| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 105 | | tagDensity | 0.01 | | leniency | 0.019 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2383 | | 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) | |
| 85.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2383 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "charged" | | 1 | "weight" | | 2 | "pulsed" | | 3 | "familiar" | | 4 | "trembled" | | 5 | "streaming" |
| |
| 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 | 238 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 238 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 342 | | 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 | 2383 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 114 | | wordCount | 1940 | | uniqueNames | 25 | | maxNameDensity | 1.96 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Raven | 3 | | Nest | 4 | | Harlow | 2 | | Quinn | 38 | | Herrera | 2 | | Wardour | 2 | | Street | 3 | | Saint | 2 | | Christopher | 2 | | Tomás | 30 | | Oxford | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Morris | 4 | | Lambeth | 1 | | Camden | 2 | | London | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | One | 4 | | Three | 3 | | Metal | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Morris" | | 9 | "Veil" | | 10 | "One" | | 11 | "Metal" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Oxford" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Lambeth" | | 8 | "London" |
| | globalScore | 0.521 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 154 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2383 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 342 | | matches | | 0 | "warned that people" | | 1 | "treated that way" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 237 | | mean | 10.05 | | std | 10.97 | | cv | 1.091 | | sampleLengths | | 0 | 39 | | 1 | 43 | | 2 | 1 | | 3 | 13 | | 4 | 8 | | 5 | 35 | | 6 | 5 | | 7 | 3 | | 8 | 3 | | 9 | 4 | | 10 | 1 | | 11 | 39 | | 12 | 7 | | 13 | 5 | | 14 | 8 | | 15 | 47 | | 16 | 4 | | 17 | 24 | | 18 | 2 | | 19 | 4 | | 20 | 6 | | 21 | 51 | | 22 | 9 | | 23 | 4 | | 24 | 36 | | 25 | 1 | | 26 | 9 | | 27 | 15 | | 28 | 25 | | 29 | 2 | | 30 | 6 | | 31 | 7 | | 32 | 5 | | 33 | 5 | | 34 | 5 | | 35 | 33 | | 36 | 16 | | 37 | 9 | | 38 | 11 | | 39 | 5 | | 40 | 40 | | 41 | 5 | | 42 | 24 | | 43 | 16 | | 44 | 4 | | 45 | 9 | | 46 | 9 | | 47 | 8 | | 48 | 8 | | 49 | 7 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 238 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 342 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 342 | | ratio | 0.003 | | matches | | 0 | "One guard wore a railway porter’s cap; the other had antlers bound flat along his skull with copper wire." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1947 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.011299435028248588 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005136106831022085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 342 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 342 | | mean | 6.97 | | std | 4.99 | | cv | 0.717 | | sampleLengths | | 0 | 8 | | 1 | 6 | | 2 | 5 | | 3 | 20 | | 4 | 27 | | 5 | 6 | | 6 | 10 | | 7 | 1 | | 8 | 13 | | 9 | 8 | | 10 | 7 | | 11 | 5 | | 12 | 7 | | 13 | 16 | | 14 | 5 | | 15 | 3 | | 16 | 3 | | 17 | 4 | | 18 | 1 | | 19 | 13 | | 20 | 5 | | 21 | 21 | | 22 | 7 | | 23 | 5 | | 24 | 8 | | 25 | 8 | | 26 | 19 | | 27 | 20 | | 28 | 4 | | 29 | 24 | | 30 | 2 | | 31 | 4 | | 32 | 6 | | 33 | 6 | | 34 | 19 | | 35 | 9 | | 36 | 17 | | 37 | 9 | | 38 | 4 | | 39 | 15 | | 40 | 8 | | 41 | 13 | | 42 | 1 | | 43 | 9 | | 44 | 10 | | 45 | 5 | | 46 | 4 | | 47 | 11 | | 48 | 4 | | 49 | 6 |
| |
| 53.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3382352941176471 | | totalSentences | 340 | | uniqueOpeners | 115 | |
| 30.17% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 221 | | matches | | 0 | "Then he ran." | | 1 | "Then she ripped the baton" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 53 | | totalSentences | 221 | | matches | | 0 | "Her coat clung between her" | | 1 | "She checked the worn leather" | | 2 | "He looked straight at her." | | 3 | "His face tightened." | | 4 | "He shouldered between two men" | | 5 | "His coat flew open, exposing" | | 6 | "He clipped a sandwich board," | | 7 | "She entered the alley." | | 8 | "He shoved through a knot" | | 9 | "She hurdled the first." | | 10 | "Her heel landed inside the" | | 11 | "She tore free without breaking" | | 12 | "His voice carried back through" | | 13 | "He slipped into the crowd." | | 14 | "She dropped from the rail" | | 15 | "She twisted aside, caught herself" | | 16 | "He saw it too." | | 17 | "He changed direction and charged" | | 18 | "He might have meant officers." | | 19 | "He might have meant civilians." |
| | ratio | 0.24 | |
| 48.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 182 | | totalSentences | 221 | | matches | | 0 | "Detective Harlow Quinn stood beneath" | | 1 | "Her coat clung between her" | | 2 | "She checked the worn leather" | | 3 | "Tomás Herrera had entered the" | | 4 | "Quinn watched him turn towards" | | 5 | "Olive skin washed green beneath" | | 6 | "He looked straight at her." | | 7 | "His face tightened." | | 8 | "Quinn left the shelter." | | 9 | "He shouldered between two men" | | 10 | "A minicab struck its horn." | | 11 | "Tyres hissed over standing water" | | 12 | "Quinn reached the road a" | | 13 | "The driver’s abuse followed her" | | 14 | "Tomás glanced over his shoulder" | | 15 | "His coat flew open, exposing" | | 16 | "He clipped a sandwich board," | | 17 | "Quinn drew her radio." | | 18 | "That description barely touched the" | | 19 | "Hospital-grade tubing in a skip" |
| | ratio | 0.824 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 221 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 3 | | matches | | 0 | "It carried smells that did not belong beneath Camden or anywhere near London: hot iron, cinnamon, damp soil, something sweet with a rotten edge." | | 1 | "Beside her, a child-sized figure in a waxed coat held a glass jar containing something that tapped from inside." | | 2 | "At midnight, according to the informant who had died before finishing his statement, the Veil Market moved." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 105 | | tagDensity | 0.01 | | leniency | 0.019 | | rawRatio | 0 | | effectiveRatio | 0 | |