| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 875 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 37.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 875 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "scanned" | | 1 | "flicker" | | 2 | "gloom" | | 3 | "pulse" | | 4 | "footsteps" | | 5 | "flickered" | | 6 | "gleaming" | | 7 | "predator" | | 8 | "echoed" | | 9 | "etched" |
| |
| 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 | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 868 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.49% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 792 | | uniqueNames | 11 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | | globalScore | 0.995 | | windowScore | 1 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like they’d been carved from the e" | | 1 | "appeared beside her, his voice low" |
| |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 3.456 | | wordCount | 868 | | matches | | 0 | "No sound but" | | 1 | "not with the usual city noise, but with a low, almost imperceptible vibration, like the pulse o" | | 2 | "Not the slap of wet shoes on pavement, but the soft scuff of boots on stone" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 31 | | std | 19.84 | | cv | 0.64 | | sampleLengths | | 0 | 66 | | 1 | 46 | | 2 | 51 | | 3 | 70 | | 4 | 46 | | 5 | 30 | | 6 | 65 | | 7 | 26 | | 8 | 63 | | 9 | 17 | | 10 | 44 | | 11 | 5 | | 12 | 18 | | 13 | 31 | | 14 | 11 | | 15 | 5 | | 16 | 3 | | 17 | 19 | | 18 | 37 | | 19 | 51 | | 20 | 13 | | 21 | 15 | | 22 | 35 | | 23 | 26 | | 24 | 10 | | 25 | 29 | | 26 | 27 | | 27 | 9 |
| |
| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 76 | | matches | | 0 | "been carved" | | 1 | "was gone" | | 2 | "was gone" | | 3 | "was gone" |
| |
| 92.47% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 124 | | matches | | 0 | "weren’t looking" | | 1 | "were looking" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 86 | | ratio | 0.058 | | matches | | 0 | "The suspect—tall, lanky, moving with the kind of urgency that screamed guilt—ducked around a corner." | | 1 | "The steps were steep, the air thick with the scent of damp earth and something else—something metallic, like old coins or blood." | | 2 | "Lanterns flickered, casting long shadows over tables laden with jars of murky liquids, bundles of dried herbs, and things she couldn’t—didn’t want to—identify." | | 3 | "The market’s patrons turned to stare—faces half-hidden under hoods, eyes gleaming in the dimness." | | 4 | "The market’s entrance—the staircase she’d come down—was gone." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 803 | | adjectiveStacks | 1 | | stackExamples | | 0 | "suspect—tall, lanky, moving" |
| | adverbCount | 18 | | adverbRatio | 0.0224159402241594 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 10.09 | | std | 6.14 | | cv | 0.608 | | sampleLengths | | 0 | 14 | | 1 | 17 | | 2 | 13 | | 3 | 22 | | 4 | 3 | | 5 | 22 | | 6 | 15 | | 7 | 6 | | 8 | 15 | | 9 | 12 | | 10 | 20 | | 11 | 4 | | 12 | 13 | | 13 | 12 | | 14 | 2 | | 15 | 8 | | 16 | 13 | | 17 | 22 | | 18 | 7 | | 19 | 10 | | 20 | 29 | | 21 | 5 | | 22 | 16 | | 23 | 5 | | 24 | 4 | | 25 | 22 | | 26 | 23 | | 27 | 3 | | 28 | 13 | | 29 | 4 | | 30 | 8 | | 31 | 4 | | 32 | 14 | | 33 | 13 | | 34 | 14 | | 35 | 16 | | 36 | 20 | | 37 | 9 | | 38 | 4 | | 39 | 4 | | 40 | 7 | | 41 | 14 | | 42 | 12 | | 43 | 2 | | 44 | 9 | | 45 | 5 | | 46 | 5 | | 47 | 13 | | 48 | 10 | | 49 | 21 |
| |
| 40.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.29069767441860467 | | totalSentences | 86 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 73 | | matches | | 0 | "Then she saw it: a" | | 1 | "Then she heard it: footsteps." | | 2 | "Instead, he nodded towards the" |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 73 | | matches | | 0 | "She didn’t hesitate." | | 1 | "She followed, her sharp jaw" | | 2 | "It gave way with a" | | 3 | "She pulled her torch from" | | 4 | "She descended, one hand trailing" | | 5 | "She’d heard whispers of it" | | 6 | "She lost sight of him" | | 7 | "She ignored them, her gaze" | | 8 | "They swayed, then stilled." | | 9 | "She spun, her fist already" | | 10 | "His Saint Christopher medallion glinted" | | 11 | "His lips twitched, but there" | | 12 | "She glanced past him, towards" | | 13 | "She shoved past Tomás, her" | | 14 | "She picked up the token," | | 15 | "He didn’t answer." | | 16 | "They weren’t looking at her." | | 17 | "They were looking past her," | | 18 | "It smiled, and the lanterns" | | 19 | "It was a test." |
| | ratio | 0.274 | |
| 49.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 73 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The salt-and-pepper stubble on her" | | 3 | "A shadow darted between the" | | 4 | "She didn’t hesitate." | | 5 | "Quinn pushed off the damp" | | 6 | "The suspect—tall, lanky, moving with" | | 7 | "She followed, her sharp jaw" | | 8 | "The alley narrowed, the stench" | | 9 | "A fire escape groaned above" | | 10 | "The door swung shut." | | 11 | "Quinn reached it in three" | | 12 | "It gave way with a" | | 13 | "She pulled her torch from" | | 14 | "The steps were steep, the" | | 15 | "She descended, one hand trailing" | | 16 | "The torchlight caught glimpses of" | | 17 | "The deeper she went, the" | | 18 | "The suspect was down here." | | 19 | "The staircase opened into a" |
| | ratio | 0.822 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "She lost sight of him behind a curtain of beads that clattered like bones." | | 1 | "She spun, her fist already raised, but the man who stood there didn’t flinch." | | 2 | "And then she saw it: a bone token on the floor, its surface etched with symbols that matched the graffiti on the stairs." | | 3 | "They were looking past her, their expressions shifting from curiosity to something darker." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |