| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 113 | | tagDensity | 0.035 | | leniency | 0.071 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2497 | | 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) | |
| 81.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2497 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "silence" | | 3 | "footsteps" | | 4 | "grave" | | 5 | "measured" |
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| 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 | 247 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 247 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 354 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2496 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 115 | | wordCount | 1939 | | uniqueNames | 30 | | maxNameDensity | 2.06 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 40 | | Raven | 2 | | Nest | 2 | | Tomás | 1 | | Herrera | 36 | | Mini | 1 | | Piccadilly | 1 | | Circus | 1 | | Christopher | 1 | | Wardour | 1 | | Street | 3 | | Oxford | 1 | | Seville | 1 | | Waterloo | 1 | | Bridge | 1 | | Spanish | 1 | | Met | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Underground | 2 | | Northern | 1 | | Bermondsey | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | One | 3 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "One" |
| | places | | 0 | "Soho" | | 1 | "Piccadilly" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "Oxford" | | 5 | "Seville" | | 6 | "Waterloo" | | 7 | "Bridge" | | 8 | "Tottenham" | | 9 | "Court" | | 10 | "Road" | | 11 | "Bermondsey" | | 12 | "Veil" |
| | globalScore | 0.469 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 163 | | 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 | 2496 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 354 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 250 | | mean | 9.98 | | std | 10.99 | | cv | 1.101 | | sampleLengths | | 0 | 34 | | 1 | 7 | | 2 | 6 | | 3 | 2 | | 4 | 2 | | 5 | 27 | | 6 | 4 | | 7 | 38 | | 8 | 46 | | 9 | 4 | | 10 | 6 | | 11 | 6 | | 12 | 7 | | 13 | 16 | | 14 | 5 | | 15 | 2 | | 16 | 10 | | 17 | 3 | | 18 | 1 | | 19 | 33 | | 20 | 2 | | 21 | 10 | | 22 | 21 | | 23 | 35 | | 24 | 6 | | 25 | 5 | | 26 | 9 | | 27 | 7 | | 28 | 5 | | 29 | 5 | | 30 | 5 | | 31 | 8 | | 32 | 28 | | 33 | 14 | | 34 | 72 | | 35 | 8 | | 36 | 18 | | 37 | 9 | | 38 | 21 | | 39 | 1 | | 40 | 3 | | 41 | 6 | | 42 | 4 | | 43 | 10 | | 44 | 3 | | 45 | 6 | | 46 | 3 | | 47 | 5 | | 48 | 5 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 247 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 351 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 354 | | ratio | 0.006 | | matches | | 0 | "Licence revoked after a run of unauthorised treatments—blood transfusions without matching records, compounds that no hospital pharmacist could identify, patients discharged under false names." | | 1 | "Its note had weight; Quinn felt it in her teeth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1943 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.0159547092125579 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002058672156459084 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 354 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 354 | | mean | 7.05 | | std | 4.78 | | cv | 0.678 | | sampleLengths | | 0 | 6 | | 1 | 28 | | 2 | 7 | | 3 | 6 | | 4 | 2 | | 5 | 2 | | 6 | 6 | | 7 | 21 | | 8 | 4 | | 9 | 5 | | 10 | 8 | | 11 | 19 | | 12 | 6 | | 13 | 6 | | 14 | 8 | | 15 | 24 | | 16 | 6 | | 17 | 2 | | 18 | 4 | | 19 | 6 | | 20 | 6 | | 21 | 7 | | 22 | 16 | | 23 | 5 | | 24 | 2 | | 25 | 10 | | 26 | 3 | | 27 | 1 | | 28 | 12 | | 29 | 10 | | 30 | 11 | | 31 | 2 | | 32 | 10 | | 33 | 8 | | 34 | 2 | | 35 | 11 | | 36 | 9 | | 37 | 10 | | 38 | 3 | | 39 | 2 | | 40 | 11 | | 41 | 6 | | 42 | 5 | | 43 | 9 | | 44 | 7 | | 45 | 5 | | 46 | 5 | | 47 | 5 | | 48 | 8 | | 49 | 11 |
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| 53.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3474576271186441 | | totalSentences | 354 | | uniqueOpeners | 123 | |
| 29.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 228 | | matches | | 0 | "Then he vanished into the" | | 1 | "Then she unfastened the worn" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 228 | | matches | | 0 | "Their eyes met through the" | | 1 | "His curse chased her for" | | 2 | "She ignored both, crossed against" | | 3 | "He moved like a man" | | 4 | "He hooked left into Wardour" | | 5 | "She caught his shoulder, moved" | | 6 | "He watched the cars, watched" | | 7 | "His fist closed around the" | | 8 | "She slapped her warrant card" | | 9 | "Her worn leather watch dragged" | | 10 | "She cleared the corner." | | 11 | "He was fifty yards ahead," | | 12 | "He looked back." | | 13 | "His voice carried with a" | | 14 | "His stride hitched." | | 15 | "Her lungs burned under her" | | 16 | "He never reached for a" | | 17 | "He checked his watch twice." | | 18 | "He wasn’t escaping arrest." | | 19 | "He was late." |
| | ratio | 0.241 | |
| 38.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 192 | | totalSentences | 228 | | matches | | 0 | "Rain lacquered Soho black and" | | 1 | "Detective Harlow Quinn shouldered through" | | 2 | "Their eyes met through the" | | 3 | "A taxi horn swallowed the" | | 4 | "Herrera vaulted the bonnet of" | | 5 | "Quinn went after him." | | 6 | "A cyclist clipped her elbow." | | 7 | "His curse chased her for" | | 8 | "She ignored both, crossed against" | | 9 | "Herrera had twenty yards on" | | 10 | "He moved like a man" | | 11 | "A chain flashed at his" | | 12 | "Patron saint of travellers." | | 13 | "He hooked left into Wardour" | | 14 | "Quinn touched the radio at" | | 15 | "Static scraped back at her." | | 16 | "The rain struck harder, bouncing" | | 17 | "Herrera shoved through a knot" | | 18 | "She caught his shoulder, moved" | | 19 | "Herrera crossed Oxford Street beneath" |
| | ratio | 0.842 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 228 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 1 | | matches | | 0 | "Stalls crowded the old tracks where tables displayed knives with twitching shadows, silver cages full of bottled whispers, powders that crawled across their tra…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn muttered (mutter)" |
| | dialogueSentences | 113 | | tagDensity | 0.009 | | leniency | 0.018 | | rawRatio | 1 | | effectiveRatio | 0.018 | |