| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 846 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 29.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 846 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "echoing" | | 1 | "chill" | | 2 | "scanning" | | 3 | "pulse" | | 4 | "eyebrow" | | 5 | "familiar" | | 6 | "measured" | | 7 | "could feel" | | 8 | "raced" | | 9 | "delving" | | 10 | "weight" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 45 | | matches | (empty) | |
| 47.62% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 45 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 844 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 636 | | uniqueNames | 14 | | maxNameDensity | 1.42 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 2 | | Quinn | 9 | | Veil | 4 | | Market | 1 | | London | 1 | | Brennan | 9 | | British | 1 | | Museum | 1 | | Eva | 2 | | Kowalski | 1 | | Compass | 3 | | Morris | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Brennan" | | 3 | "Museum" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Morris" |
| | places | | | globalScore | 0.792 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 844 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 35.17 | | std | 21.7 | | cv | 0.617 | | sampleLengths | | 0 | 80 | | 1 | 14 | | 2 | 52 | | 3 | 79 | | 4 | 14 | | 5 | 9 | | 6 | 41 | | 7 | 16 | | 8 | 62 | | 9 | 52 | | 10 | 9 | | 11 | 26 | | 12 | 8 | | 13 | 17 | | 14 | 64 | | 15 | 52 | | 16 | 38 | | 17 | 38 | | 18 | 15 | | 19 | 41 | | 20 | 16 | | 21 | 36 | | 22 | 45 | | 23 | 20 |
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| 89.67% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 45 | | matches | | 0 | "was disheveled" | | 1 | "was buried" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 103 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 59 | | ratio | 0.051 | | matches | | 0 | "She crouched down, picking up a book with a familiar insignia on the cover—the British Museum's restricted archives." | | 1 | "This man wasn't just a user; he was a researcher, like Eva Kowalski, her best friend's confidant." | | 2 | "This was no mere market; it was a nexus, a place where the veil between worlds was thin." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 638 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.017241379310344827 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.006269592476489028 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.31 | | std | 7.72 | | cv | 0.54 | | sampleLengths | | 0 | 20 | | 1 | 22 | | 2 | 25 | | 3 | 13 | | 4 | 10 | | 5 | 4 | | 6 | 26 | | 7 | 26 | | 8 | 9 | | 9 | 16 | | 10 | 17 | | 11 | 17 | | 12 | 20 | | 13 | 10 | | 14 | 4 | | 15 | 4 | | 16 | 5 | | 17 | 11 | | 18 | 30 | | 19 | 3 | | 20 | 13 | | 21 | 20 | | 22 | 18 | | 23 | 7 | | 24 | 17 | | 25 | 17 | | 26 | 3 | | 27 | 32 | | 28 | 9 | | 29 | 10 | | 30 | 16 | | 31 | 4 | | 32 | 4 | | 33 | 4 | | 34 | 13 | | 35 | 12 | | 36 | 17 | | 37 | 17 | | 38 | 18 | | 39 | 28 | | 40 | 24 | | 41 | 6 | | 42 | 14 | | 43 | 18 | | 44 | 11 | | 45 | 13 | | 46 | 14 | | 47 | 10 | | 48 | 5 | | 49 | 15 |
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| 61.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3898305084745763 | | totalSentences | 59 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 45 | | matches | | 0 | "she greeted, her own voice" | | 1 | "She knelt beside him, her" | | 2 | "She reached out, her fingers" | | 3 | "she said, standing up and" | | 4 | "She crouched down, picking up" | | 5 | "Her lips pressed into a" | | 6 | "She turned it over in" | | 7 | "she replied, tucking the compass" | | 8 | "She began to walk towards" | | 9 | "She could feel the energy" | | 10 | "He'd come here, perhaps seeking" | | 11 | "She turned to Brennan, who" | | 12 | "she declared, her voice echoing" | | 13 | "She wouldn't let history repeat" | | 14 | "She would uncover the truth," |
| | ratio | 0.333 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 45 | | matches | | 0 | "The detective's boots clanged Against" | | 1 | "Detective Harlow Quinn's breath misted" | | 2 | "The Veil Market, a place" | | 3 | "she greeted, her own voice" | | 4 | "Brennan, a young detective with" | | 5 | "Quinn's sharp jaw tightened as" | | 6 | "The man lay sprawled on" | | 7 | "She knelt beside him, her" | | 8 | "The man's attire was disheveled," | | 9 | "She reached out, her fingers" | | 10 | "she said, standing up and" | | 11 | "Brennan raised an eyebrow." | | 12 | "Quinn's brown eyes narrowed as" | | 13 | "Brennan shrugged, unconvinced." | | 14 | "Quinn's gaze drifted to the" | | 15 | "She crouched down, picking up" | | 16 | "Her lips pressed into a" | | 17 | "This man wasn't just a" | | 18 | "The Veil Compass." | | 19 | "She turned it over in" |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "She reached out, her fingers brushing against the man's wrist, feeling for a pulse that she knew wouldn't be there." | | 1 | "The man was a researcher, likely delving into mysteries that were better left untouched." | | 2 | "As she spoke, she felt the weight of her past, the loss of her partner, DS Morris, in a case that had eerie similarities to this one." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "she declared, her voice echoing off the stone walls" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "she declared (declare)" |
| | dialogueSentences | 20 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.2 | | effectiveRatio | 0.1 | |