| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "Lucien quoted softly [softly]" | | 1 | "he ordered softly [softly]" |
| | dialogueSentences | 47 | | tagDensity | 0.383 | | leniency | 0.766 | | rawRatio | 0.111 | | effectiveRatio | 0.085 | |
| 85.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1733 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "truly" | | 1 | "softly" | | 2 | "suddenly" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 50.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1733 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | word | "the last thing" | | count | 1 |
| | 12 | | | 13 | | | 14 | |
| | highlights | | 0 | "velvet" | | 1 | "pulse" | | 2 | "scanning" | | 3 | "familiar" | | 4 | "unreadable" | | 5 | "tracing" | | 6 | "silk" | | 7 | "predator" | | 8 | "treacherous" | | 9 | "eyebrow" | | 10 | "race" | | 11 | "the last thing" | | 12 | "traced" | | 13 | "shattered" | | 14 | "perfect" |
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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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 134 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 163 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1733 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1284 | | uniqueNames | 18 | | maxNameDensity | 1.01 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Moreau | 1 | | Marseille | 1 | | Mrs | 2 | | Brick | 1 | | Lane | 1 | | Rory | 8 | | Electricity | 1 | | Heat | 1 | | Lucien | 13 | | Golden | 1 | | Empress | 1 | | Cardiff | 2 | | Eva | 2 | | Evan | 2 | | Nausea | 1 | | Whitechapel | 1 | | French | 1 | | Ptolemy | 4 |
| | persons | | 0 | "Moreau" | | 1 | "Mrs" | | 2 | "Rory" | | 3 | "Electricity" | | 4 | "Heat" | | 5 | "Lucien" | | 6 | "Eva" | | 7 | "Evan" | | 8 | "Nausea" | | 9 | "Ptolemy" |
| | places | | 0 | "Marseille" | | 1 | "Brick" | | 2 | "Lane" | | 3 | "Golden" | | 4 | "Cardiff" | | 5 | "Whitechapel" | | 6 | "French" |
| | globalScore | 0.994 | | windowScore | 1 | |
| 97.92% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like prophecy" | | 1 | "felt like a trap, all its cramped corne" |
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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 | 1733 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 163 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 29.88 | | std | 17.99 | | cv | 0.602 | | sampleLengths | | 0 | 52 | | 1 | 8 | | 2 | 20 | | 3 | 19 | | 4 | 62 | | 5 | 2 | | 6 | 48 | | 7 | 41 | | 8 | 21 | | 9 | 27 | | 10 | 46 | | 11 | 25 | | 12 | 26 | | 13 | 32 | | 14 | 53 | | 15 | 44 | | 16 | 58 | | 17 | 36 | | 18 | 41 | | 19 | 40 | | 20 | 10 | | 21 | 24 | | 22 | 6 | | 23 | 35 | | 24 | 43 | | 25 | 22 | | 26 | 52 | | 27 | 55 | | 28 | 6 | | 29 | 12 | | 30 | 52 | | 31 | 5 | | 32 | 35 | | 33 | 44 | | 34 | 3 | | 35 | 49 | | 36 | 31 | | 37 | 8 | | 38 | 43 | | 39 | 43 | | 40 | 12 | | 41 | 42 | | 42 | 57 | | 43 | 9 | | 44 | 4 | | 45 | 66 | | 46 | 47 | | 47 | 18 | | 48 | 21 | | 49 | 40 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 134 | | matches | | 0 | "been organised" | | 1 | "being cocked" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 234 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 163 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1286 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.03576982892690513 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008553654743390357 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 163 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 163 | | mean | 10.63 | | std | 7 | | cv | 0.659 | | sampleLengths | | 0 | 11 | | 1 | 21 | | 2 | 20 | | 3 | 8 | | 4 | 6 | | 5 | 14 | | 6 | 11 | | 7 | 8 | | 8 | 20 | | 9 | 10 | | 10 | 4 | | 11 | 5 | | 12 | 23 | | 13 | 2 | | 14 | 16 | | 15 | 13 | | 16 | 5 | | 17 | 14 | | 18 | 5 | | 19 | 3 | | 20 | 5 | | 21 | 11 | | 22 | 6 | | 23 | 11 | | 24 | 14 | | 25 | 7 | | 26 | 10 | | 27 | 8 | | 28 | 9 | | 29 | 14 | | 30 | 7 | | 31 | 10 | | 32 | 15 | | 33 | 10 | | 34 | 15 | | 35 | 11 | | 36 | 8 | | 37 | 7 | | 38 | 14 | | 39 | 18 | | 40 | 4 | | 41 | 13 | | 42 | 12 | | 43 | 24 | | 44 | 11 | | 45 | 33 | | 46 | 7 | | 47 | 20 | | 48 | 31 | | 49 | 5 |
