| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 2 | | adverbTags | | 0 | "Herrera asked suddenly [suddenly]" | | 1 | "he said softly [softly]" |
| | dialogueSentences | 27 | | tagDensity | 0.481 | | leniency | 0.963 | | rawRatio | 0.154 | | effectiveRatio | 0.148 | |
| 78.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1170 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "cautiously" | | 1 | "suddenly" | | 2 | "softly" |
| |
| 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) | |
| 5.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1170 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "perfect" | | 1 | "ceaseless" | | 2 | "throb" | | 3 | "scanned" | | 4 | "stomach" | | 5 | "weight" | | 6 | "reminder" | | 7 | "flickered" | | 8 | "maw" | | 9 | "cacophony" | | 10 | "pulsed" | | 11 | "familiar" | | 12 | "amidst" | | 13 | "beacon" | | 14 | "gloom" | | 15 | "gleaming" | | 16 | "stark" | | 17 | "simmering" | | 18 | "whisper" | | 19 | "flicker" | | 20 | "echoing" | | 21 | "silence" |
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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 | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1172 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 994 | | uniqueNames | 16 | | maxNameDensity | 1.71 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Blackwood | 3 | | Tube | 1 | | London | 2 | | Metropolitan | 1 | | Police | 1 | | Silas | 8 | | Morris | 1 | | Soho | 1 | | Veil | 1 | | Market | 1 | | Glock | 2 | | Tomás | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Harlow | 17 |
| | persons | | 0 | "Blackwood" | | 1 | "Police" | | 2 | "Silas" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Harlow" |
| | places | | | globalScore | 0.645 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | 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 | 1172 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 23.44 | | std | 15.67 | | cv | 0.668 | | sampleLengths | | 0 | 55 | | 1 | 15 | | 2 | 42 | | 3 | 34 | | 4 | 56 | | 5 | 3 | | 6 | 4 | | 7 | 56 | | 8 | 46 | | 9 | 17 | | 10 | 40 | | 11 | 36 | | 12 | 20 | | 13 | 2 | | 14 | 29 | | 15 | 47 | | 16 | 41 | | 17 | 8 | | 18 | 3 | | 19 | 58 | | 20 | 5 | | 21 | 29 | | 22 | 45 | | 23 | 13 | | 24 | 4 | | 25 | 19 | | 26 | 4 | | 27 | 37 | | 28 | 11 | | 29 | 19 | | 30 | 16 | | 31 | 23 | | 32 | 12 | | 33 | 30 | | 34 | 7 | | 35 | 21 | | 36 | 23 | | 37 | 8 | | 38 | 25 | | 39 | 28 | | 40 | 16 | | 41 | 22 | | 42 | 18 | | 43 | 29 | | 44 | 15 | | 45 | 19 | | 46 | 4 | | 47 | 29 | | 48 | 12 | | 49 | 17 |
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| 98.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 99 | | matches | | 0 | "was gone" | | 1 | "were crammed" |
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| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 176 | | matches | | 0 | "was breathing" | | 1 | "was talking" | | 2 | "wasn’t buying" | | 3 | "was handing" |
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| 67.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 113 | | ratio | 0.027 | | matches | | 0 | "The rational part of her – the years of training, the ingrained protocols of the Metropolitan Police – screamed at her to call for backup." | | 1 | "A low hum of activity, a cacophony of unfamiliar sounds – bartering, laughter, the clatter of unseen objects." | | 2 | "Herrera’s expression didn’t change, but Harlow detected a flicker of something in his eyes – a brief, almost imperceptible flash of sadness." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 992 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.034274193548387094 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.014112903225806451 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 10.37 | | std | 5.2 | | cv | 0.501 | | sampleLengths | | 0 | 7 | | 1 | 17 | | 2 | 15 | | 3 | 16 | | 4 | 15 | | 5 | 11 | | 6 | 14 | | 7 | 8 | | 8 | 5 | | 9 | 4 | | 10 | 16 | | 11 | 18 | | 12 | 18 | | 13 | 12 | | 14 | 9 | | 15 | 17 | | 16 | 3 | | 17 | 4 | | 18 | 18 | | 19 | 3 | | 20 | 4 | | 21 | 14 | | 22 | 13 | | 23 | 4 | | 24 | 11 | | 25 | 25 | | 26 | 10 | | 27 | 6 | | 28 | 4 | | 29 | 7 | | 30 | 13 | | 31 | 9 | | 32 | 11 | | 33 | 7 | | 34 | 16 | | 35 | 20 | | 36 | 20 | | 37 | 2 | | 38 | 17 | | 39 | 12 | | 40 | 16 | | 41 | 9 | | 42 | 4 | | 43 | 18 | | 44 | 5 | | 45 | 8 | | 46 | 15 | | 47 | 13 | | 48 | 8 | | 49 | 3 |
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| 69.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4336283185840708 | | totalSentences | 113 | | uniqueOpeners | 49 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Suddenly, the market erupted." |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 95 | | matches | | 0 | "He was fast, deceptively so" | | 1 | "she called out, her voice" | | 2 | "He didn’t bother to respond," | | 3 | "She scanned the crowd, searching" | | 4 | "He was gone." | | 5 | "She knew that grate." | | 6 | "She hesitated, the rain drumming" | | 7 | "He was a threat." | | 8 | "It was a relic from" | | 9 | "She pulled the grate open" | | 10 | "It was the noise." | | 11 | "She'd heard whispers, of course." | | 12 | "It was even more bizarre" | | 13 | "She spotted a familiar figure" | | 14 | "His coat, a splash of" | | 15 | "He was talking to a" | | 16 | "It was Tomás Herrera." | | 17 | "He leaned against a stall" | | 18 | "he said, gesturing vaguely at" | | 19 | "she said, her gaze fixed" |
| | ratio | 0.284 | |
| 33.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 95 | | matches | | 0 | "The knife tasted of iron" | | 1 | "Harlow pressed her shoulder against" | | 2 | "He was fast, deceptively so" | | 3 | "The alley reeked of stale" | | 4 | "she called out, her voice" | | 5 | "He didn’t bother to respond," | | 6 | "This time the blade grazed" | | 7 | "Harlow grunted, forcing herself to" | | 8 | "Silas Blackwood was bad news." | | 9 | "The rain intensified, plastering his" | | 10 | "Harlow surged after him, ignoring" | | 11 | "The main road was a" | | 12 | "Silas vanished into the throng," | | 13 | "Harlow cursed, her breath misting" | | 14 | "She scanned the crowd, searching" | | 15 | "He was gone." | | 16 | "A puddle reflected a hint" | | 17 | "Harlow’s stomach clenched." | | 18 | "She knew that grate." | | 19 | "Locals called it An abandoned" |
| | ratio | 0.853 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 95 | | matches | | 0 | "To assess the situation, secure" | | 1 | "Now, the folklore was breathing" |
| | ratio | 0.021 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "The stench of damp earth and something indefinably older wafted up, a subterranean breath that tasted of decay and secrets." | | 1 | "Vials filled with viscous, iridescent liquids sat next to bundles of dried herbs that smelled of alien spices." | | 2 | "Harlow started moving towards them, her boots crunching on the damp concrete floor." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 4 | | matches | | 0 | "Harlow said, her voice tight" | | 1 | "He paused, his gaze lingering on her injured arm" | | 2 | "Herrera asked suddenly, his tone hardening" | | 3 | "Harlow said, her voice barely a whisper" |
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| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "Silas sneered (sneer)" |
| | dialogueSentences | 27 | | tagDensity | 0.37 | | leniency | 0.741 | | rawRatio | 0.2 | | effectiveRatio | 0.148 | |