| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 22 | | tagDensity | 0.318 | | leniency | 0.636 | | rawRatio | 0.143 | | effectiveRatio | 0.091 | |
| 95.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1089 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 81.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1089 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "scanning" | | 1 | "etched" | | 2 | "perfect" |
| |
| 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 | 70 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1064 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 716 | | uniqueNames | 12 | | maxNameDensity | 1.68 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 12 | | Chen | 7 | | Camden | 1 | | Veil | 2 | | Market | 1 | | Morris | 2 | | Shade | 1 | | Compass | 1 | | Eva | 2 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Chen" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Compass" | | 6 | "Eva" | | 7 | "Kowalski" |
| | places | (empty) | | globalScore | 0.662 | | windowScore | 0.667 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 7 | | per1kWords | 6.579 | | wordCount | 1064 | | matches | | 0 | "not for graffiti or rats, but for absence" | | 1 | "not from files, but from whispers she had chased since her partner DS Morris had" | | 2 | "not with decay, but with something like ink" | | 3 | "not the horror, but the geometry of the expression" | | 4 | "not from violence but from comprehension" | | 5 | "not smoke damage but burn patterns" | | 6 | "not near the body, but hidden behind a loose tile, needle quivering toward the tunn" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 28.76 | | std | 21.69 | | cv | 0.754 | | sampleLengths | | 0 | 23 | | 1 | 65 | | 2 | 27 | | 3 | 22 | | 4 | 8 | | 5 | 76 | | 6 | 13 | | 7 | 47 | | 8 | 5 | | 9 | 25 | | 10 | 43 | | 11 | 29 | | 12 | 8 | | 13 | 4 | | 14 | 38 | | 15 | 7 | | 16 | 32 | | 17 | 68 | | 18 | 5 | | 19 | 50 | | 20 | 17 | | 21 | 7 | | 22 | 16 | | 23 | 43 | | 24 | 27 | | 25 | 17 | | 26 | 33 | | 27 | 16 | | 28 | 59 | | 29 | 3 | | 30 | 48 | | 31 | 13 | | 32 | 26 | | 33 | 11 | | 34 | 20 | | 35 | 22 | | 36 | 91 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 70 | | matches | | |
| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 126 | | matches | | 0 | "were scanning" | | 1 | "was screaming" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 3 | | flaggedSentences | 13 | | totalSentences | 85 | | ratio | 0.153 | | matches | | 0 | "The abandoned Tube station smelled of rust, wet brick, and something older—ozone and copper, the scent of a struck match held to bone." | | 1 | "Military precision governed her posture, but her brown eyes—tired, relentless—were scanning not for graffiti or rats, but for absence." | | 2 | "DS Chen—young, ambitious, sweat beading at his collar—approached with a clipboard." | | 3 | "The entry required a bone token; the schedule moved every full moon." | | 4 | "Quinn knew these details not from files, but from whispers she had chased since her partner DS Morris had fallen three years ago—under unexplained circumstances with supernatural origins she still refused to fully name." | | 5 | "The victim’s mouth gaped, but the tongue was black—not with decay, but with something like ink." | | 6 | "She studied the face—not the horror, but the geometry of the expression." | | 7 | "No footprints save the runner’s and the uniform boots—too neat, stamped in ritual patterns." | | 8 | "She looked for what others missed: the absence of dust on the victim’s shoes, meaning he had not walked far to reach this spot; the faint scorch marks on the ceiling tiles in a perfect circle, not smoke damage but burn patterns; the smell—ozone and copper." | | 9 | "The Veil Compass—attuned to supernatural energy, pointing to the nearest rift or portal." | | 10 | "She glanced at her watch—worn leather, left wrist—then at the victim’s hand, where the token was clutched." | | 11 | "Not from fire—there was no ash, no smell of burnt material." | | 12 | "Behind her, Eva Kowalski’s description came unbidden to mind—worn leather satchel, round glasses, curly red hair, green eyes—an occult researcher who might have understood this language of absence and sigil." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 738 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03116531165311653 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009485094850948509 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 12.52 | | std | 10.65 | | cv | 0.851 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 1 | | 3 | 4 | | 4 | 21 | | 5 | 19 | | 6 | 16 | | 7 | 11 | | 8 | 22 | | 9 | 6 | | 10 | 2 | | 11 | 18 | | 12 | 12 | | 13 | 12 | | 14 | 34 | | 15 | 7 | | 16 | 2 | | 17 | 2 | | 18 | 2 | | 19 | 3 | | 20 | 5 | | 21 | 16 | | 22 | 23 | | 23 | 5 | | 24 | 4 | | 25 | 21 | | 26 | 2 | | 27 | 7 | | 28 | 12 | | 29 | 2 | | 30 | 3 | | 31 | 17 | | 32 | 26 | | 33 | 2 | | 34 | 1 | | 35 | 8 | | 36 | 2 | | 37 | 2 | | 38 | 27 | | 39 | 11 | | 40 | 7 | | 41 | 32 | | 42 | 8 | | 43 | 14 | | 44 | 46 | | 45 | 5 | | 46 | 10 | | 47 | 9 | | 48 | 18 | | 49 | 13 |
| |
| 65.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.42857142857142855 | | totalSentences | 84 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 58 | | matches | | 0 | "Then she found the compass." | | 1 | "Only Quinn, the brass compass" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 58 | | matches | | 0 | "She had not slept." | | 1 | "Her sharp jaw was set," | | 2 | "They walked past the tiled" | | 3 | "His fingers, splayed in the" | | 4 | "She studied the face—not the" | | 5 | "She noticed the token first:" | | 6 | "She stood, brushing grit from" | | 7 | "She walked the perimeter, torch" | | 8 | "She looked for what others" | | 9 | "It lay not near the" | | 10 | "she said quietly" | | 11 | "She glanced at her watch—worn" | | 12 | "She walked to the curved" | | 13 | "She faced Chen now, brown" | | 14 | "She pocketed the compass." | | 15 | "She straightened, sharp jaw lifted" | | 16 | "She turned away from the" |
| | ratio | 0.293 | |
| 72.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 58 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Detective Harlow Quinn stood at" | | 2 | "She had not slept." | | 3 | "Her sharp jaw was set," | | 4 | "Military precision governed her posture," | | 5 | "DS Chen—young, ambitious, sweat beading" | | 6 | "Quinn did not take the" | | 7 | "They walked past the tiled" | | 8 | "The Veil Market’s territory, though" | | 9 | "The entry required a bone" | | 10 | "Quinn knew these details not" | | 11 | "The body lay against the" | | 12 | "The victim’s mouth gaped, but" | | 13 | "His fingers, splayed in the" | | 14 | "Chen flipped a page." | | 15 | "The concrete was cold against" | | 16 | "She studied the face—not the" | | 17 | "She noticed the token first:" | | 18 | "She stood, brushing grit from" | | 19 | "She walked the perimeter, torch" |
| | ratio | 0.776 | |
| 86.21% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 58 | | matches | | | ratio | 0.017 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "Behind her, Eva Kowalski’s description came unbidden to mind—worn leather satchel, round glasses, curly red hair, green eyes—an occult researcher who might have…" | | 1 | "Only Quinn, the brass compass in her pocket warming like a second heartbeat, and the certainty that the evidence others missed was screaming in every silent det…" |
| |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, not turning," |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0 | | effectiveRatio | 0 | |