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| 64.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4110429447852761 | | totalSentences | 163 | | uniqueOpeners | 67 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 123 | | matches | | 0 | "Instead it settled low in" | | 1 | "Instead she curled her fingers" | | 2 | "Instead it only made her" |
| | ratio | 0.024 | |
| 93.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 123 | | matches | | 0 | "His mismatched eyes caught the" | | 1 | "She tried to slam the" | | 2 | "His voice carried that low" | | 3 | "She would not step back." | | 4 | "She would not remember the" | | 5 | "He pushed the door wider." | | 6 | "She let him." | | 7 | "He crossed the threshold, cane" | | 8 | "He lifted a scroll, eyes" | | 9 | "She snatched the parchment from" | | 10 | "She turned away, busying her" | | 11 | "He'd kissed it once, tongue" | | 12 | "She kept her voice cool," | | 13 | "He stepped closer" | | 14 | "His hand rose, hovered near" | | 15 | "Her abusive ex." | | 16 | "She'd thought Evan was merely" | | 17 | "Her fingers tightened on the" | | 18 | "She could still taste blood" | | 19 | "She whirled to face him." |
| | ratio | 0.317 | |
| 45.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 123 | | matches | | 0 | "Rory yanked back the last" | | 1 | "Lucien Moreau filled the narrow" | | 2 | "His mismatched eyes caught the" | | 3 | "The word scraped out of" | | 4 | "She tried to slam the" | | 5 | "The ivory handle of his" | | 6 | "His voice carried that low" | | 7 | "The flat's stale air stirred" | | 8 | "Ptolemy wound between her ankles," | | 9 | "Rory planted her feet." | | 10 | "She would not step back." | | 11 | "She would not remember the" | | 12 | "Lucien tilted his head, studying" | | 13 | "The charcoal suit hugged his" | | 14 | "The kind of creature who" | | 15 | "He pushed the door wider." | | 16 | "She let him." | | 17 | "Patel could hear everything, and" | | 18 | "Patel had enough gossip to" | | 19 | "He crossed the threshold, cane" |
| | ratio | 0.829 | |
| 81.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 123 | | matches | | 0 | "Because the alternative was wrestling" | | 1 | "Now it felt too small" |
| | ratio | 0.016 | |
| 79.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 5 | | matches | | 0 | "The kind of creature who collected favours like currency and discarded women like tissues." | | 1 | "Turns out the supernatural underworld had its own opinions about men who trafficked more than just fists." | | 2 | "She pushed past him, needing air that didn't carry his scent." | | 3 | "His hands roamed her back, relearning every curve like a man reclaiming stolen territory." | | 4 | "The black eye flared with something demonic, a spark of actual flame that should have terrified her." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 3 | | matches | | 0 | "He crossed, cane tapping once against the warped floorboards" | | 1 | "She snatched, their hands brushing" | | 2 | "The amber eye warmed, unreadable" |
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| 64.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "Lucien quoted softly (quote)" | | 1 | "she whispered (whisper)" | | 2 | "he murmured (murmur)" | | 3 | "he ordered softly (order)" |
| | dialogueSentences | 47 | | tagDensity | 0.085 | | leniency | 0.17 | | rawRatio | 1 | | effectiveRatio | 0.17 | |