| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 32 | | adverbTagCount | 2 | | adverbTags | | 0 | "His name tasted like [like]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 103 | | tagDensity | 0.311 | | leniency | 0.621 | | rawRatio | 0.063 | | effectiveRatio | 0.039 | |
| 79.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1992 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "slightly" | | 1 | "suddenly" | | 2 | "carefully" | | 3 | "slowly" | | 4 | "quickly" | | 5 | "very" | | 6 | "utterly" |
| |
| 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) | |
| 62.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1992 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "flickered" | | 2 | "constructed" | | 3 | "stomach" | | 4 | "silence" | | 5 | "calculated" | | 6 | "pulse" | | 7 | "footsteps" | | 8 | "traced" | | 9 | "weight" | | 10 | "tension" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "stomach dropped/sank" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "stomach dropped" | | 2 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 116 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 116 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1991 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1132 | | uniqueNames | 12 | | maxNameDensity | 1.33 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Moreau | 2 | | Rory | 12 | | Amber | 1 | | Lucien | 15 | | Evan | 4 | | Frenchman | 1 | | London | 1 | | Yu-Fei | 1 | | Don | 2 | | Low | 1 | | Marsh | 1 | | Ptolemy | 3 |
| | persons | | 0 | "Moreau" | | 1 | "Rory" | | 2 | "Amber" | | 3 | "Lucien" | | 4 | "Evan" | | 5 | "Low" | | 6 | "Ptolemy" |
| | places | | 0 | "London" | | 1 | "Yu-Fei" | | 2 | "Marsh" |
| | globalScore | 0.837 | | windowScore | 1 | |
| 59.64% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 3 | | matches | | 0 | "tasted like copper on her tongue" | | 1 | "seemed inadequate the books and scrolls and research notes she'd accumulated over months of hiding now laughably exposed" | | 2 | "looked like outside Yu-Fei's restaurant," |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1991 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 102 | | mean | 19.52 | | std | 14.18 | | cv | 0.727 | | sampleLengths | | 0 | 18 | | 1 | 27 | | 2 | 44 | | 3 | 12 | | 4 | 33 | | 5 | 7 | | 6 | 6 | | 7 | 32 | | 8 | 19 | | 9 | 27 | | 10 | 29 | | 11 | 18 | | 12 | 15 | | 13 | 44 | | 14 | 5 | | 15 | 7 | | 16 | 26 | | 17 | 5 | | 18 | 46 | | 19 | 9 | | 20 | 3 | | 21 | 14 | | 22 | 53 | | 23 | 9 | | 24 | 32 | | 25 | 4 | | 26 | 4 | | 27 | 22 | | 28 | 56 | | 29 | 8 | | 30 | 14 | | 31 | 37 | | 32 | 23 | | 33 | 46 | | 34 | 24 | | 35 | 28 | | 36 | 5 | | 37 | 11 | | 38 | 31 | | 39 | 5 | | 40 | 6 | | 41 | 10 | | 42 | 40 | | 43 | 18 | | 44 | 35 | | 45 | 22 | | 46 | 7 | | 47 | 2 | | 48 | 20 | | 49 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 116 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 216 | | matches | | 0 | "was hiding" | | 1 | "was already moving" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 184 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1135 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 43 | | adverbRatio | 0.03788546255506608 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.010572687224669603 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 10.82 | | std | 7.57 | | cv | 0.7 | | sampleLengths | | 0 | 8 | | 1 | 10 | | 2 | 27 | | 3 | 14 | | 4 | 12 | | 5 | 12 | | 6 | 6 | | 7 | 9 | | 8 | 3 | | 9 | 30 | | 10 | 3 | | 11 | 7 | | 12 | 6 | | 13 | 12 | | 14 | 10 | | 15 | 10 | | 16 | 17 | | 17 | 2 | | 18 | 18 | | 19 | 6 | | 20 | 3 | | 21 | 26 | | 22 | 3 | | 23 | 12 | | 24 | 6 | | 25 | 9 | | 26 | 6 | | 27 | 8 | | 28 | 3 | | 29 | 21 | | 30 | 12 | | 31 | 5 | | 32 | 7 | | 33 | 7 | | 34 | 19 | | 35 | 5 | | 36 | 23 | | 37 | 23 | | 38 | 3 | | 39 | 6 | | 40 | 3 | | 41 | 14 | | 42 | 32 | | 43 | 21 | | 44 | 5 | | 45 | 4 | | 46 | 15 | | 47 | 17 | | 48 | 4 | | 49 | 4 |
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| 48.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.33152173913043476 | | totalSentences | 184 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 111 | | matches | | 0 | "Then his footsteps, soft on" | | 1 | "Then a sound cut through" | | 2 | "Then something shifted in his" | | 3 | "Then he reached into his" |
| | ratio | 0.036 | |
| 54.23% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 111 | | matches | | 0 | "His platinum hair was slightly" | | 1 | "His name tasted like copper" | | 2 | "He pushed past her before" | | 3 | "She crossed her arms, positioning" | | 4 | "He turned to face her," | | 5 | "She hadn't succeeded." | | 6 | "She kept her voice level" | | 7 | "He laughed, but it lacked" | | 8 | "It always did." | | 9 | "She felt her jaw tighten," | | 10 | "It was exactly what Evan" | | 11 | "She'd spent three months trying" | | 12 | "she said slowly" | | 13 | "He reached into his jacket" | | 14 | "She pulled back quickly, tearing" | | 15 | "Her photograph, taken from what" | | 16 | "He made a sound that" | | 17 | "It couldn't be." | | 18 | "He did transactions, exchanges, carefully" | | 19 | "His voice had gone quiet" |
| | ratio | 0.414 | |
| 45.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 111 | | matches | | 0 | "The knock came at half-two" | | 1 | "Rory yanked it open, ready" | | 2 | "Lucien leaned against her doorframe," | | 3 | "The ivory caught the dim" | | 4 | "His platinum hair was slightly" | | 5 | "Lucien Moreau did not do" | | 6 | "His name tasted like copper" | | 7 | "He pushed past her before" | | 8 | "Rory shut the door, engaging" | | 9 | "Ptolemy hissed from his perch" | | 10 | "The tabby had always possessed" | | 11 | "She crossed her arms, positioning" | | 12 | "He turned to face her," | | 13 | "Amber and obsidian, demon and" | | 14 | "Something flickered across his features," | | 15 | "She hadn't succeeded." | | 16 | "She kept her voice level" | | 17 | "He laughed, but it lacked" | | 18 | "The flat felt smaller with" | | 19 | "It always did." |
| | ratio | 0.829 | |
| 45.05% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 111 | | matches | | 0 | "Because that wasn't what this" |
| | ratio | 0.009 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "The mysterious Frenchman who'd appeared in her life right when she'd finally escaped." | | 1 | "The man who'd offered her information about her ex's debts and obligations, information she'd never asked for but couldn't stop wanting." | | 2 | "The smile that crossed Lucien's face was sharp and dangerous and utterly unlike his usual polished expressions." |
| |
| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 32 | | uselessAdditionCount | 2 | | matches | | 0 | "He turned, and the lamplight caught his mismatched eyes" | | 1 | "His eyes had, the amber almost entirely swallowed by shadow" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 4 | | fancyTags | | 0 | "He pushed (push)" | | 1 | "He laughed (laugh)" | | 2 | "She laughed (laugh)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 103 | | tagDensity | 0.049 | | leniency | 0.097 | | rawRatio | 0.8 | | effectiveRatio | 0.078 | |