| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "She looked around [around]" | | 1 | "he said quietly [quietly]" |
| | dialogueSentences | 43 | | tagDensity | 0.326 | | leniency | 0.651 | | rawRatio | 0.143 | | effectiveRatio | 0.093 | |
| 96.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1421 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 82.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1421 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "tracing" | | 1 | "navigating" | | 2 | "weight" | | 3 | "glinting" |
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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 | 2 | | narrationSentences | 104 | | matches | | 0 | "was scared" | | 1 | "felt guilty" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1421 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1182 | | uniqueNames | 19 | | maxNameDensity | 0.76 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Aurora | 4 | | Golden | 1 | | Empress | 1 | | Fitzrovia | 1 | | Raven | 1 | | Nest | 1 | | Prague | 1 | | Silas | 9 | | Si | 1 | | Evan | 5 | | Two | 1 | | Brendan | 1 | | Carter | 1 | | Soho | 2 | | Eva | 1 | | Cardiff | 2 | | Thames | 1 | | London | 3 | | Pre-Law | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Silas" | | 2 | "Evan" | | 3 | "Brendan" | | 4 | "Carter" | | 5 | "Eva" |
| | places | | 0 | "Fitzrovia" | | 1 | "Raven" | | 2 | "Prague" | | 3 | "Two" | | 4 | "Soho" | | 5 | "Cardiff" | | 6 | "Thames" | | 7 | "London" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 2 | | matches | | 0 | "quite line up with the others, the hidden back room she knew existed because she’d once heard a low voice through the floorboards at two in the morning" | | 1 | "felt like absolution" |
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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 | 1421 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 133 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 26.81 | | std | 26.6 | | cv | 0.992 | | sampleLengths | | 0 | 92 | | 1 | 76 | | 2 | 91 | | 3 | 38 | | 4 | 33 | | 5 | 43 | | 6 | 9 | | 7 | 10 | | 8 | 70 | | 9 | 4 | | 10 | 23 | | 11 | 1 | | 12 | 28 | | 13 | 15 | | 14 | 3 | | 15 | 5 | | 16 | 64 | | 17 | 7 | | 18 | 11 | | 19 | 6 | | 20 | 25 | | 21 | 24 | | 22 | 20 | | 23 | 43 | | 24 | 4 | | 25 | 100 | | 26 | 9 | | 27 | 5 | | 28 | 18 | | 29 | 11 | | 30 | 4 | | 31 | 35 | | 32 | 12 | | 33 | 13 | | 34 | 5 | | 35 | 83 | | 36 | 11 | | 37 | 5 | | 38 | 72 | | 39 | 11 | | 40 | 55 | | 41 | 7 | | 42 | 5 | | 43 | 2 | | 44 | 22 | | 45 | 56 | | 46 | 23 | | 47 | 23 | | 48 | 18 | | 49 | 2 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 104 | | matches | | 0 | "were papered" | | 1 | "were unhurried" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 210 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1192 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 39 | | adverbRatio | 0.03271812080536913 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007550335570469799 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 10.68 | | std | 8 | | cv | 0.748 | | sampleLengths | | 0 | 19 | | 1 | 21 | | 2 | 38 | | 3 | 14 | | 4 | 26 | | 5 | 9 | | 6 | 7 | | 7 | 18 | | 8 | 16 | | 9 | 16 | | 10 | 6 | | 11 | 11 | | 12 | 16 | | 13 | 42 | | 14 | 9 | | 15 | 20 | | 16 | 9 | | 17 | 4 | | 18 | 14 | | 19 | 15 | | 20 | 10 | | 21 | 9 | | 22 | 3 | | 23 | 7 | | 24 | 14 | | 25 | 5 | | 26 | 4 | | 27 | 5 | | 28 | 5 | | 29 | 3 | | 30 | 21 | | 31 | 19 | | 32 | 22 | | 33 | 5 | | 34 | 4 | | 35 | 6 | | 36 | 13 | | 37 | 2 | | 38 | 2 | | 39 | 1 | | 40 | 11 | | 41 | 7 | | 42 | 10 | | 43 | 6 | | 44 | 4 | | 45 | 5 | | 46 | 3 | | 47 | 5 | | 48 | 18 | | 49 | 6 |
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| 40.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2556390977443609 | | totalSentences | 133 | | uniqueOpeners | 34 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Instead she’d drawn a tight" |
| | ratio | 0.011 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 62 | | totalSentences | 95 | | matches | | 0 | "It was the kind of" | | 1 | "She'd been on her bike" | | 2 | "Her legs ached from the" | | 3 | "She pushed into The Raven's" | | 4 | "He didn't look up at" | | 5 | "He had a slight limp" | | 6 | "She knew the hours he" | | 7 | "She had not spoken to" | | 8 | "He saw her then." | | 9 | "His hazel eyes found her" | | 10 | "he said, as if he’d" | | 11 | "She shrugged off her jacket," | | 12 | "It was straight, shoulder-length, black," | | 13 | "She used to call him" | | 14 | "He set the glass down." | | 15 | "She smiled, quick and cool." | | 16 | "He studied her." | | 17 | "She was twenty-five now, five-six" | | 18 | "She’d told him once, after" | | 19 | "He’d never asked which childhood." |
| | ratio | 0.653 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 88 | | totalSentences | 95 | | matches | | 0 | "The green neon sign over" | | 1 | "It was the kind of" | | 2 | "She'd been on her bike" | | 3 | "Her legs ached from the" | | 4 | "She pushed into The Raven's" | | 5 | "The bell over the door" | | 6 | "The walls were papered with" | | 7 | "The smell of beer and" | | 8 | "Silas was behind it, polishing" | | 9 | "He didn't look up at" | | 10 | "The silver signet ring caught" | | 11 | "He had a slight limp" | | 12 | "Aurora had lived above him" | | 13 | "She knew the hours he" | | 14 | "She had not spoken to" | | 15 | "He saw her then." | | 16 | "His hazel eyes found her" | | 17 | "he said, as if he’d" | | 18 | "She shrugged off her jacket," | | 19 | "It was straight, shoulder-length, black," |
| | ratio | 0.926 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 99.13% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 3 | | matches | | 0 | "Silas was behind it, polishing a pint glass with a cloth that had been white once." | | 1 | "He had a slight limp when he moved, a hitch in his left leg from Prague, and even at the bar he stood with that quiet authority that had made her nervous the fi…" | | 2 | "The words landed with a precision that was infuriating because it was true." |
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| 17.86% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, as if he’d been expecting her and hadn’t at the same time" | | 1 | "he said, not unkindly" | | 2 | "she said, and the words surprised her with how easily they came" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 43 | | tagDensity | 0.279 | | leniency | 0.558 | | rawRatio | 0.083 | | effectiveRatio | 0.047 | |