| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 53 | | tagDensity | 0.075 | | leniency | 0.151 | | rawRatio | 0.25 | | effectiveRatio | 0.038 | |
| 77.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1341 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "sharply" |
| |
| 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) | |
| 55.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1341 | | totalAiIsms | 12 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "down her spine" | | count | 1 |
| | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "flickered" | | 1 | "intensity" | | 2 | "unreadable" | | 3 | "jaw clenched" | | 4 | "determined" | | 5 | "weight" | | 6 | "silence" | | 7 | "down her spine" | | 8 | "electric" | | 9 | "charged" | | 10 | "pulse" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
| | 1 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "jaw clenched" | | 1 | "sent a shiver down" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 104 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1332 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1018 | | uniqueNames | 9 | | maxNameDensity | 1.38 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 14 | | Silas | 2 | | Ptolemy | 2 | | Eva | 3 | | Moreau | 1 | | Soho | 1 | | Lucien | 7 | | Six | 3 | | Wanted | 3 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Ptolemy" | | 3 | "Eva" | | 4 | "Moreau" | | 5 | "Lucien" |
| | places | | | globalScore | 0.812 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "something like gunpowder, trailing after him" |
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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 | 1332 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 71 | | mean | 18.76 | | std | 16.21 | | cv | 0.864 | | sampleLengths | | 0 | 47 | | 1 | 3 | | 2 | 54 | | 3 | 33 | | 4 | 3 | | 5 | 13 | | 6 | 20 | | 7 | 5 | | 8 | 55 | | 9 | 5 | | 10 | 60 | | 11 | 6 | | 12 | 10 | | 13 | 45 | | 14 | 37 | | 15 | 3 | | 16 | 2 | | 17 | 9 | | 18 | 16 | | 19 | 3 | | 20 | 7 | | 21 | 12 | | 22 | 12 | | 23 | 22 | | 24 | 28 | | 25 | 54 | | 26 | 20 | | 27 | 25 | | 28 | 5 | | 29 | 25 | | 30 | 11 | | 31 | 7 | | 32 | 63 | | 33 | 17 | | 34 | 6 | | 35 | 2 | | 36 | 5 | | 37 | 17 | | 38 | 6 | | 39 | 2 | | 40 | 30 | | 41 | 11 | | 42 | 5 | | 43 | 5 | | 44 | 14 | | 45 | 13 | | 46 | 23 | | 47 | 1 | | 48 | 22 | | 49 | 12 |
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| 95.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 104 | | matches | | 0 | "was amused" | | 1 | "was—was" | | 2 | "was determined" |
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| 90.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 183 | | matches | | 0 | "was wiping" | | 1 | "was standing" | | 2 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 151 | | ratio | 0.06 | | matches | | 0 | "The flat was a mess—takeaway containers littered the coffee table, a half-empty bottle of wine sat open on the kitchen side, and Ptolemy had knocked over a stack of Eva’s research notes, scattering them like confetti." | | 1 | "The peephole showed a distorted, fish-eye view of the hallway—empty." | | 2 | "His heterochromatic eyes—one amber, one black—flickered over her with an intensity that made her skin prickle." | | 3 | "Should’ve told him to piss off, that whatever they’d had—whatever half-finished, half-broken thing it was—was over." | | 4 | "Knew the way his mind worked—layered, deliberate." | | 5 | "He studied her for a long moment, his gaze lingering on the crescent-shaped scar on her left wrist—old, faded, but still visible." | | 6 | "She remembered the way he’d looked at her the night they’d met—like she was a puzzle he was determined to solve." | | 7 | "She studied him—the way his suit jacket pulled slightly across his shoulders, the way his heterochromatic eyes gave nothing away." | | 8 | "Not when he was looking at her like that—like she was the only thing in the world worth seeing." |
