| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 567 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 64.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 567 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flickered" | | 1 | "apprehension" | | 2 | "chill" | | 3 | "otherworldly" |
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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 | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 575 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 463 | | uniqueNames | 8 | | maxNameDensity | 1.73 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 8 | | Quinn | 1 | | Tube | 1 | | Kowalski | 1 | | Evie | 8 | | Carpathian | 1 | | Sala | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Evie" |
| | places | | | globalScore | 0.636 | | windowScore | 0.667 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed bent on sabotaging her own career via irksome persistence" | | 1 | "quite fit her usual joie de vivre" |
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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 | 575 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 98.11% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 15 | | mean | 38.33 | | std | 18.91 | | cv | 0.493 | | sampleLengths | | 0 | 66 | | 1 | 51 | | 2 | 49 | | 3 | 29 | | 4 | 19 | | 5 | 40 | | 6 | 45 | | 7 | 29 | | 8 | 9 | | 9 | 37 | | 10 | 29 | | 11 | 55 | | 12 | 78 | | 13 | 30 | | 14 | 9 |
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| 76.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 48 | | matches | | 0 | "was tied" | | 1 | "was disheveled" | | 2 | "was glued" | | 3 | "was drawn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 76 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 57 | | ratio | 0.123 | | matches | | 0 | "She tamped down a shiver — cold, or perhaps apprehension." | | 1 | "Not that it mattered — evidence be damned, the bloody mess that awaited her in the tunnel didn't care about fashion." | | 2 | "DC \"Evie\" Kowalski — the newbie reporter who seemed bent on sabotaging her own career via irksome persistence." | | 3 | "Asphyxiation was unusual — unless it was tied to the occult." | | 4 | "Strange hieroglyphs dotted the wall — they looked Carpathian to her untrained eye." | | 5 | "She's practiced detachment; all life is cheap to Sala." | | 6 | "Miraculous melange — bullet foul." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 421 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.0332541567695962 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.014251781472684086 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 10.09 | | std | 6.07 | | cv | 0.602 | | sampleLengths | | 0 | 20 | | 1 | 12 | | 2 | 10 | | 3 | 24 | | 4 | 12 | | 5 | 4 | | 6 | 14 | | 7 | 21 | | 8 | 13 | | 9 | 18 | | 10 | 18 | | 11 | 10 | | 12 | 19 | | 13 | 5 | | 14 | 11 | | 15 | 3 | | 16 | 25 | | 17 | 15 | | 18 | 2 | | 19 | 11 | | 20 | 2 | | 21 | 3 | | 22 | 9 | | 23 | 13 | | 24 | 5 | | 25 | 8 | | 26 | 9 | | 27 | 3 | | 28 | 9 | | 29 | 9 | | 30 | 6 | | 31 | 4 | | 32 | 22 | | 33 | 5 | | 34 | 14 | | 35 | 15 | | 36 | 7 | | 37 | 2 | | 38 | 8 | | 39 | 9 | | 40 | 10 | | 41 | 10 | | 42 | 9 | | 43 | 17 | | 44 | 16 | | 45 | 1 | | 46 | 14 | | 47 | 13 | | 48 | 9 | | 49 | 7 |
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| 98.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6666666666666666 | | totalSentences | 57 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 43 | | matches | | 0 | "She tamped down a shiver" | | 1 | "Her boots splashed in a" | | 2 | "She'd worn her good suit" | | 3 | "She glanced around." | | 4 | "It didn't quite fit her" | | 5 | "She didn't usually cover hush-hush" | | 6 | "Her gray hair was disheveled," | | 7 | "She's practiced detachment; all life" | | 8 | "She can never let herself" | | 9 | "Her gaze was drawn to" |
| | ratio | 0.233 | |
| 87.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 43 | | matches | | 0 | "The stench of ozone and" | | 1 | "She tamped down a shiver" | | 2 | "The murder of a high-profile" | | 3 | "Her boots splashed in a" | | 4 | "Harlow muttered a curse." | | 5 | "She'd worn her good suit" | | 6 | "Several officers milled about, but" | | 7 | "DC Kowalski — the newbie" | | 8 | "Evie chirped, far too brightly" | | 9 | "Harlow said, not unkindly" | | 10 | "Evie consulted her notebook" | | 11 | "Asphyxiation was unusual — unless" | | 12 | "She glanced around." | | 13 | "Mist floated through puncture-like gaps" | | 14 | "Harlow shook off a chill." | | 15 | "A flashbulb's pop snapped her" | | 16 | "Evie started, tucking a curl" | | 17 | "It didn't quite fit her" | | 18 | "Evie mumbled, sidling past" | | 19 | "Harlow watched her for a" |
| | ratio | 0.744 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow said, not unkindly" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Evie chirped (chirp)" | | 1 | "Evie mumbled (mumble)" |
| | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.667 | | effectiveRatio | 0.5 | |