| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "Aurora asked curtly [curtly]" |
| | dialogueSentences | 17 | | tagDensity | 0.529 | | leniency | 1 | | rawRatio | 0.111 | | effectiveRatio | 0.111 | |
| 95.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1042 | | 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) | |
| 42.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1042 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "pounding" | | 1 | "whisper" | | 2 | "echoed" | | 3 | "tension" | | 4 | "charged" | | 5 | "lilt" | | 6 | "echo" | | 7 | "glint" | | 8 | "grapple" | | 9 | "pulse" | | 10 | "pumping" | | 11 | "dance" |
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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 | 65 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1045 | | ratio | 0 | | matches | (empty) | |
| 89.29% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 1 | | matches | | 0 | "About to say more, he spat a glob of saliva and red into her antique tatami mats." |
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| 89.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 915 | | uniqueNames | 13 | | maxNameDensity | 1.2 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 11 | | Carter | 1 | | Lucien | 7 | | Moreau | 2 | | Parisian | 1 | | Belushi | 1 | | French | 1 | | Authentication | 1 | | Ovollo | 1 | | Coven | 1 | | London | 1 | | Apache | 1 | | Luc | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Lucien" | | 3 | "Moreau" | | 4 | "Belushi" | | 5 | "Coven" | | 6 | "Luc" |
| | places | | | globalScore | 0.899 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "as if savouring the taste already" |
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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 | 1045 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 46.81% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 52.25 | | std | 16.4 | | cv | 0.314 | | sampleLengths | | 0 | 52 | | 1 | 49 | | 2 | 59 | | 3 | 76 | | 4 | 40 | | 5 | 81 | | 6 | 32 | | 7 | 47 | | 8 | 63 | | 9 | 43 | | 10 | 64 | | 11 | 69 | | 12 | 13 | | 13 | 65 | | 14 | 44 | | 15 | 66 | | 16 | 29 | | 17 | 58 | | 18 | 56 | | 19 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "were pulling" | | 1 | "was materializing" |
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| 64.58% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 73 | | ratio | 0.027 | | matches | | 0 | "The last time they'd met - a charged night that started with drinks in a dimly lit Parisian corner of Belushi's and folded into a mess of sheets and sweat at his flat in the 6th arrondissement - had made it clear boundaries were a good thing." | | 1 | "She matched his stride for move, backing into the shower - the only space in this one bedroom flat that had a direct window to the alley." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 919 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.030467899891186073 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.015233949945593036 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 14.32 | | std | 7.94 | | cv | 0.555 | | sampleLengths | | 0 | 16 | | 1 | 7 | | 2 | 16 | | 3 | 13 | | 4 | 23 | | 5 | 15 | | 6 | 11 | | 7 | 10 | | 8 | 1 | | 9 | 13 | | 10 | 20 | | 11 | 15 | | 12 | 18 | | 13 | 9 | | 14 | 47 | | 15 | 2 | | 16 | 32 | | 17 | 8 | | 18 | 8 | | 19 | 7 | | 20 | 4 | | 21 | 21 | | 22 | 26 | | 23 | 15 | | 24 | 9 | | 25 | 4 | | 26 | 19 | | 27 | 11 | | 28 | 12 | | 29 | 20 | | 30 | 4 | | 31 | 7 | | 32 | 18 | | 33 | 13 | | 34 | 20 | | 35 | 5 | | 36 | 27 | | 37 | 16 | | 38 | 13 | | 39 | 26 | | 40 | 7 | | 41 | 18 | | 42 | 14 | | 43 | 24 | | 44 | 31 | | 45 | 13 | | 46 | 12 | | 47 | 6 | | 48 | 20 | | 49 | 27 |
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| 93.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5616438356164384 | | totalSentences | 73 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 61.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 63 | | matches | | 0 | "She recognized his silhouette through" | | 1 | "He looked much the same" | | 2 | "Her eyes drifted shut as" | | 3 | "His could only mean one" | | 4 | "It was only a heartbeat's" | | 5 | "She heard him close the" | | 6 | "Her stance remained guarded, arms" | | 7 | "His voice, a rich baritone" | | 8 | "It made no sense." | | 9 | "She had blinkered well enough" | | 10 | "Her coven in London remained" | | 11 | "He stepped into her space," | | 12 | "Her elemental shield was materializing" | | 13 | "It would be so satisfying..." | | 14 | "She jerked her chin towards" | | 15 | "His grip bit into her" | | 16 | "Her eyes flitted towards the" | | 17 | "She matched his stride for" | | 18 | "he murmured, cornering her" | | 19 | "His tongue slid along his" |
| | ratio | 0.397 | |
| 86.98% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 63 | | matches | | 0 | "She recognized his silhouette through" | | 1 | "Heart pounding, Aurora Carter hesitated," | | 2 | "He looked much the same" | | 3 | "Her eyes drifted shut as" | | 4 | "His could only mean one" | | 5 | "Moreau stood a breath apart," | | 6 | "It was only a heartbeat's" | | 7 | "She heard him close the" | | 8 | "Aurora asked curtly over her" | | 9 | "Her stance remained guarded, arms" | | 10 | "The last time they'd met" | | 11 | "His voice, a rich baritone" | | 12 | "Aurora frowned, morphing her surprise" | | 13 | "The supernatural criminal empire wanted" | | 14 | "It made no sense." | | 15 | "She had blinkered well enough" | | 16 | "Her coven in London remained" | | 17 | "Eyes narrowing, Aurora put space" | | 18 | "this man before her." | | 19 | "Lucien's eyes flashed amber, haloing" |
| | ratio | 0.746 | |
| 79.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 63 | | matches | | 0 | "To ignore his summons now" |
| | ratio | 0.016 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "Her eyes drifted shut as his knuckles rapped against the wood, a staccato double-knock that echoed in the thin hallway beyond her flat." | | 1 | "The last time they'd met - a charged night that started with drinks in a dimly lit Parisian corner of Belushi's and folded into a mess of sheets and sweat at hi…" | | 2 | "Lucien smirked dangerously, a dimple notched in his left cheek, as if he could read this deadly play out in the fiery glint flaring across her iris." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 32.35% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Aurora spat (spit)" | | 1 | "he murmured (murmur)" |
| | dialogueSentences | 17 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0.667 | | effectiveRatio | 0.235 | |