| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 79.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1738 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "sharply" | | 1 | "precisely" | | 2 | "very" | | 3 | "slowly" |
| |
| 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) | |
| 68.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1738 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "raced" | | 1 | "echoing" | | 2 | "scanned" | | 3 | "pawn" | | 4 | "hulking" | | 5 | "wavered" | | 6 | "silence" | | 7 | "weight" | | 8 | "aligned" | | 9 | "charged" |
| |
| 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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 131 | | filterMatches | (empty) | | hedgeMatches | | 0 | "started to" | | 1 | "managed to" |
| |
| 98.18% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0.008 | | matches | | 0 | "Theילки в римской столице." |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1731 | | 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 | 30 | | wordCount | 1729 | | uniqueNames | 22 | | maxNameDensity | 0.52 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 9 | | Soho | 1 | | Paddington | 1 | | Mayfair | 1 | | London | 1 | | Thames | 1 | | Troll | 1 | | Miu | 1 | | Itos | 1 | | Roz | 1 | | Town | 1 | | Launch | 1 | | Chant | 1 | | Blood | 1 | | Sons | 1 | | Juniper | 1 | | First | 1 | | Herein | 1 | | Wheal | 1 | | Reehan | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miu" | | 3 | "Blood" | | 4 | "Juniper" | | 5 | "Herein" |
| | places | | 0 | "Soho" | | 1 | "Paddington" | | 2 | "Mayfair" | | 3 | "London" | | 4 | "Thames" | | 5 | "Roz" | | 6 | "Town" | | 7 | "First" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 1 | | matches | | 0 | "as if keeping something valuable from touching something privately" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.578 | | wordCount | 1731 | | matches | | 0 | "not a cok ran over now, but winding up his fist" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 131 | | matches | | |
| 78.02% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 86.55 | | std | 36.62 | | cv | 0.423 | | sampleLengths | | 0 | 88 | | 1 | 70 | | 2 | 98 | | 3 | 88 | | 4 | 74 | | 5 | 122 | | 6 | 81 | | 7 | 106 | | 8 | 91 | | 9 | 74 | | 10 | 57 | | 11 | 98 | | 12 | 100 | | 13 | 46 | | 14 | 193 | | 15 | 27 | | 16 | 44 | | 17 | 146 | | 18 | 77 | | 19 | 51 |
| |
| 97.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 131 | | matches | | 0 | "were lined" | | 1 | "was ripped" | | 2 | "been warned" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 281 | | matches | | 0 | "was petting" | | 1 | "was conceding" |
| |
| 99.24% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 131 | | ratio | 0.015 | | matches | | 0 | "Quinn's heart pounded as she scanned for exits, then spotted it - a hidden door in the space beneath the fire escape." | | 1 | "A floating dock marked the culmination of the old shoreline, a hulking outpost of bronzed stone opposite with oddly angular windows - not precisely tuber." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1736 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 56 | | adverbRatio | 0.03225806451612903 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.01152073732718894 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 13.21 | | std | 8.37 | | cv | 0.633 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 14 | | 3 | 37 | | 4 | 19 | | 5 | 14 | | 6 | 8 | | 7 | 22 | | 8 | 7 | | 9 | 7 | | 10 | 13 | | 11 | 8 | | 12 | 23 | | 13 | 30 | | 14 | 17 | | 15 | 13 | | 16 | 11 | | 17 | 27 | | 18 | 27 | | 19 | 10 | | 20 | 23 | | 21 | 8 | | 22 | 17 | | 23 | 15 | | 24 | 4 | | 25 | 7 | | 26 | 10 | | 27 | 5 | | 28 | 10 | | 29 | 22 | | 30 | 12 | | 31 | 26 | | 32 | 17 | | 33 | 20 | | 34 | 11 | | 35 | 4 | | 36 | 18 | | 37 | 26 | | 38 | 22 | | 39 | 13 | | 40 | 13 | | 41 | 25 | | 42 | 5 | | 43 | 27 | | 44 | 23 | | 45 | 4 | | 46 | 7 | | 47 | 8 | | 48 | 7 | | 49 | 7 |
| |
| 67.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4351145038167939 | | totalSentences | 131 | | uniqueOpeners | 57 | |
| 26.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 127 | | matches | | 0 | "More adept hound fell silently." |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 127 | | matches | | 0 | "Her quarry, a young man" | | 1 | "She cursed under her breath" | | 2 | "Her knees barked with pain" | | 3 | "It swung closed with a" | | 4 | "She weighed her options for" | | 5 | "Her partner, arrested and waiting" | | 6 | "It twisted sharply and delivered" | | 7 | "Her quarry stood at a" | | 8 | "She was alone here, an" | | 9 | "Her instincts screamed caution." | | 10 | "It was a risk but" | | 11 | "She put on a sprint," | | 12 | "She knew she'd only get" | | 13 | "Her hound in." | | 14 | "His gaze turned desperate around" | | 15 | "His only way out." | | 16 | "I was nice to get" | | 17 | "She hesitated one final, agonizing" | | 18 | "She was a better job." | | 19 | "It took out her plenary." |
| | ratio | 0.228 | |
| 82.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 127 | | matches | | 0 | "The rain came down in" | | 1 | "Her quarry, a young man" | | 2 | "She cursed under her breath" | | 3 | "The suspect had led her" | | 4 | "Quinn grabbed the corner with" | | 5 | "Her knees barked with pain" | | 6 | "Quinn's heart pounded as she" | | 7 | "It swung closed with a" | | 8 | "She weighed her options for" | | 9 | "Her partner, arrested and waiting" | | 10 | "The cryptic clues left behind" | | 11 | "Quinn shoved through the ingress" | | 12 | "It twisted sharply and delivered" | | 13 | "The cavern walls were lined" | | 14 | "Throngs of denizens milled between" | | 15 | "The air swam with the" | | 16 | "Her quarry stood at a" | | 17 | "Quinn hesitated, unsure whether to" | | 18 | "The AFL crowd and its" | | 19 | "She was alone here, an" |
| | ratio | 0.756 | |
| 78.74% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 127 | | matches | | 0 | "To follow would be jumping" | | 1 | "To their right, the corridor" |
| | ratio | 0.016 | |
| 84.15% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 6 | | matches | | 0 | "She put on a sprint, pelting through the market, nearly tumbling past a man swaddled in glowing turquoise robes who was petting a keening crimson hound." | | 1 | "Tatsuhiro and Miu found themselves surrounded by a muted white glow that made their hearts kick purchased, pained." | | 2 | "Is he, and she found herself interacting by a child in the hunch layered crimson cloak shifting in place as if keeping something valuable from touching somethin…" | | 3 | "Were going to make way for an alley as well as her hand needing to turn as if nothing else in front of a ignoble sign of the opportunity for death while followi…" | | 4 | "Represents a community wall behind Wheal as though that the hint of violent sidearm or board how, and pull from the meeting." | | 5 | "Bailed herself as if they're turning at his shoulder blades, walking away with their own hand in this pace." |
| |
| 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 | |