| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "she asked suddenly [suddenly]" |
| | dialogueSentences | 25 | | tagDensity | 0.64 | | leniency | 1 | | rawRatio | 0.063 | | effectiveRatio | 0.063 | |
| 84.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2631 | | totalAiIsmAdverbs | 8 | | found | | 0 | | adverb | "reluctantly" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | |
| | highlights | | 0 | "reluctantly" | | 1 | "sadly" | | 2 | "slightly" | | 3 | "completely" | | 4 | "quickly" | | 5 | "suddenly" | | 6 | "really" | | 7 | "truly" |
| |
| 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) | |
| 58.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2631 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "the last thing" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | word | "practiced ease" | | count | 1 |
| | 8 | | | 9 | | word | "carried the weight" | | count | 1 |
| | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "treacherous" | | 1 | "jaw clenched" | | 2 | "stomach" | | 3 | "the last thing" | | 4 | "determined" | | 5 | "flickered" | | 6 | "echoed" | | 7 | "practiced ease" | | 8 | "could feel" | | 9 | "carried the weight" | | 10 | "grave" | | 11 | "depths" | | 12 | "echoing" | | 13 | "lilt" | | 14 | "traced" | | 15 | "measured" | | 16 | "raced" | | 17 | "weight" | | 18 | "familiar" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 226 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 226 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 234 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2631 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 95 | | wordCount | 2314 | | uniqueNames | 23 | | maxNameDensity | 1.21 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 28 | | Soho | 1 | | Raven | 4 | | Nest | 5 | | Herrera | 18 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Morris | 8 | | Saint | 2 | | Christopher | 2 | | London | 2 | | Seville | 1 | | Tube | 3 | | Tomás | 3 | | Veil | 2 | | Market | 2 | | Sevillian | 1 | | Warm | 1 | | Try | 1 | | Detective | 3 | | Rain | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tube" | | 7 | "Tomás" | | 8 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Charing" | | 3 | "Cross" | | 4 | "Road" | | 5 | "London" | | 6 | "Seville" | | 7 | "Market" |
| | globalScore | 0.895 | | windowScore | 1 | |
| 76.04% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 169 | | glossingSentenceCount | 5 | | matches | | 0 | "looked like dried herbs that screamed whe" | | 1 | "as if testing something only he could feel" | | 2 | "as if sniffing the air" | | 3 | "something close to pity" | | 4 | "looked like Morris's" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2631 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 234 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 44.59 | | std | 23.64 | | cv | 0.53 | | sampleLengths | | 0 | 82 | | 1 | 67 | | 2 | 78 | | 3 | 63 | | 4 | 86 | | 5 | 49 | | 6 | 9 | | 7 | 77 | | 8 | 86 | | 9 | 4 | | 10 | 57 | | 11 | 52 | | 12 | 6 | | 13 | 85 | | 14 | 36 | | 15 | 73 | | 16 | 39 | | 17 | 72 | | 18 | 68 | | 19 | 58 | | 20 | 3 | | 21 | 39 | | 22 | 21 | | 23 | 42 | | 24 | 53 | | 25 | 62 | | 26 | 52 | | 27 | 34 | | 28 | 47 | | 29 | 30 | | 30 | 54 | | 31 | 41 | | 32 | 79 | | 33 | 57 | | 34 | 3 | | 35 | 42 | | 36 | 80 | | 37 | 43 | | 38 | 26 | | 39 | 50 | | 40 | 37 | | 41 | 4 | | 42 | 50 | | 43 | 16 | | 44 | 44 | | 45 | 57 | | 46 | 19 | | 47 | 28 | | 48 | 40 | | 49 | 54 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 226 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 409 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 234 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2059 | | adjectiveStacks | 2 | | stackExamples | | 0 | "paramedic turned underground healer." | | 1 | "underground supernatural black market" |
| | adverbCount | 59 | | adverbRatio | 0.028654686741136474 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.009713453132588635 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 234 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 234 | | mean | 11.24 | | std | 8.3 | | cv | 0.738 | | sampleLengths | | 0 | 13 | | 1 | 13 | | 2 | 16 | | 3 | 28 | | 4 | 2 | | 5 | 10 | | 6 | 8 | | 7 | 20 | | 8 | 8 | | 9 | 15 | | 10 | 7 | | 11 | 9 | | 12 | 4 | | 13 | 15 | | 14 | 4 | | 15 | 11 | | 16 | 14 | | 17 | 8 | | 18 | 13 | | 19 | 9 | | 20 | 11 | | 21 | 4 | | 22 | 9 | | 23 | 3 | | 24 | 23 | | 25 | 13 | | 26 | 12 | | 27 | 8 | | 28 | 3 | | 29 | 21 | | 30 | 15 | | 31 | 5 | | 32 | 2 | | 33 | 7 | | 34 | 13 | | 35 | 10 | | 36 | 20 | | 37 | 7 | | 38 | 12 | | 39 | 9 | | 40 | 3 | | 41 | 13 | | 42 | 2 | | 43 | 3 | | 44 | 7 | | 45 | 13 | | 46 | 12 | | 47 | 10 | | 48 | 7 | | 49 | 7 |
