| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped aside [aside]" |
| | dialogueSentences | 60 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.048 | | effectiveRatio | 0.033 | |
| 92.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1362 | | totalAiIsmAdverbs | 2 | | 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) | |
| 88.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1362 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 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 | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1377 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 964 | | uniqueNames | 6 | | maxNameDensity | 0.62 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 4 | | Moreau | 1 | | Rory | 6 | | Yu-Fei | 1 | | Barely | 1 | | Marseille | 1 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Yu-Fei" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "he'd decided, visibly, to find her intere" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1377 | | 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 | 52 | | mean | 26.48 | | std | 22.3 | | cv | 0.842 | | sampleLengths | | 0 | 102 | | 1 | 33 | | 2 | 1 | | 3 | 1 | | 4 | 26 | | 5 | 58 | | 6 | 33 | | 7 | 65 | | 8 | 9 | | 9 | 20 | | 10 | 9 | | 11 | 3 | | 12 | 26 | | 13 | 40 | | 14 | 16 | | 15 | 74 | | 16 | 47 | | 17 | 12 | | 18 | 7 | | 19 | 16 | | 20 | 3 | | 21 | 1 | | 22 | 2 | | 23 | 65 | | 24 | 41 | | 25 | 8 | | 26 | 39 | | 27 | 47 | | 28 | 50 | | 29 | 16 | | 30 | 6 | | 31 | 3 | | 32 | 31 | | 33 | 32 | | 34 | 3 | | 35 | 28 | | 36 | 40 | | 37 | 37 | | 38 | 59 | | 39 | 21 | | 40 | 46 | | 41 | 17 | | 42 | 10 | | 43 | 2 | | 44 | 39 | | 45 | 5 | | 46 | 42 | | 47 | 2 | | 48 | 35 | | 49 | 20 |
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| 83.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 79 | | matches | | 0 | "being drawn" | | 1 | "been tailored" | | 2 | "was slicked" | | 3 | "was worn" | | 4 | "were stacked" | | 5 | "been rehearsed" |
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| 72.61% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 157 | | matches | | 0 | "was already building" | | 1 | "was thinking" | | 2 | "was already reaching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 113 | | ratio | 0.106 | | matches | | 0 | "Not the quick, assessing glance he gave strangers — a full, slow inventory, starting at her eyes and working down to the courier bag still slung across her chest." | | 1 | "Papers were stacked on the small table by the door — bills, by the look of them, and a newspaper with a section circled in red." | | 2 | "\"You gave it to me once. The second time I deleted it on purpose.\" She met his amber eye, then his black one — the mismatch had always thrown her for a half-second before her brain caught up." | | 3 | "\"So do I. She was my mum's colleague at the school.\" Rory unzipped the courier bag and pulled out a folder — Yu-Fei's takeout receipts, she'd grabbed them off the passenger seat without thinking — and set it on the table on top of his papers." | | 4 | "The screen glowed with a waveform — a woman's voice, clipped and frightened, the kind of fear that had been rehearsed so many times it had gone smooth." | | 5 | "He held it the way he held everything — like it was evidence and he was already building the case." | | 6 | "He didn't cross to her — he moved to the window and looked down at the street, both hands in his pockets." | | 7 | "Rory watched his shoulders — the line of them, the way the jacket sat — and understood he'd heard the crack in the sentence as clearly as she had." | | 8 | "A laugh escaped before she could catch it — short, involuntary, startled out of her." | | 9 | "\"You'll have it by tomorrow.\" He moved past her to the table and pulled a business card from the stack — blank except for a phone number." | | 10 | "\"You know how one door works. You've been in one place too long.\" He picked up his cane and balanced it across his palm, a habit she remembered — a tell for when he was thinking." | | 11 | "He looked at her — really looked, from her boots to her eyes, the whole of her, the way he had when the door opened and he'd decided to catalogue instead of ask." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 952 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03361344537815126 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008403361344537815 | |
| 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 | 12.19 | | std | 10.36 | | cv | 0.85 | | sampleLengths | | 0 | 12 | | 1 | 39 | | 2 | 7 | | 3 | 32 | | 4 | 12 | | 5 | 4 | | 6 | 29 | | 7 | 1 | | 8 | 1 | | 9 | 14 | | 10 | 12 | | 11 | 5 | | 12 | 37 | | 13 | 16 | | 14 | 33 | | 15 | 9 | | 16 | 24 | | 17 | 26 | | 18 | 6 | | 19 | 3 | | 20 | 6 | | 21 | 9 | | 22 | 5 | | 23 | 6 | | 24 | 9 | | 25 | 3 | | 26 | 5 | | 27 | 15 | | 28 | 6 | | 29 | 38 | | 30 | 2 | | 31 | 5 | | 32 | 5 | | 33 | 6 | | 34 | 46 | | 35 | 21 | | 36 | 7 | | 37 | 30 | | 38 | 15 | | 39 | 2 | | 40 | 3 | | 41 | 9 | | 42 | 7 | | 43 | 6 | | 44 | 10 | | 45 | 3 | | 46 | 1 | | 47 | 2 | | 48 | 25 | | 49 | 28 |
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| 64.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.40707964601769914 | | totalSentences | 113 | | uniqueOpeners | 46 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | | ratio | 0.014 | |
| 22.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 73 | | matches | | 0 | "His platinum hair was slicked" | | 1 | "He looked at her." | | 2 | "He leaned against the frame" | | 3 | "He stepped aside anyway, one" | | 4 | "She didn't take off her" | | 5 | "She counted them without meaning" | | 6 | "he said from behind her" | | 7 | "He moved past her toward" | | 8 | "She met his amber eye," | | 9 | "He almost smiled" | | 10 | "It didn't reach either eye." | | 11 | "He lowered himself into the" | | 12 | "She stayed standing." | | 13 | "He held it the way" | | 14 | "He listened to the whole" | | 15 | "He said it flatly, without" | | 16 | "She'd meant to say it" | | 17 | "It came out sharper than" | | 18 | "He rose from the chair" | | 19 | "He didn't cross to her" |
| | ratio | 0.493 | |
| 21.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 73 | | matches | | 0 | "The lock gave before her" | | 1 | "The sound of a chain" | | 2 | "Charcoal suit, no tie, top" | | 3 | "His platinum hair was slicked" | | 4 | "The ivory head of his" | | 5 | "He looked at her." | | 6 | "He leaned against the frame" | | 7 | "Rory shifted her weight" | | 8 | "The carpet beneath her boots" | | 9 | "He stepped aside anyway, one" | | 10 | "The flat was spare to" | | 11 | "A single armchair, a bookshelf" | | 12 | "Papers were stacked on the" | | 13 | "Everything else was surface and" | | 14 | "Rory walked in." | | 15 | "She didn't take off her" | | 16 | "Lucien closed the door and" | | 17 | "She counted them without meaning" | | 18 | "he said from behind her" | | 19 | "He moved past her toward" |
| | ratio | 0.877 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 2 | | matches | | 0 | "A single armchair, a bookshelf that held more bottle than book, a window left open to the sounds of the street four stories below." | | 1 | "The screen glowed with a waveform — a woman's voice, clipped and frightened, the kind of fear that had been rehearsed so many times it had gone smooth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | 0 | "He lowered, ankle resting on knee" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 60 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.167 | | effectiveRatio | 0.033 | |