| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 675 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 70.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 675 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "reminder" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "trembled" |
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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 | 54 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | 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 | 675 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 632 | | uniqueNames | 12 | | maxNameDensity | 1.11 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Street" | | discoveredNames | | Oxford | 1 | | Street | 3 | | Berwick | 1 | | Quinn | 7 | | Old | 1 | | Compton | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 3 | | London | 1 | | Spanish | 1 | | Morris | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Herrera" | | 4 | "Morris" |
| | places | | 0 | "Oxford" | | 1 | "Street" | | 2 | "Berwick" | | 3 | "Old" | | 4 | "Compton" | | 5 | "London" | | 6 | "Spanish" |
| | globalScore | 0.946 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | 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 | 675 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 33.75 | | std | 22.7 | | cv | 0.673 | | sampleLengths | | 0 | 55 | | 1 | 70 | | 2 | 7 | | 3 | 35 | | 4 | 71 | | 5 | 8 | | 6 | 53 | | 7 | 15 | | 8 | 14 | | 9 | 4 | | 10 | 64 | | 11 | 51 | | 12 | 18 | | 13 | 7 | | 14 | 55 | | 15 | 36 | | 16 | 55 | | 17 | 20 | | 18 | 23 | | 19 | 14 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 54 | | matches | | 0 | "been prised" | | 1 | "was splintered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 99 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 634 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.02996845425867508 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00946372239747634 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 11.44 | | std | 7.01 | | cv | 0.613 | | sampleLengths | | 0 | 20 | | 1 | 20 | | 2 | 12 | | 3 | 3 | | 4 | 10 | | 5 | 22 | | 6 | 17 | | 7 | 2 | | 8 | 19 | | 9 | 3 | | 10 | 4 | | 11 | 3 | | 12 | 13 | | 13 | 19 | | 14 | 7 | | 15 | 27 | | 16 | 2 | | 17 | 21 | | 18 | 2 | | 19 | 2 | | 20 | 10 | | 21 | 8 | | 22 | 19 | | 23 | 13 | | 24 | 21 | | 25 | 6 | | 26 | 9 | | 27 | 7 | | 28 | 7 | | 29 | 4 | | 30 | 23 | | 31 | 6 | | 32 | 10 | | 33 | 25 | | 34 | 20 | | 35 | 2 | | 36 | 13 | | 37 | 16 | | 38 | 6 | | 39 | 8 | | 40 | 4 | | 41 | 7 | | 42 | 7 | | 43 | 29 | | 44 | 11 | | 45 | 8 | | 46 | 11 | | 47 | 13 | | 48 | 6 | | 49 | 6 |
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| 75.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4915254237288136 | | totalSentences | 59 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 47 | | matches | | 0 | "Just a cold stairwell and" | | 1 | "Only the hum answered her," |
| | ratio | 0.043 | |
| 66.81% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 47 | | matches | | 0 | "She ignored it." | | 1 | "He took the corner onto" | | 2 | "He didn't stop." | | 3 | "He vaulted a low railing," | | 4 | "Her left wrist ached where" | | 5 | "She pushed the thought down" | | 6 | "He hit one with his" | | 7 | "he panted, not turning" | | 8 | "He reached the end of" | | 9 | "She pulled her torch from" | | 10 | "Her radio crackled on her" | | 11 | "She did not answer." | | 12 | "It carried the lily smell" | | 13 | "She had her warrant card," | | 14 | "Her hand went to her" | | 15 | "She had taken it from" | | 16 | "She had not yet worked" | | 17 | "she called, her voice flat" |
| | ratio | 0.383 | |
| 45.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 47 | | matches | | 0 | "The rain had been falling" | | 1 | "Harlow Quinn kept her eyes" | | 2 | "Water ran off the peak" | | 3 | "She ignored it." | | 4 | "He took the corner onto" | | 5 | "The green neon above the" | | 6 | "Quinn saw the crooked medallion" | | 7 | "He didn't stop." | | 8 | "He vaulted a low railing," | | 9 | "The scar on his left" | | 10 | "Quinn's shoes skidded on the" | | 11 | "Her left wrist ached where" | | 12 | "Morris had been ahead of" | | 13 | "She pushed the thought down" | | 14 | "Herrera cut left into an" | | 15 | "He hit one with his" | | 16 | "Quinn stepped over the spill" | | 17 | "he panted, not turning" | | 18 | "He reached the end of" | | 19 | "Quinn reached it three seconds" |
| | ratio | 0.83 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 4 | | matches | | 0 | "The rain had been falling since Oxford Street, a thin, mean drizzle that turned to sheets somewhere past Berwick Street." | | 1 | "Ex-paramedic, struck off, now a man who patched up things that had no business walking into an NHS ward." | | 2 | "Beyond it, stairs led down into a darkness that smelled of wet stone and something sweeter underneath, like lilies left too long in a vase." | | 3 | "Far down, she heard voices, a low hum of conversation, a bass note like an engine idling, and then a bright burst of laughter that didn't sound entirely human." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "he panted, not turning" | | 1 | "she called, her voice flat in the stone throat of the stairwell" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "he panted (pant)" |
| | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.5 | | effectiveRatio | 0.444 | |