| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2011 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "lightly" | | 1 | "quickly" | | 2 | "slightly" | | 3 | "slowly" | | 4 | "really" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 67.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2011 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "warmth" | | 1 | "weight" | | 2 | "traced" | | 3 | "fractured" | | 4 | "throbbed" | | 5 | "measured" | | 6 | "silence" | | 7 | "footsteps" |
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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 | 202 | | matches | (empty) | |
| 79.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 1 | | narrationSentences | 202 | | filterMatches | | 0 | "look" | | 1 | "see" | | 2 | "feel" | | 3 | "watch" | | 4 | "know" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | 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 | 2011 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 3 | | matches | | 0 | "You could have been one of them, Owen said." | | 1 | "You should go to the door on Saturday, Owen said after a long pause." | | 2 | "I know, she said." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 83 | | wordCount | 2011 | | uniqueNames | 32 | | maxNameDensity | 1.04 | | worstName | "You" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Aurora | 1 | | Golden | 2 | | Empress | 2 | | Raven | 1 | | Nest | 2 | | Soho | 2 | | Ordnance | 1 | | Survey | 1 | | Taff | 1 | | River | 1 | | Cardiff | 3 | | Eva | 3 | | Silas | 7 | | Rory | 8 | | Owen | 10 | | Tuesday | 1 | | Dalston | 1 | | Limehouse | 1 | | Monmouth | 1 | | March | 1 | | Wales | 1 | | London | 2 | | Evan | 1 | | Sunday | 1 | | Inns | 1 | | Oxford | 1 | | October | 1 | | November | 1 | | Saturday | 1 | | Bath | 1 | | Thursday | 1 | | You | 21 |
| | persons | | 0 | "Aurora" | | 1 | "Empress" | | 2 | "Raven" | | 3 | "Survey" | | 4 | "Eva" | | 5 | "Silas" | | 6 | "Rory" | | 7 | "Owen" | | 8 | "Evan" | | 9 | "You" |
| | places | | 0 | "Golden" | | 1 | "Nest" | | 2 | "Soho" | | 3 | "Taff" | | 4 | "River" | | 5 | "Cardiff" | | 6 | "Monmouth" | | 7 | "March" | | 8 | "Wales" | | 9 | "London" | | 10 | "Inns" | | 11 | "October" | | 12 | "Bath" |
| | globalScore | 0.978 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 128 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a stone in her throat as she" | | 1 | "not quite a smile" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.497 | | wordCount | 2011 | | matches | | 0 | "not unless the calculation warranted it, but her steps were sure across the scarred floorboards" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 202 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 60.94 | | std | 47.57 | | cv | 0.781 | | sampleLengths | | 0 | 246 | | 1 | 120 | | 2 | 4 | | 3 | 138 | | 4 | 11 | | 5 | 54 | | 6 | 1 | | 7 | 76 | | 8 | 4 | | 9 | 30 | | 10 | 27 | | 11 | 40 | | 12 | 42 | | 13 | 55 | | 14 | 16 | | 15 | 53 | | 16 | 65 | | 17 | 46 | | 18 | 55 | | 19 | 111 | | 20 | 53 | | 21 | 48 | | 22 | 116 | | 23 | 114 | | 24 | 37 | | 25 | 91 | | 26 | 45 | | 27 | 46 | | 28 | 19 | | 29 | 58 | | 30 | 46 | | 31 | 65 | | 32 | 79 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 202 | | matches | | |
| 75.22% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 374 | | matches | | 0 | "were running" | | 1 | "were just leaving" | | 2 | "was extracting" | | 3 | "was pulling" | | 4 | "was drowning" | | 5 | "were standing" | | 6 | "was timing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 202 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2021 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 77 | | adverbRatio | 0.03809995051954478 | | lyAdverbCount | 25 | | lyAdverbRatio | 0.012370113805047007 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 9.96 | | std | 7.68 | | cv | 0.772 | | sampleLengths | | 0 | 28 | | 1 | 24 | | 2 | 17 | | 3 | 15 | | 4 | 22 | | 5 | 15 | | 6 | 6 | | 7 | 26 | | 8 | 5 | | 9 | 19 | | 10 | 24 | | 11 | 20 | | 12 | 11 | | 13 | 14 | | 14 | 8 | | 15 | 26 | | 16 | 12 | | 17 | 7 | | 18 | 20 | | 19 | 28 | | 20 | 19 | | 21 | 4 | | 22 | 24 | | 23 | 17 | | 24 | 29 | | 25 | 26 | | 26 | 1 | | 27 | 41 | | 28 | 5 | | 29 | 3 | | 30 | 3 | | 31 | 20 | | 32 | 3 | | 33 | 9 | | 34 | 4 | | 35 | 18 | | 36 | 1 | | 37 | 9 | | 38 | 18 | | 39 | 5 | | 40 | 15 | | 41 | 15 | | 42 | 14 | | 43 | 4 | | 44 | 2 | | 45 | 4 | | 46 | 15 | | 47 | 9 | | 48 | 8 | | 49 | 5 |
