| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 29 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn nodded curtly [curtly]" | | 1 | "Eva corrected automatically [automatically]" |
| | dialogueSentences | 59 | | tagDensity | 0.492 | | leniency | 0.983 | | rawRatio | 0.069 | | effectiveRatio | 0.068 | |
| 69.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1454 | | totalAiIsmAdverbs | 9 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 2 |
| | 2 | | | 3 | | adverb | "reluctantly" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "very" | | 1 | "deliberately" | | 2 | "really" | | 3 | "reluctantly" | | 4 | "carefully" | | 5 | "slowly" |
| |
| 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) | |
| 72.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1454 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "intricate" | | 1 | "standard" | | 2 | "etched" | | 3 | "familiar" | | 4 | "whisper" | | 5 | "processed" | | 6 | "newfound" | | 7 | "chaotic" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 75 | | matches | (empty) | |
| 66.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "appeared to" | | 2 | "tended to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1438 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 815 | | uniqueNames | 10 | | maxNameDensity | 3.31 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 27 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Kowalski | 2 | | Eva | 20 | | Reynolds | 9 | | Morris | 4 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Reynolds" | | 6 | "Morris" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.695 | | wordCount | 1438 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 26.15 | | std | 14.07 | | cv | 0.538 | | sampleLengths | | 0 | 64 | | 1 | 15 | | 2 | 9 | | 3 | 37 | | 4 | 64 | | 5 | 34 | | 6 | 42 | | 7 | 42 | | 8 | 19 | | 9 | 28 | | 10 | 20 | | 11 | 35 | | 12 | 35 | | 13 | 30 | | 14 | 45 | | 15 | 9 | | 16 | 27 | | 17 | 34 | | 18 | 35 | | 19 | 12 | | 20 | 31 | | 21 | 26 | | 22 | 39 | | 23 | 5 | | 24 | 28 | | 25 | 10 | | 26 | 5 | | 27 | 37 | | 28 | 28 | | 29 | 9 | | 30 | 2 | | 31 | 21 | | 32 | 4 | | 33 | 8 | | 34 | 45 | | 35 | 33 | | 36 | 27 | | 37 | 13 | | 38 | 22 | | 39 | 15 | | 40 | 20 | | 41 | 10 | | 42 | 38 | | 43 | 18 | | 44 | 21 | | 45 | 17 | | 46 | 48 | | 47 | 21 | | 48 | 8 | | 49 | 36 |
| |
| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 75 | | matches | | 0 | "been transformed" | | 1 | "being slashed" | | 2 | "been stenciled" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 105 | | ratio | 0.067 | | matches | | 0 | "The air grew thicker with each step, carrying the unmistakable scent of copper and something else—something acrid that caught in the back of her throat." | | 1 | "But what captured Quinn's attention wasn't the body or even the ritualistic staging—it was the absence of blood spatter." | | 2 | "\"Then why is there no blood anywhere but directly under him? A cut like that—\" she gestured to the victim's throat, \"—would have sprayed everywhere. And look at the chalk. Not a single smudge or footprint.\"" | | 3 | "Quinn felt a familiar prickle at the back of her neck—the same sensation she'd experienced during her last case with Morris before his disappearance." | | 4 | "The candles hadn't burned naturally—their wax had been artificially dripped." | | 5 | "Quinn remembered fragments of Morris's rambling in the weeks before he vanished—tales of impossible things sold in shadow markets beneath London's streets." | | 6 | "\"I'm interested in justice, Ms. Kowalski. For this man—\" she gestured to the body, \"—and for DS Morris. If this market holds answers, that's where I need to go.\"" |
| |
| 95.04% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 791 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.04424778761061947 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.021491782553729456 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 13.7 | | std | 7.55 | | cv | 0.552 | | sampleLengths | | 0 | 19 | | 1 | 14 | | 2 | 13 | | 3 | 18 | | 4 | 15 | | 5 | 9 | | 6 | 12 | | 7 | 25 | | 8 | 7 | | 9 | 19 | | 10 | 25 | | 11 | 13 | | 12 | 16 | | 13 | 6 | | 14 | 12 | | 15 | 19 | | 16 | 23 | | 17 | 20 | | 18 | 22 | | 19 | 5 | | 20 | 14 | | 21 | 12 | | 22 | 16 | | 23 | 9 | | 24 | 11 | | 25 | 19 | | 26 | 16 | | 27 | 18 | | 28 | 17 | | 29 | 10 | | 30 | 20 | | 31 | 9 | | 32 | 36 | | 33 | 9 | | 34 | 13 | | 35 | 14 | | 36 | 4 | | 37 | 28 | | 38 | 2 | | 39 | 14 | | 40 | 21 | | 41 | 12 | | 42 | 6 | | 43 | 25 | | 44 | 24 | | 45 | 2 | | 46 | 14 | | 47 | 15 | | 48 | 10 | | 49 | 5 |
| |
| 88.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5523809523809524 | | totalSentences | 105 | | uniqueOpeners | 58 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 66 | | matches | | 0 | "Her worn leather shoes clicked" | | 1 | "His arms were extended, palms" | | 2 | "His eyes were open, fixed" | | 3 | "She tucked a strand of" | | 4 | "she gestured to the victim's" | | 5 | "She reached into her satchel" | | 6 | "She pointed to the victim's" | | 7 | "She stood, mentally reconstructing the" | | 8 | "She turned to Eva" | | 9 | "She'd dismissed it as stress-induced" | | 10 | "She turned to Reynolds." | | 11 | "she gestured to the body," |
| | ratio | 0.182 | |
| 20.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 66 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "Her worn leather shoes clicked" | | 2 | "Water dripped somewhere in the" | | 3 | "The Metropolitan Police had set" | | 4 | "Quinn asked the uniformed officer" | | 5 | "Quinn followed the trail of" | | 6 | "The air grew thicker with" | | 7 | "The crime scene opened up" | | 8 | "The station platform had been" | | 9 | "Chalk symbols covered the concrete" | | 10 | "Candle stubs formed another circle" | | 11 | "His arms were extended, palms" | | 12 | "His eyes were open, fixed" | | 13 | "Eva Kowalski stood near the" | | 14 | "She tucked a strand of" | | 15 | "Quinn nodded curtly" | | 16 | "Eva clutched her worn leather" | | 17 | "Quinn circled the scene, careful" | | 18 | "Eva knelt beside the chalk" | | 19 | "DS Reynolds, Quinn's current partner," |
| | ratio | 0.879 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | 0 | "Now she wasn't so certain." |
| | ratio | 0.015 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn ducked under the police tape that cordoned off what remained of the abandoned Tube station entrance." | | 1 | "Chalk symbols covered the concrete floor in concentric circles, intricate patterns that made her eyes hurt if she tried to follow them for too long." | | 2 | "DS Reynolds, Quinn's current partner, approached from where he'd been interviewing the urban explorers who'd discovered the body." | | 3 | "Eva guided Quinn around the scene, pointing out discrepancies that had escaped initial observation." | | 4 | "As Reynolds reluctantly departed, Quinn turned back to Eva, who was carefully photographing the symbols with her phone." |
| |
| 90.52% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn asked, her patience wearing thin" | | 1 | "Quinn's voice remained, but her hands clenched involuntarily" |
| |
| 65.25% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 5 | | fancyTags | | 0 | "Reynolds suggested (suggest)" | | 1 | "Quinn challenged (challenge)" | | 2 | "Eva corrected automatically (correct)" | | 3 | "Eva suggested (suggest)" | | 4 | "she murmured (murmur)" |
| | dialogueSentences | 59 | | tagDensity | 0.186 | | leniency | 0.373 | | rawRatio | 0.455 | | effectiveRatio | 0.169 | |