneath the copper and sage layer rode an chemical bite she recognised from earlier cases: banned shadow distillate, under the table sales only, items that left…" | | 2 | "The needle swung left of Callahan and locked hard onto a half-collapsed stall draped in spider-silk cloth that had once shone silver and now hung grey." | | 3 | "The stalls creaked distant complaint as if the whole structure waited for moonrise so it could fold itself away again." | | 4 | "Curly red hair, now matted, framed a freckled face that had once tucked stray locks behind one ear." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.103 | | leniency | 0.207 | | rawRatio | 0 | | effectiveRatio | 0 | |