| |
| 55.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.358974358974359 | | totalSentences | 234 | | uniqueOpeners | 84 | |
| 62.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 212 | | matches | | 0 | "Instead he bolted down a" | | 1 | "Instead, torchlight flickered against curved" | | 2 | "Immediately three vendors turned to" | | 3 | "Then he ran." |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 59 | | totalSentences | 212 | | matches | | 0 | "She had spotted him slipping" | | 1 | "Her voice cracked against the" | | 2 | "She pumped her arms harder," | | 3 | "Her sharp jaw clenched tight" | | 4 | "He knew these streets too" | | 5 | "His warm brown eyes flashed" | | 6 | "She had been watching the" | | 7 | "He never came out." | | 8 | "She leaped over a puddle" | | 9 | "She wondered if that scar" | | 10 | "she yelled, voice hoarse" | | 11 | "He didn't answer." | | 12 | "She reached the top of" | | 13 | "It meant entering territory she" | | 14 | "She thought of Morris again," | | 15 | "She still couldn't say the" | | 16 | "She descended the steps." | | 17 | "Her bearing remained crisp despite" | | 18 | "She stayed close to the" | | 19 | "She'd heard whispers during her" |
| | ratio | 0.278 | |
| 42.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 177 | | totalSentences | 212 | | matches | | 0 | "Detective Harlow Quinn sprinted through" | | 1 | "She had spotted him slipping" | | 2 | "The name burned in her" | | 3 | "Her voice cracked against the" | | 4 | "She pumped her arms harder," | | 5 | "Her sharp jaw clenched tight" | | 6 | "This man moved with purpose," | | 7 | "He knew these streets too" | | 8 | "Herrera glanced back once." | | 9 | "His warm brown eyes flashed" | | 10 | "Quinn followed, lungs burning." | | 11 | "The rain plastered her closely" | | 12 | "Water streamed down her face," | | 13 | "She had been watching the" | | 14 | "Something rotten hid behind those" | | 15 | "Something connected to her partner's" | | 16 | "DS Morris had gone into" | | 17 | "He never came out." | | 18 | "The official report called it" | | 19 | "Quinn knew better." |
| | ratio | 0.835 | |
| 70.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 212 | | matches | | 0 | "If Herrera treated supernatural patients," | | 1 | "If she lost him in" | | 2 | "Now doubt crept in, cold" |
| | ratio | 0.014 | |
| 55.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 106 | | technicalSentenceCount | 13 | | matches | | 0 | "She leaped over a puddle that had become a small lake, her military precision keeping her balanced despite the treacherous footing." | | 1 | "And now this man, this Tomás Herrera, providing off-the-books medical care to exactly the sort of people who shouldn't exist." | | 2 | "Vendors hawked their wares under strings of colored lanterns that floated without visible support." | | 3 | "An underground supernatural black market that moved locations every full moon." | | 4 | "The rain still dripped from her clothes, creating small puddles that reflected the impossible scene above." | | 5 | "Things that might explain the way her partner's blood had turned to black smoke when it hit the ground." | | 6 | "He examined a particular blade, running his scarred forearm along its flat side as if testing something only he could feel." | | 7 | "A tall figure in a hooded cloak near the bookstall tilted its head, as if sniffing the air." | | 8 | "Medical equipment that looked both modern and ancient filled the spaces between." | | 9 | "He peeled open the brown paper to reveal a set of surgical needles that gleamed with unnatural sharpness." | | 10 | "Her brown eyes never left his face, watching for the slightest twitch that might betray a lie." | | 11 | "Herrera stopped at the bottom of the stairs. Rain could be heard hammering against the street above. He turned to face her, medallion catching what little light…" | | 12 | "She studied his face. The scar on his forearm. The way he carried himself like a man who had made peace with impossible things. Her own reflection stared back a…" |
| |
| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "he warned, voice tight" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "she yelled (yell)" | | 1 | "it hissed (hiss)" | | 2 | "she demanded (demand)" | | 3 | "he warned (warn)" |
| | dialogueSentences | 25 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.4 | | effectiveRatio | 0.32 | |