| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 91 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1688 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1688 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 199 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1688 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1066 | | uniqueNames | 12 | | maxNameDensity | 2.63 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 28 | | Metropolitan | 1 | | Police | 1 | | Tube | 1 | | Detective | 2 | | Sergeant | 1 | | Martin | 1 | | Pike | 17 | | Patel | 4 | | Eva | 7 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Martin" | | 4 | "Pike" | | 5 | "Patel" | | 6 | "Eva" | | 7 | "Kowalski" |
| | places | (empty) | | globalScore | 0.187 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1688 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 199 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 112 | | mean | 15.07 | | std | 13.55 | | cv | 0.899 | | sampleLengths | | 0 | 47 | | 1 | 10 | | 2 | 50 | | 3 | 28 | | 4 | 4 | | 5 | 24 | | 6 | 7 | | 7 | 3 | | 8 | 9 | | 9 | 4 | | 10 | 16 | | 11 | 23 | | 12 | 26 | | 13 | 55 | | 14 | 1 | | 15 | 16 | | 16 | 3 | | 17 | 19 | | 18 | 25 | | 19 | 5 | | 20 | 5 | | 21 | 49 | | 22 | 1 | | 23 | 7 | | 24 | 42 | | 25 | 4 | | 26 | 19 | | 27 | 3 | | 28 | 16 | | 29 | 47 | | 30 | 3 | | 31 | 3 | | 32 | 4 | | 33 | 25 | | 34 | 3 | | 35 | 21 | | 36 | 19 | | 37 | 6 | | 38 | 28 | | 39 | 8 | | 40 | 40 | | 41 | 8 | | 42 | 11 | | 43 | 5 | | 44 | 9 | | 45 | 8 | | 46 | 49 | | 47 | 7 | | 48 | 5 | | 49 | 38 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 174 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 199 | | ratio | 0.005 | | matches | | 0 | "A cloth shopping bag rested near his feet; three packets of tea had spilled from it, their paper labels darkened where they touched the blood." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 471 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.0021231422505307855 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 199 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 199 | | mean | 8.48 | | std | 4.86 | | cv | 0.573 | | sampleLengths | | 0 | 19 | | 1 | 9 | | 2 | 19 | | 3 | 10 | | 4 | 11 | | 5 | 12 | | 6 | 11 | | 7 | 16 | | 8 | 13 | | 9 | 3 | | 10 | 12 | | 11 | 4 | | 12 | 7 | | 13 | 17 | | 14 | 7 | | 15 | 3 | | 16 | 9 | | 17 | 4 | | 18 | 2 | | 19 | 14 | | 20 | 14 | | 21 | 9 | | 22 | 5 | | 23 | 21 | | 24 | 8 | | 25 | 15 | | 26 | 7 | | 27 | 25 | | 28 | 1 | | 29 | 16 | | 30 | 3 | | 31 | 6 | | 32 | 13 | | 33 | 6 | | 34 | 12 | | 35 | 7 | | 36 | 5 | | 37 | 5 | | 38 | 7 | | 39 | 8 | | 40 | 11 | | 41 | 11 | | 42 | 12 | | 43 | 1 | | 44 | 7 | | 45 | 8 | | 46 | 14 | | 47 | 20 | | 48 | 4 | | 49 | 19 |
| |
| 60.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.37185929648241206 | | totalSentences | 199 | | uniqueOpeners | 74 | |
| 29.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 113 | | matches | | 0 | "Dark red marked each one." |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 113 | | matches | | 0 | "She stepped over a string" | | 1 | "He rose when he saw" | | 2 | "he greeted her" | | 3 | "He lay between a counter" | | 4 | "His collar had folded beneath" | | 5 | "She leaned close enough to" | | 6 | "She followed them without stepping" | | 7 | "It sat inches from the" | | 8 | "She returned to the body." | | 9 | "She followed it to his" | | 10 | "She lowered the cuff" | | 11 | "It had been cut close" | | 12 | "Her gaze moved to the" | | 13 | "Its glass had cracked at" | | 14 | "He pointed towards the tracks" | | 15 | "She moved to the service" | | 16 | "It turned back across her" | | 17 | "She was short, with curly" | | 18 | "Her eyes stayed on the" | | 19 | "she told Quinn" |
| | ratio | 0.239 | |
| 35.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 113 | | matches | | 0 | "Detective Harlow Quinn reached the" | | 1 | "Someone had painted black bars" | | 2 | "Canvas awnings ran along the" | | 3 | "Traders had left bowls, scales" | | 4 | "A row of glass jars" | | 5 | "Quinn checked the time on" | | 6 | "She stepped over a string" | | 7 | "The constable fell into step" | | 8 | "He rose when he saw" | | 9 | "he greeted her" | | 10 | "Quinn took in the man" | | 11 | "He lay between a counter" | | 12 | "Blood filled the grout beside" | | 13 | "A cloth shopping bag rested" | | 14 | "Pike gestured at the vacant" | | 15 | "Quinn looked back along the" | | 16 | "A lantern burned behind a" | | 17 | "Pike offered her a pair" | | 18 | "Quinn pulled them on and" | | 19 | "The dead man wore a" |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 113 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn reached the platform by a staircase lined with advertisements for concerts that had ended years ago." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 91 | | tagDensity | 0.066 | | leniency | 0.132 | | rawRatio | 0.167 | | effectiveRatio | 0.022 | |