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| 32.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 36 | | diversityRatio | 0.28217821782178215 | | totalSentences | 202 | | uniqueOpeners | 57 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 185 | | matches | | 0 | "Then she saw him." | | 1 | "Then Rory stood." | | 2 | "Still pretending he’s retired when" | | 3 | "Then I watched the world" | | 4 | "Always will be." |
| | ratio | 0.027 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 105 | | totalSentences | 185 | | matches | | 0 | "She shook water from the" | | 1 | "He did not look up" | | 2 | "He finished polishing a highball" | | 3 | "His hazel eyes met hers." | | 4 | "He tapped the silver signet" | | 5 | "She nodded, murmured thanks, and" | | 6 | "She traced the rim of" | | 7 | "She should have changed before" | | 8 | "She stayed in the delivery" | | 9 | "He wore a charcoal overcoat" | | 10 | "His posture was exactly as" | | 11 | "She had not seen him" | | 12 | "He set the glass down." | | 13 | "She did not smile." | | 14 | "She rarely did, not unless" | | 15 | "Your name had always sat" | | 16 | "He rose, offering a hand." | | 17 | "His grip was warm, calloused" | | 18 | "She released the hand quickly," | | 19 | "You look well, Owen." |
| | ratio | 0.568 | |
| 54.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 150 | | totalSentences | 185 | | matches | | 0 | "The green neon sign above" | | 1 | "The smell hit her first:" | | 2 | "She shook water from the" | | 3 | "runs to the Golden Empress," | | 4 | "The city outside had turned" | | 5 | "Silas stood behind the polished" | | 6 | "He did not look up" | | 7 | "He finished polishing a highball" | | 8 | "His hazel eyes met hers." | | 9 | "The faint scar of a" | | 10 | "He tapped the silver signet" | | 11 | "Rory caught the subtle shift" | | 12 | "She nodded, murmured thanks, and" | | 13 | "The walls were a patchwork" | | 14 | "She traced the rim of" | | 15 | "The thermal liner against her" | | 16 | "She should have changed before" | | 17 | "She stayed in the delivery" | | 18 | "Pragmatism was a muscle she" | | 19 | "He wore a charcoal overcoat" |
| | ratio | 0.811 | |
| 54.05% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 185 | | matches | | 0 | "Now it felt like a" | | 1 | "If I’m thinking about returning" |
| | ratio | 0.011 | |
| 76.19% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 7 | | matches | | 0 | "His posture was exactly as she remembered: spine straight, shoulders relaxed, hands resting on the table with the ease of someone who had nowhere left to prove …" | | 1 | "Still drawing blueprints for houses that will outlast our grandchildren?" | | 2 | "Owen leaned back slightly, his hazel eyes reflecting the green neon that painted his jawline in cool tones." | | 3 | "And now, Eva’s flatmate is sleeping above a bar that doubles as a dead-drop archive." | | 4 | "He nodded toward Silas, who was now arguing quietly with a patron about the provenance of a particularly cloudy bottle of bourbon." | | 5 | "The weight of it was in the space between them, in the years of silence that had accumulated like dust on a windowsill." | | 6 | "She listened to the rain strike the windowpane, to Silas pouring spirits, to the low murmur of strangers who had found temporary shelter in the same dim room." |
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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 | |