| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1231 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 51.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1231 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "structure" | | 2 | "gloom" | | 3 | "scanning" | | 4 | "weight" | | 5 | "echoed" | | 6 | "pulsed" | | 7 | "rhythmic" | | 8 | "silence" |
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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 | 126 | | matches | (empty) | |
| 86.17% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 0 | | narrationSentences | 126 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | 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 | 1231 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1231 | | uniqueNames | 11 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 3 | | Saint | 1 | | Christopher | 1 | | Detective | 1 | | Metropolitan | 1 | | Morris | 4 | | Veil | 1 | | Market | 2 | | Herrera | 5 | | Quinn | 13 | | You | 9 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Herrera" | | 5 | "Quinn" | | 6 | "You" |
| | places | | | globalScore | 0.972 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like paper" | | 1 | "felt like a key" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1231 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 126 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 64.79 | | std | 40.89 | | cv | 0.631 | | sampleLengths | | 0 | 120 | | 1 | 89 | | 2 | 99 | | 3 | 102 | | 4 | 76 | | 5 | 8 | | 6 | 78 | | 7 | 24 | | 8 | 36 | | 9 | 10 | | 10 | 51 | | 11 | 28 | | 12 | 48 | | 13 | 21 | | 14 | 48 | | 15 | 68 | | 16 | 117 | | 17 | 46 | | 18 | 162 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 218 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 126 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1235 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.011336032388663968 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0032388663967611335 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.77 | | std | 5.84 | | cv | 0.598 | | sampleLengths | | 0 | 17 | | 1 | 19 | | 2 | 22 | | 3 | 10 | | 4 | 10 | | 5 | 5 | | 6 | 21 | | 7 | 8 | | 8 | 8 | | 9 | 13 | | 10 | 6 | | 11 | 22 | | 12 | 7 | | 13 | 7 | | 14 | 13 | | 15 | 16 | | 16 | 5 | | 17 | 2 | | 18 | 13 | | 19 | 23 | | 20 | 15 | | 21 | 10 | | 22 | 10 | | 23 | 4 | | 24 | 22 | | 25 | 5 | | 26 | 13 | | 27 | 25 | | 28 | 6 | | 29 | 7 | | 30 | 18 | | 31 | 12 | | 32 | 16 | | 33 | 10 | | 34 | 20 | | 35 | 3 | | 36 | 11 | | 37 | 23 | | 38 | 9 | | 39 | 8 | | 40 | 7 | | 41 | 2 | | 42 | 16 | | 43 | 15 | | 44 | 7 | | 45 | 9 | | 46 | 22 | | 47 | 5 | | 48 | 10 | | 49 | 9 |
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| 45.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3412698412698413 | | totalSentences | 126 | | uniqueOpeners | 43 | |
| 28.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 118 | | matches | | 0 | "Especially when a Metropolitan investigator" |
| | ratio | 0.008 | |
| 87.80% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 118 | | matches | | 0 | "She checked the worn leather" | | 1 | "She pushed off the wall," | | 2 | "She moved fast, silent, cutting" | | 3 | "She raised her palm, demanding" | | 4 | "He ignored her, fumbled with" | | 5 | "He disappeared into the gloom." | | 6 | "She stepped past the threshold." | | 7 | "Their accents tangled through the" | | 8 | "She noted a hooded woman" | | 9 | "She moved forward, boots whispering" | | 10 | "You are tracking mud on" | | 11 | "He wore a stained leather" | | 12 | "He gestured with the scalpel" | | 13 | "I am following a suspect." | | 14 | "She shifted her weight, eyes" | | 15 | "He passed through." | | 16 | "He tapped the silver scar" | | 17 | "You are far from your" | | 18 | "He set the scalpel down" | | 19 | "You look for answers in" |
| | ratio | 0.331 | |
| 36.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 118 | | matches | | 0 | "The rain hammered the wet" | | 1 | "Quinn pressed her shoulder into" | | 2 | "She checked the worn leather" | | 3 | "Military precision governed her movements." | | 4 | "She pushed off the wall," | | 5 | "The city breathed wet concrete" | | 6 | "She moved fast, silent, cutting" | | 7 | "A heavy iron door yawned" | | 8 | "Water cascaded from the rusted" | | 9 | "The suspect stood in the" | | 10 | "Quinn closed the distance without" | | 11 | "She raised her palm, demanding" | | 12 | "He ignored her, fumbled with" | | 13 | "The structure swung on warped" | | 14 | "He disappeared into the gloom." | | 15 | "The concrete climbed beneath her" | | 16 | "The air cooled, carrying the" | | 17 | "The staircase widened, opening into" | | 18 | "Iron girders arched overhead like" | | 19 | "An abandoned tube station." |
| | ratio | 0.847 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 80.20% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 5 | | matches | | 0 | "The staircase widened, opening into a cavernous expanse that defied the cramped London footprint above." | | 1 | "Especially when a Metropolitan investigator disappears into a ghost station after a partner vanishes with a case file that turns to static." | | 2 | "Morris’s empty desk still held a chipped ceramic mug, a photograph of a golden retriever, a case file that dissolved into static under her fingertips." | | 3 | "Following the suspect meant crossing into a jurisdiction that operated outside statutes and streetlights." | | 4 | "Water dripped from the ceiling, pooling in shallow depressions that reflected the flickering sconces." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |