| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said quietly [quietly]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 47 | | tagDensity | 0.191 | | leniency | 0.383 | | rawRatio | 0.222 | | effectiveRatio | 0.085 | |
| 90.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1647 | | totalAiIsmAdverbs | 3 | | 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) | |
| 72.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1647 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "stomach" | | 3 | "tracing" | | 4 | "etched" | | 5 | "familiar" | | 6 | "ensure" |
| |
| 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 | 87 | | matches | (empty) | |
| 77.18% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "tried to" | | 2 | "tended to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 25 | | totalWords | 1640 | | ratio | 0.015 | | matches | | 0 | "You found nothing because he was taken deeper." | | 1 | "Who is this?" | | 2 | "Come to the Museum Tavern. Sit at the back booth. Bring a bone token." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 962 | | uniqueNames | 15 | | maxNameDensity | 1.98 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Smithfield | 2 | | Market | 1 | | Harlow | 1 | | Quinn | 19 | | November | 1 | | London | 2 | | Clapham | 1 | | Morris | 2 | | Museum | 4 | | Tavern | 2 | | British | 1 | | Street | 1 | | Kowalski | 2 | | Latin | 1 | | Eva | 16 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | | 0 | "Smithfield" | | 1 | "November" | | 2 | "London" | | 3 | "Museum" | | 4 | "Tavern" | | 5 | "British" | | 6 | "Street" |
| | globalScore | 0.512 | | windowScore | 0.5 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like it belonged to someone else" | | 1 | "looked like someone who'd seen something" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1640 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 125 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 24.48 | | std | 16.6 | | cv | 0.678 | | sampleLengths | | 0 | 16 | | 1 | 40 | | 2 | 4 | | 3 | 18 | | 4 | 39 | | 5 | 4 | | 6 | 13 | | 7 | 48 | | 8 | 32 | | 9 | 8 | | 10 | 2 | | 11 | 1 | | 12 | 47 | | 13 | 7 | | 14 | 3 | | 15 | 43 | | 16 | 12 | | 17 | 15 | | 18 | 46 | | 19 | 6 | | 20 | 6 | | 21 | 18 | | 22 | 5 | | 23 | 1 | | 24 | 41 | | 25 | 40 | | 26 | 46 | | 27 | 20 | | 28 | 4 | | 29 | 2 | | 30 | 42 | | 31 | 24 | | 32 | 20 | | 33 | 29 | | 34 | 28 | | 35 | 41 | | 36 | 47 | | 37 | 5 | | 38 | 35 | | 39 | 26 | | 40 | 36 | | 41 | 51 | | 42 | 2 | | 43 | 37 | | 44 | 18 | | 45 | 52 | | 46 | 5 | | 47 | 34 | | 48 | 21 | | 49 | 6 |
| |
| 81.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 87 | | matches | | 0 | "were drawn" | | 1 | "was taken" | | 2 | "been sealed" | | 3 | "been open" | | 4 | "was scarred" | | 5 | "was etched" |
| |
| 78.05% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 164 | | matches | | 0 | "was starting" | | 1 | "were escaping" | | 2 | "was beginning" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 125 | | ratio | 0.008 | | matches | | 0 | "The city never went quiet—not really—but here, in this pocket between commerce and neglect, the noise of London felt distant, muffled, like hearing a party through a brick wall." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 967 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02481902792140641 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0062047569803516025 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 13.11 | | std | 10.3 | | cv | 0.786 | | sampleLengths | | 0 | 16 | | 1 | 17 | | 2 | 23 | | 3 | 4 | | 4 | 18 | | 5 | 5 | | 6 | 18 | | 7 | 16 | | 8 | 4 | | 9 | 6 | | 10 | 7 | | 11 | 6 | | 12 | 13 | | 13 | 29 | | 14 | 14 | | 15 | 1 | | 16 | 17 | | 17 | 8 | | 18 | 2 | | 19 | 1 | | 20 | 2 | | 21 | 21 | | 22 | 24 | | 23 | 7 | | 24 | 3 | | 25 | 11 | | 26 | 2 | | 27 | 18 | | 28 | 12 | | 29 | 12 | | 30 | 15 | | 31 | 11 | | 32 | 2 | | 33 | 5 | | 34 | 4 | | 35 | 20 | | 36 | 4 | | 37 | 2 | | 38 | 2 | | 39 | 2 | | 40 | 6 | | 41 | 9 | | 42 | 5 | | 43 | 4 | | 44 | 5 | | 45 | 20 | | 46 | 12 | | 47 | 9 | | 48 | 8 | | 49 | 6 |
| |
| 49.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.336 | | totalSentences | 125 | | uniqueOpeners | 42 | |
| 42.19% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 79 | | matches | | 0 | "Then the number went dead." |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 79 | | matches | | 0 | "Her shoes clicked against wet" | | 1 | "She stopped at the spot" | | 2 | "She was taller than him" | | 3 | "She walked past him, her" | | 4 | "She'd been awake for nineteen" | | 5 | "Her flat in Clapham called" | | 6 | "Her phone buzzed against her" | | 7 | "It wasn't random tagging." | | 8 | "She'd seen that symbol before," | | 9 | "She typed back: *Who is" | | 10 | "She pushed through the door" | | 11 | "Her red curls were escaping" | | 12 | "Her fingers lingered on its" | | 13 | "She'd heard this kind of" | | 14 | "She looked like someone who'd" | | 15 | "She set it on the" | | 16 | "She looked at the compass," | | 17 | "Her hand went to her" | | 18 | "Her knees ached from the" | | 19 | "She'd been chasing ghosts for" |
| | ratio | 0.278 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 79 | | matches | | 0 | "The alley behind Smithfield Market" | | 1 | "Detective Harlow Quinn stood at" | | 2 | "The uniformed officer beside her" | | 3 | "Quinn's gaze swept the cobblestones." | | 4 | "A single arc light cast" | | 5 | "The metal shutters of the" | | 6 | "The constable's mouth opened and" | | 7 | "Quinn stepped into the alley" | | 8 | "Her shoes clicked against wet" | | 9 | "The city never went quiet—not" | | 10 | "She stopped at the spot" | | 11 | "Graffiti scarred the wall: a" | | 12 | "the constable offered" | | 13 | "She was taller than him" | | 14 | "She walked past him, her" | | 15 | "She'd been awake for nineteen" | | 16 | "Her flat in Clapham called" | | 17 | "Her phone buzzed against her" | | 18 | "The message came from an" | | 19 | "Quinn read the message twice," |
| | ratio | 0.937 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 38.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 6 | | matches | | 0 | "The uniformed officer beside her kept shifting his weight from foot to foot, his radio crackling with static that nobody bothered to answer." | | 1 | "A single arc light cast everything in jaundiced yellow, cutting shadows that seemed too long for the hour." | | 2 | "Her red curls were escaping a messy bun, and her freckled face held the pallor of someone who hadn't slept in days." | | 3 | "She'd heard this kind of talk before, from the occult nuts who hung around the fringes of cold cases, spinning fairy tales to explain the unexplainable." | | 4 | "She looked like someone who'd seen something she couldn't unsee." | | 5 | "Her hand went to her stomach, pressing against a knot that had formed beneath her ribs." |
| |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, her breath misting" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.085 | | leniency | 0.17 | | rawRatio | 0 | | effectiveRatio | 0 | |