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| 89.23% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1034 | | adjectiveStacks | 1 | | stackExamples | | 0 | "half-finished, half-broken thing" |
| | adverbCount | 47 | | adverbRatio | 0.045454545454545456 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.012572533849129593 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 8.82 | | std | 6.48 | | cv | 0.734 | | sampleLengths | | 0 | 15 | | 1 | 9 | | 2 | 6 | | 3 | 9 | | 4 | 5 | | 5 | 3 | | 6 | 2 | | 7 | 1 | | 8 | 12 | | 9 | 36 | | 10 | 6 | | 11 | 12 | | 12 | 10 | | 13 | 8 | | 14 | 3 | | 15 | 3 | | 16 | 2 | | 17 | 11 | | 18 | 9 | | 19 | 11 | | 20 | 5 | | 21 | 24 | | 22 | 15 | | 23 | 16 | | 24 | 5 | | 25 | 7 | | 26 | 2 | | 27 | 21 | | 28 | 30 | | 29 | 6 | | 30 | 6 | | 31 | 4 | | 32 | 8 | | 33 | 16 | | 34 | 7 | | 35 | 14 | | 36 | 7 | | 37 | 16 | | 38 | 14 | | 39 | 3 | | 40 | 2 | | 41 | 4 | | 42 | 5 | | 43 | 12 | | 44 | 4 | | 45 | 3 | | 46 | 7 | | 47 | 2 | | 48 | 10 | | 49 | 9 |
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| 56.29% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3576158940397351 | | totalSentences | 151 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 95 | | matches | | 0 | "Too deliberate to be a" | | 1 | "Too tall for Eva, too" | | 2 | "Just the faint scuff of" | | 3 | "Instead, she stepped back, pressing" | | 4 | "Instead, she reached up, her" |
| | ratio | 0.053 | |
| 76.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 95 | | matches | | 0 | "She froze, the dishcloth still" | | 1 | "She didn’t have time for" | | 2 | "She padded to the door," | | 3 | "She pulled the door open." | | 4 | "His heterochromatic eyes—one amber, one" | | 5 | "He tilted his head, just" | | 6 | "She should’ve slammed the door" | | 7 | "He stepped inside, the scent" | | 8 | "His gaze swept the room," | | 9 | "He turned to face her," | | 10 | "She knew that tone." | | 11 | "He wasn’t here for a" | | 12 | "She gestured sharply to the" | | 13 | "He settled into the chair," | | 14 | "He studied her for a" | | 15 | "His voice was smooth, unruffled" | | 16 | "She remembered the way he’d" | | 17 | "she said finally" | | 18 | "He sighed, shifting in his" | | 19 | "She stopped mid-step." |
| | ratio | 0.358 | |
| 86.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 95 | | matches | | 0 | "The knock came just as" | | 1 | "She froze, the dishcloth still" | | 2 | "Eva never knocked like that." | | 3 | "Neither did Silas." | | 4 | "Rory exhaled through her nose" | | 5 | "The flat was a mess—takeaway" | | 6 | "She didn’t have time for" | | 7 | "She padded to the door," | | 8 | "The peephole showed a distorted," | | 9 | "A shadow shifted." | | 10 | "Rory rolled her eyes and" | | 11 | "She pulled the door open." | | 12 | "Lucien Moreau stood there, one" | | 13 | "The hallway light caught the" | | 14 | "His heterochromatic eyes—one amber, one" | | 15 | "Rory’s fingers tightened around the" | | 16 | "He tilted his head, just" | | 17 | "She should’ve slammed the door" | | 18 | "Lucien didn’t wait for a" | | 19 | "He stepped inside, the scent" |
| | ratio | 0.747 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "His heterochromatic eyes—one amber, one black—flickered over her with an intensity that made her skin prickle." | | 1 | "Ptolemy, who had been watching from the top of the bookshelf, let out a low, suspicious mrrow and slunk out of sight." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "He stood, the cane tapping once against the floor as he straightened" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 53 | | tagDensity | 0.057 | | leniency | 0.113 | | rawRatio | 0.333 | | effectiveRatio | 0.038 | |