| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1230 | | totalAiIsmAdverbs | 3 | | 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) | |
| 18.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1230 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "perfect" | | 1 | "determined" | | 2 | "pulsed" | | 3 | "unreadable" | | 4 | "charm" | | 5 | "flickered" | | 6 | "standard" | | 7 | "silence" | | 8 | "flicked" | | 9 | "gloom" | | 10 | "oppressive" | | 11 | "rhythmic" | | 12 | "resonated" | | 13 | "shimmered" | | 14 | "glint" | | 15 | "resolve" | | 16 | "echoing" |
| |
| 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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | 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 | 2 | | markdownWords | 2 | | totalWords | 1225 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1180 | | uniqueNames | 17 | | maxNameDensity | 0.68 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Harlow | 7 | | West | 1 | | End | 1 | | Herrera | 8 | | Silas | 5 | | Soho | 1 | | Square | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Brewer | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | London | 2 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Herrera" | | 2 | "Silas" | | 3 | "Raven" | | 4 | "Market" |
| | places | | 0 | "West" | | 1 | "End" | | 2 | "Soho" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Brewer" | | 7 | "Street" | | 8 | "Nest" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like a slap" | | 1 | "nce, towards a seemingly blank section of th" | | 2 | "seemed plucked from another era" |
| |
| 36.73% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.633 | | wordCount | 1225 | | matches | | 0 | "not yet, but tonight felt different" | | 1 | "not exactly, but moving with a determined, almost urgent stride" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 94.76% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 40.83 | | std | 19.67 | | cv | 0.482 | | sampleLengths | | 0 | 63 | | 1 | 102 | | 2 | 63 | | 3 | 59 | | 4 | 69 | | 5 | 49 | | 6 | 42 | | 7 | 34 | | 8 | 8 | | 9 | 17 | | 10 | 12 | | 11 | 16 | | 12 | 16 | | 13 | 32 | | 14 | 26 | | 15 | 39 | | 16 | 44 | | 17 | 36 | | 18 | 41 | | 19 | 31 | | 20 | 29 | | 21 | 57 | | 22 | 62 | | 23 | 42 | | 24 | 50 | | 25 | 23 | | 26 | 52 | | 27 | 30 | | 28 | 42 | | 29 | 39 |
| |
| 92.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 111 | | matches | | 0 | "were crammed" | | 1 | "were dressed" | | 2 | "was hidden" | | 3 | "was involved" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 195 | | matches | | 0 | "wasn’t running" | | 1 | "was wiping" | | 2 | "was hitting" | | 3 | "wasn’t responding" | | 4 | "was examining" | | 5 | "was handing" | | 6 | "was buying" |
| |
| 93.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 116 | | ratio | 0.017 | | matches | | 0 | "He paused, glanced over his shoulder—not at her, thankfully—then walked straight *past* the entrance, towards a seemingly blank section of the wall beside it." | | 1 | "This wasn't just unusual; it was impossible." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1185 | | adjectiveStacks | 1 | | stackExamples | | 0 | "narrow, stone-lined corridor," |
| | adverbCount | 42 | | adverbRatio | 0.035443037974683546 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.015189873417721518 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 10.56 | | std | 6.51 | | cv | 0.617 | | sampleLengths | | 0 | 13 | | 1 | 10 | | 2 | 26 | | 3 | 14 | | 4 | 18 | | 5 | 23 | | 6 | 14 | | 7 | 16 | | 8 | 31 | | 9 | 13 | | 10 | 13 | | 11 | 21 | | 12 | 7 | | 13 | 1 | | 14 | 1 | | 15 | 7 | | 16 | 8 | | 17 | 13 | | 18 | 26 | | 19 | 9 | | 20 | 3 | | 21 | 21 | | 22 | 9 | | 23 | 16 | | 24 | 3 | | 25 | 5 | | 26 | 6 | | 27 | 9 | | 28 | 4 | | 29 | 24 | | 30 | 19 | | 31 | 2 | | 32 | 3 | | 33 | 9 | | 34 | 24 | | 35 | 3 | | 36 | 3 | | 37 | 13 | | 38 | 11 | | 39 | 10 | | 40 | 8 | | 41 | 9 | | 42 | 8 | | 43 | 9 | | 44 | 3 | | 45 | 8 | | 46 | 8 | | 47 | 11 | | 48 | 5 | | 49 | 10 |
| |
| 37.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3017241379310345 | | totalSentences | 116 | | uniqueOpeners | 35 | |
| 63.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 105 | | matches | | 0 | "Then he stopped, abruptly, in" | | 1 | "Then, she heard voices." |
| | ratio | 0.019 | |
| 33.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 105 | | matches | | 0 | "She angled her jaw, a" | | 1 | "He moved with a deceptive" | | 2 | "She’d been tailing him for" | | 3 | "He was the connector, the" | | 4 | "She hadn’t been able to" | | 5 | "He’d met with a known" | | 6 | "He cut sharply left, disappearing" | | 7 | "Her boots splashed through puddles" | | 8 | "She risked a glance at" | | 9 | "He turned again, and she" | | 10 | "He wasn’t running, not exactly," | | 11 | "He crossed Charing Cross Road" | | 12 | "It was a known haunt" | | 13 | "He paused, glanced over his" | | 14 | "He pressed a hand against" | | 15 | "She’d pulled every favor she" | | 16 | "She waited, letting a full" | | 17 | "He didn’t acknowledge her presence," | | 18 | "she said, her voice crisp" | | 19 | "He finally looked up, his" |
| | ratio | 0.467 | |
| 7.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 105 | | matches | | 0 | "Rain lashed at Harlow’s face," | | 1 | "Each gust off the West" | | 2 | "She angled her jaw, a" | | 3 | "He moved with a deceptive" | | 4 | "She’d been tailing him for" | | 5 | "The whispers around Silas, the" | | 6 | "He was the connector, the" | | 7 | "She hadn’t been able to" | | 8 | "He’d met with a known" | | 9 | "He cut sharply left, disappearing" | | 10 | "Harlow followed, the worn leather" | | 11 | "Her boots splashed through puddles" | | 12 | "She risked a glance at" | | 13 | "Herrera was good, or he" | | 14 | "He turned again, and she" | | 15 | "He wasn’t running, not exactly," | | 16 | "He crossed Charing Cross Road" | | 17 | "The Raven’s Nest." | | 18 | "A distinctive green neon sign" | | 19 | "Harlow slowed, taking a moment" |
| | ratio | 0.905 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 4 | | matches | | 0 | "She followed the corridor downwards, her flashlight revealing a series of turning passages." | | 1 | "Murmured conversations, the clinking of glass, and a low, rhythmic hum that resonated in her chest." | | 2 | "A woman with eyes that shimmered with an unnatural gold was examining a collection of antique daggers." | | 3 | "She moved forward, her flashlight beam cutting through the shadowy cavern, her boots echoing on the stone floor." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, her voice crisp and authoritative" | | 1 | "she said, her tone carefully neutral" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |