| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2074 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "really" | | 3 | "completely" |
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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) | |
| 68.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2074 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "quickened" | | 1 | "glinting" | | 2 | "fluttered" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "pulsed" | | 6 | "standard" | | 7 | "processed" | | 8 | "shattered" | | 9 | "raced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 184 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 205 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2074 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.72% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1744 | | uniqueNames | 16 | | maxNameDensity | 1.61 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 28 | | Soho | 2 | | Raven | 3 | | Nest | 4 | | Morris | 4 | | Herrera | 16 | | Camden | 3 | | Saint | 3 | | Christopher | 3 | | Tube | 1 | | Veil | 3 | | Market | 8 | | Movement | 1 | | Water | 3 | | One | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Market" | | 8 | "Water" | | 9 | "One" |
| | places | | | globalScore | 0.697 | | windowScore | 0.833 | |
| 97.55% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 143 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like dried dragon scales" | | 1 | "looked like living stars" | | 2 | "appeared above her head" |
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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 | 2074 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 205 | | matches | (empty) | |
| 79.29% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 39.13 | | std | 16.73 | | cv | 0.428 | | sampleLengths | | 0 | 58 | | 1 | 74 | | 2 | 9 | | 3 | 77 | | 4 | 58 | | 5 | 58 | | 6 | 71 | | 7 | 37 | | 8 | 48 | | 9 | 49 | | 10 | 42 | | 11 | 7 | | 12 | 23 | | 13 | 18 | | 14 | 47 | | 15 | 64 | | 16 | 48 | | 17 | 50 | | 18 | 21 | | 19 | 52 | | 20 | 55 | | 21 | 40 | | 22 | 28 | | 23 | 11 | | 24 | 47 | | 25 | 55 | | 26 | 50 | | 27 | 18 | | 28 | 28 | | 29 | 45 | | 30 | 21 | | 31 | 39 | | 32 | 33 | | 33 | 7 | | 34 | 38 | | 35 | 44 | | 36 | 16 | | 37 | 29 | | 38 | 43 | | 39 | 41 | | 40 | 30 | | 41 | 42 | | 42 | 44 | | 43 | 36 | | 44 | 30 | | 45 | 43 | | 46 | 34 | | 47 | 17 | | 48 | 41 | | 49 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 326 | | matches | | 0 | "was building" | | 1 | "wasn't trying" | | 2 | "wasn't just running" | | 3 | "was only beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 205 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1612 | | adjectiveStacks | 1 | | stackExamples | | 0 | "revealing short curly dark hair" |
| | adverbCount | 53 | | adverbRatio | 0.03287841191066997 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.01054590570719603 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 205 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 205 | | mean | 10.12 | | std | 5.15 | | cv | 0.51 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 27 | | 3 | 16 | | 4 | 10 | | 5 | 11 | | 6 | 9 | | 7 | 3 | | 8 | 2 | | 9 | 17 | | 10 | 6 | | 11 | 9 | | 12 | 4 | | 13 | 5 | | 14 | 13 | | 15 | 19 | | 16 | 2 | | 17 | 3 | | 18 | 5 | | 19 | 2 | | 20 | 6 | | 21 | 18 | | 22 | 10 | | 23 | 15 | | 24 | 10 | | 25 | 11 | | 26 | 12 | | 27 | 15 | | 28 | 6 | | 29 | 10 | | 30 | 27 | | 31 | 6 | | 32 | 14 | | 33 | 8 | | 34 | 13 | | 35 | 4 | | 36 | 11 | | 37 | 15 | | 38 | 7 | | 39 | 16 | | 40 | 4 | | 41 | 10 | | 42 | 10 | | 43 | 5 | | 44 | 13 | | 45 | 13 | | 46 | 7 | | 47 | 3 | | 48 | 8 | | 49 | 15 |
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| 52.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.33658536585365856 | | totalSentences | 205 | | uniqueOpeners | 69 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 179 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 179 | | matches | | 0 | "She kept her eyes locked" | | 1 | "She gripped her radio, then" | | 2 | "she shouted, voice cutting through" | | 3 | "She closed the gap slightly" | | 4 | "Their eyes met across the" | | 5 | "His warm brown gaze held" | | 6 | "She hesitated, flashlight beam piercing" | | 7 | "Her partner's face flashed in" | | 8 | "She couldn't let another lead" | | 9 | "She ripped away the remaining" | | 10 | "Her flashlight beam danced across" | | 11 | "She chose the path with" | | 12 | "They straightened as she approached," | | 13 | "It moved every full moon," | | 14 | "She had neither, but momentum" | | 15 | "He weaved between stalls with" | | 16 | "she called, using the name" | | 17 | "He glanced back, scar on" | | 18 | "She forced her focus back" | | 19 | "She spun, hand going to" |
| | ratio | 0.218 | |
| 27.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 155 | | totalSentences | 179 | | matches | | 0 | "Detective Harlow Quinn sprinted through" | | 1 | "She kept her eyes locked" | | 2 | "The suspect moved with practiced" | | 3 | "Quinn's breath came steady despite" | | 4 | "She gripped her radio, then" | | 5 | "This wasn't official." | | 6 | "The supernatural angle whispered too" | | 7 | "she shouted, voice cutting through" | | 8 | "The figure didn't slow." | | 9 | "Quinn pushed harder, coat flapping" | | 10 | "The suspect's hood slipped back" | | 11 | "The off-the-books medic." | | 12 | "Quinn had seen his file." | | 13 | "Herrera ducked left at the" | | 14 | "Quinn followed, her worn leather" | | 15 | "Water streamed down her sharp" | | 16 | "The streets grew narrower, lined" | | 17 | "Music thumped from hidden clubs," | | 18 | "She closed the gap slightly" | | 19 | "Their eyes met across the" |
| | ratio | 0.866 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 179 | | matches | | 0 | "If anything, the pace quickened." | | 1 | "Now he patched up the" | | 2 | "Before they could respond, a" | | 3 | "Now it pulsed with impossible" | | 4 | "Even if it meant descending" | | 5 | "Even if the shadows remembered" |
| | ratio | 0.034 | |
| 91.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 83 | | technicalSentenceCount | 6 | | matches | | 0 | "The suspect moved with practiced grace, dodging puddles and late-night revelers who scattered like startled rats." | | 1 | "The supernatural angle whispered too close to the shadows that had swallowed DS Morris three years ago." | | 2 | "Quinn pushed harder, coat flapping open to reveal her badge glinting under streetlamps." | | 3 | "Morris, bleeding out in an alley not unlike this one, whispering about things that shouldn't exist." | | 4 | "Quinn kept her flashlight sweeping in wide arcs, catching glimpses of impossible items." | | 5 | "She plunged into the passage, shoulders brushing against walls covered in old maps that matched the ones in The Raven's Nest." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, voice cutting through the rain" |
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| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "the taller enforcer growled (growl)" |
| | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.667 | | effectiveRatio | 0.148 | |