| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 906 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "suddenly" | | 1 | "cautiously" | | 2 | "slowly" |
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| 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) | |
| 17.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 906 | | totalAiIsms | 15 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "down her spine" | | count | 1 |
| | 9 | | | 10 | |
| | highlights | | 0 | "scanning" | | 1 | "glistening" | | 2 | "determined" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "quickened" | | 6 | "scanned" | | 7 | "chill" | | 8 | "down her spine" | | 9 | "raced" | | 10 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 64 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 64 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 903 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 4.17% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 720 | | uniqueNames | 10 | | maxNameDensity | 2.92 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 21 | | Tomás | 10 | | Herrera | 1 | | Taking | 1 | | Silas | 5 | | Raven | 1 | | Nest | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Silas" | | 5 | "Raven" |
| | places | | | globalScore | 0.042 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "quite put her finger on it" |
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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 | 903 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 81.14% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 31.14 | | std | 13.51 | | cv | 0.434 | | sampleLengths | | 0 | 37 | | 1 | 37 | | 2 | 52 | | 3 | 37 | | 4 | 39 | | 5 | 46 | | 6 | 34 | | 7 | 52 | | 8 | 64 | | 9 | 40 | | 10 | 31 | | 11 | 55 | | 12 | 31 | | 13 | 26 | | 14 | 13 | | 15 | 29 | | 16 | 18 | | 17 | 22 | | 18 | 28 | | 19 | 13 | | 20 | 28 | | 21 | 19 | | 22 | 28 | | 23 | 8 | | 24 | 36 | | 25 | 23 | | 26 | 20 | | 27 | 11 | | 28 | 26 |
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| 99.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 64 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 28.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 75 | | ratio | 0.04 | | matches | | 0 | "There he was - Tomás Herrera, the elusive medic who had been supplying the supernatural underground with illicit medical treatments." | | 1 | "There - she spotted him weaving through the throng of people, heading towards a recessed doorway at the far end of the market." | | 2 | "Quinn narrowed her eyes, recognizing the man as Silas, the owner of the Raven's Nest bar - a known gathering place for the supernatural underground." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 501 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.017964071856287425 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.007984031936127744 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 12.04 | | std | 5.65 | | cv | 0.47 | | sampleLengths | | 0 | 22 | | 1 | 15 | | 2 | 20 | | 3 | 15 | | 4 | 2 | | 5 | 14 | | 6 | 9 | | 7 | 15 | | 8 | 14 | | 9 | 3 | | 10 | 4 | | 11 | 8 | | 12 | 9 | | 13 | 13 | | 14 | 14 | | 15 | 7 | | 16 | 8 | | 17 | 10 | | 18 | 12 | | 19 | 15 | | 20 | 19 | | 21 | 3 | | 22 | 18 | | 23 | 13 | | 24 | 18 | | 25 | 18 | | 26 | 16 | | 27 | 18 | | 28 | 15 | | 29 | 13 | | 30 | 18 | | 31 | 10 | | 32 | 23 | | 33 | 7 | | 34 | 16 | | 35 | 15 | | 36 | 13 | | 37 | 12 | | 38 | 12 | | 39 | 18 | | 40 | 6 | | 41 | 13 | | 42 | 12 | | 43 | 13 | | 44 | 13 | | 45 | 8 | | 46 | 5 | | 47 | 25 | | 48 | 4 | | 49 | 5 |
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| 64.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4 | | totalSentences | 75 | | uniqueOpeners | 30 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Suddenly, Tomás veered off the" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 60 | | matches | | 0 | "Her eyes were laser-focused, scanning" | | 1 | "She'd been chasing him for" | | 2 | "He put on an extra" | | 3 | "She swerved around a delivery" | | 4 | "She saw him up ahead," | | 5 | "She pushed the car to" | | 6 | "She threw the car into" | | 7 | "She could hear the sound" | | 8 | "She emerged into a cavernous" | | 9 | "she yelled, but her voice" | | 10 | "She burst through the doorway," | | 11 | "She was familiar, but Quinn" |
| | ratio | 0.2 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 60 | | matches | | 0 | "The rain pelted against the" | | 1 | "Her eyes were laser-focused, scanning" | | 2 | "She'd been chasing him for" | | 3 | "Quinn gripped the steering wheel," | | 4 | "The car surged forward, closing" | | 5 | "Tomás glanced over his shoulder," | | 6 | "He put on an extra" | | 7 | "Quinn's jaw tightened." | | 8 | "She swerved around a delivery" | | 9 | "Tomás darted down a side" | | 10 | "Quinn cursed under her breath" | | 11 | "The streets were emptier here," | | 12 | "Tomás was fast, but Quinn" | | 13 | "She saw him up ahead," | | 14 | "She pushed the car to" | | 15 | "Quinn gritted her teeth and" | | 16 | "The alley opened up into" | | 17 | "Quinn didn't hesitate." | | 18 | "She threw the car into" | | 19 | "Tomás took the stairs two" |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "There he was - Tomás Herrera, the elusive medic who had been supplying the supernatural underground with illicit medical treatments." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 4 | | matches | | 0 | "She could, but the stairwell was pitch black" | | 1 | "she yelled, but her voice was drowned out by the din of the market" | | 2 | "the woman said, her tone dripping with disdain" | | 3 | "Quinn demanded, her gun still trained on Silas" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "she yelled (yell)" | | 1 | "she spat (spit)" | | 2 | "a new voice interjected (interject)" | | 3 | "Quinn demanded (demand)" |
| | dialogueSentences | 17 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0.667 | | effectiveRatio | 0.471 | |