| 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 | |
| 90.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1108 | | totalAiIsmAdverbs | 2 | | 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) | |
| 27.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1108 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "glistening" | | 1 | "could feel" | | 2 | "scanned" | | 3 | "racing" | | 4 | "echoed" | | 5 | "pounding" | | 6 | "flickered" | | 7 | "scanning" | | 8 | "echoing" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 3 |
|
| | highlights | | 0 | "heart pounded in her chest" | | 1 | "The air was thick with" | | 2 | "the air was thick with" |
| |
| 98.12% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 93 | | matches | | 0 | "t with determination" | | 1 | "e with surprise" | | 2 | "e with fear" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1108 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 1087 | | uniqueNames | 9 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" |
| | places | | | globalScore | 0.994 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1108 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 72.40% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 41.04 | | std | 16.55 | | cv | 0.403 | | sampleLengths | | 0 | 76 | | 1 | 70 | | 2 | 55 | | 3 | 53 | | 4 | 48 | | 5 | 47 | | 6 | 36 | | 7 | 40 | | 8 | 43 | | 9 | 44 | | 10 | 46 | | 11 | 44 | | 12 | 45 | | 13 | 71 | | 14 | 41 | | 15 | 10 | | 16 | 41 | | 17 | 11 | | 18 | 46 | | 19 | 37 | | 20 | 14 | | 21 | 38 | | 22 | 9 | | 23 | 40 | | 24 | 43 | | 25 | 28 | | 26 | 32 |
| |
| 86.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 93 | | matches | | 0 | "been tampered" | | 1 | "were jagged" | | 2 | "was covered" | | 3 | "were dressed" | | 4 | "was involved" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 200 | | matches | | 0 | "was holding" | | 1 | "was trading" | | 2 | "were speaking" | | 3 | "was racing" | | 4 | "were backing" | | 5 | "was struggling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 97 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1087 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.020239190432382703 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.00919963201471941 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 11.42 | | std | 4.79 | | cv | 0.419 | | sampleLengths | | 0 | 18 | | 1 | 14 | | 2 | 13 | | 3 | 31 | | 4 | 14 | | 5 | 5 | | 6 | 20 | | 7 | 8 | | 8 | 11 | | 9 | 12 | | 10 | 12 | | 11 | 3 | | 12 | 17 | | 13 | 10 | | 14 | 13 | | 15 | 13 | | 16 | 15 | | 17 | 18 | | 18 | 7 | | 19 | 6 | | 20 | 14 | | 21 | 8 | | 22 | 8 | | 23 | 6 | | 24 | 6 | | 25 | 15 | | 26 | 13 | | 27 | 19 | | 28 | 11 | | 29 | 12 | | 30 | 13 | | 31 | 14 | | 32 | 13 | | 33 | 13 | | 34 | 16 | | 35 | 14 | | 36 | 13 | | 37 | 11 | | 38 | 9 | | 39 | 24 | | 40 | 10 | | 41 | 18 | | 42 | 18 | | 43 | 8 | | 44 | 8 | | 45 | 20 | | 46 | 8 | | 47 | 17 | | 48 | 14 | | 49 | 14 |
| |
| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 25 | | diversityRatio | 0.23711340206185566 | | totalSentences | 97 | | uniqueOpeners | 23 | |
| 35.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 93 | | matches | | 0 | "Then, with a sudden movement," |
| | ratio | 0.011 | |
| 13.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 93 | | matches | | 0 | "She had been tailing him" | | 1 | "Her leather watch, worn from" | | 2 | "She could feel the adrenaline" | | 3 | "She plunged into the alley," | | 4 | "She scanned the area, her" | | 5 | "She crouched down, examining the" | | 6 | "She hesitated for a moment," | | 7 | "She couldn't let him get" | | 8 | "She moved cautiously, her eyes" | | 9 | "She could see footprints in" | | 10 | "She followed the footprints up" | | 11 | "She could hear voices echoing" | | 12 | "She moved cautiously, her hand" | | 13 | "He was holding something in" | | 14 | "She ducked behind a pillar," | | 15 | "She had never seen anything" | | 16 | "She watched as the suspect" | | 17 | "He was trading it for" | | 18 | "She had to make a" | | 19 | "She could turn back, forget" |
| | ratio | 0.516 | |
| 29.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 93 | | matches | | 0 | "The rain hammered down on" | | 1 | "Detective Harlow Quinn's breath came" | | 2 | "The suspect, a shadowy figure" | | 3 | "She had been tailing him" | | 4 | "Her leather watch, worn from" | | 5 | "The time read 11:47 PM." | | 6 | "The streets were slick, the" | | 7 | "The suspect was fast, but" | | 8 | "She could feel the adrenaline" | | 9 | "The suspect ducked into an" | | 10 | "Quinn didn't hesitate." | | 11 | "She plunged into the alley," | | 12 | "The alley was narrow, the" | | 13 | "The air was thick with" | | 14 | "Quinn slowed her pace, her" | | 15 | "She scanned the area, her" | | 16 | "The sewer entrance had been" | | 17 | "She crouched down, examining the" | | 18 | "The edges were jagged, as" | | 19 | "She hesitated for a moment," |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 6 | | matches | | 0 | "She could feel the adrenaline coursing through her veins, sharpening her senses." | | 1 | "She plunged into the alley, her boots splashing through puddles that had formed in the uneven cobblestones." | | 2 | "She scanned the area, her eyes narrowing as she spotted a rusted metal grate lying on the ground." | | 3 | "He was holding something in his hand, a small bone token that glowed faintly in the dim light." | | 4 | "The Veil Market, she had heard whispers of it, a hidden supernatural black market that moved locations every full moon." | | 5 | "She could hear the people at the table scrambling, their chairs scraping against the floor." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 4 | | matches | | 0 | "she said, her voice echoing through the station" | | 1 | "she said, her voice steady" | | 2 | "she said, her voice firm" | | 3 | "she said, her voice low" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |