| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2169 | | 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) | |
| 63.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2169 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "silence" | | 1 | "mechanical" | | 2 | "grave" | | 3 | "structure" | | 4 | "footsteps" | | 5 | "pulse" | | 6 | "velvet" | | 7 | "etched" | | 8 | "scanned" | | 9 | "warmth" | | 10 | "echoed" | | 11 | "flicked" | | 12 | "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 | 218 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 218 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 271 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2169 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.65% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1902 | | uniqueNames | 20 | | maxNameDensity | 1.37 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 2 | | Nest | 1 | | Harlow | 1 | | Quinn | 26 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Fitzrovia | 1 | | Euston | 1 | | London | 1 | | Morris | 2 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Rain | 3 | | Water | 3 | | Two | 3 | | One | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Water" | | 7 | "One" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Euston" | | 5 | "London" | | 6 | "Rain" |
| | globalScore | 0.817 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 129 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.922 | | wordCount | 2169 | | matches | | 0 | "not to a person but to the whole market behind her, measuring the crowd" | | 1 | "not as a ghost but as weight, unfinished" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 271 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 147 | | mean | 14.76 | | std | 17.78 | | cv | 1.205 | | sampleLengths | | 0 | 41 | | 1 | 58 | | 2 | 6 | | 3 | 6 | | 4 | 5 | | 5 | 1 | | 6 | 4 | | 7 | 9 | | 8 | 56 | | 9 | 16 | | 10 | 32 | | 11 | 2 | | 12 | 5 | | 13 | 18 | | 14 | 14 | | 15 | 5 | | 16 | 69 | | 17 | 5 | | 18 | 12 | | 19 | 5 | | 20 | 5 | | 21 | 33 | | 22 | 1 | | 23 | 25 | | 24 | 2 | | 25 | 2 | | 26 | 54 | | 27 | 11 | | 28 | 44 | | 29 | 2 | | 30 | 1 | | 31 | 4 | | 32 | 4 | | 33 | 4 | | 34 | 2 | | 35 | 12 | | 36 | 6 | | 37 | 5 | | 38 | 5 | | 39 | 6 | | 40 | 52 | | 41 | 39 | | 42 | 22 | | 43 | 1 | | 44 | 5 | | 45 | 6 | | 46 | 29 | | 47 | 15 | | 48 | 10 | | 49 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 218 | | matches | | 0 | "been scratched" | | 1 | "been painted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 321 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 271 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1906 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.0236096537250787 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002098635886673662 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 271 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 271 | | mean | 8 | | std | 6.25 | | cv | 0.781 | | sampleLengths | | 0 | 9 | | 1 | 5 | | 2 | 27 | | 3 | 21 | | 4 | 7 | | 5 | 22 | | 6 | 8 | | 7 | 6 | | 8 | 6 | | 9 | 5 | | 10 | 1 | | 11 | 4 | | 12 | 9 | | 13 | 11 | | 14 | 14 | | 15 | 31 | | 16 | 16 | | 17 | 32 | | 18 | 2 | | 19 | 5 | | 20 | 18 | | 21 | 14 | | 22 | 5 | | 23 | 9 | | 24 | 12 | | 25 | 14 | | 26 | 1 | | 27 | 2 | | 28 | 6 | | 29 | 10 | | 30 | 6 | | 31 | 3 | | 32 | 2 | | 33 | 4 | | 34 | 5 | | 35 | 12 | | 36 | 5 | | 37 | 5 | | 38 | 4 | | 39 | 3 | | 40 | 2 | | 41 | 16 | | 42 | 3 | | 43 | 5 | | 44 | 1 | | 45 | 11 | | 46 | 14 | | 47 | 2 | | 48 | 2 | | 49 | 5 |
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| 62.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4059040590405904 | | totalSentences | 271 | | uniqueOpeners | 110 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 194 | | matches | | 0 | "Then a woman in a" | | 1 | "Then the man Quinn had" | | 2 | "Then he set off." | | 3 | "Somewhere above, a window slammed." | | 4 | "Somewhere below, a crowd murmured." | | 5 | "Then he vanished behind a" |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 194 | | matches | | 0 | "Her hair lay dark against" | | 1 | "Her left wrist rested near" | | 2 | "She kept her eyes on" | | 3 | "Her informant had come apart" | | 4 | "He moved with purpose, not" | | 5 | "She crossed through traffic." | | 6 | "His face sharpened." | | 7 | "He broke into a run." | | 8 | "He plunged through a knot" | | 9 | "He sprinted between cars." | | 10 | "Her radio snapped again." | | 11 | "She looked up long enough" | | 12 | "He drove on through Fitzrovia," | | 13 | "Her coat dragged heavy with" | | 14 | "She had done this job" | | 15 | "She gained a few feet." | | 16 | "He cut right instead, down" | | 17 | "He brought out a small" | | 18 | "He bolted for a rusted" | | 19 | "He slammed an elbow into" |
| | ratio | 0.253 | |
| 78.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 148 | | totalSentences | 194 | | matches | | 0 | "Headlights smeared across the road." | | 1 | "Water ran in gutters thick" | | 2 | "Detective Harlow Quinn stood under" | | 3 | "Her hair lay dark against" | | 4 | "Her left wrist rested near" | | 5 | "She kept her eyes on" | | 6 | "A voice crackled in her" | | 7 | "Quinn kept watching the entrance." | | 8 | "A bus rolled past and" | | 9 | "Quinn stepped back, jaw tight," | | 10 | "Her informant had come apart" | | 11 | "The man had swallowed hard." | | 12 | "That had bought him five" | | 13 | "He moved with purpose, not" | | 14 | "Quinn pushed from the awning." | | 15 | "She crossed through traffic." | | 16 | "A horn blasted." | | 17 | "The suspect turned once and" | | 18 | "His face sharpened." | | 19 | "He broke into a run." |
| | ratio | 0.763 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 194 | | matches | | 0 | "Now the bar door opened." | | 1 | "If she waited, he would" | | 2 | "By the time the paramedics" | | 3 | "Now it breathed in front" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 76 | | technicalSentenceCount | 2 | | matches | | 0 | "Cold air rolled up carrying scents that did not belong under London streets: coal smoke, blood, spice, wet earth, singed sugar." | | 1 | "His hand gripping her sleeve while he stared at nothing and whispered about people with no faces standing in the doorway." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |