| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1496 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "softly" | | 1 | "sweetly" | | 2 | "very" |
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| 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) | |
| 56.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1496 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "treacherous" | | 1 | "tracing" | | 2 | "scanned" | | 3 | "gloom" | | 4 | "silence" | | 5 | "weight" | | 6 | "magnetic" | | 7 | "stomach" | | 8 | "scanning" | | 9 | "velvet" | | 10 | "pulsed" | | 11 | "unreadable" |
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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 | 116 | | matches | (empty) | |
| 56.65% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 4 | | narrationSentences | 116 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1496 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "Stay behind me, she said to no one in particular, voice flat, steel edged." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1496 | | uniqueNames | 20 | | maxNameDensity | 0.67 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 10 | | Met | 1 | | Soho | 4 | | Tomás | 3 | | Camden | 1 | | Victorian | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Bone | 2 | | Token | 1 | | Required | 1 | | Morris | 3 | | Detective | 2 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Crown | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" |
| | places | | 0 | "London" | | 1 | "Met" | | 2 | "Soho" | | 3 | "Camden" | | 4 | "Bone" | | 5 | "Crown" |
| | globalScore | 1 | | windowScore | 1 | |
| 55.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 3 | | matches | | 0 | "vibration that seemed to come up through the soles of her boots and settle in her teeth" | | 1 | "specimens that seemed to twist when unobserved, brass instruments that spun without magnetic pull, stacks of leather ledgers bound in materials that made Quinn's stomach tighten" | | 2 | "quite match the people who cast them" |
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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 | 1496 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 88 | | std | 48.23 | | cv | 0.548 | | sampleLengths | | 0 | 136 | | 1 | 131 | | 2 | 132 | | 3 | 88 | | 4 | 123 | | 5 | 145 | | 6 | 156 | | 7 | 7 | | 8 | 92 | | 9 | 11 | | 10 | 72 | | 11 | 126 | | 12 | 79 | | 13 | 18 | | 14 | 77 | | 15 | 17 | | 16 | 86 |
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| 96.19% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 116 | | matches | | 0 | "was plastered" | | 1 | "was locked" | | 2 | "was drawn" |
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| 44.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 258 | | matches | | 0 | "wasn't running" | | 1 | "was bleeding" | | 2 | "was leading" | | 3 | "was chasing" | | 4 | "weren't just running" | | 5 | "were operating" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1496 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.02406417112299465 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.012032085561497326 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 12.9 | | std | 9.68 | | cv | 0.751 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 36 | | 3 | 32 | | 4 | 5 | | 5 | 22 | | 6 | 4 | | 7 | 5 | | 8 | 4 | | 9 | 22 | | 10 | 4 | | 11 | 4 | | 12 | 33 | | 13 | 2 | | 14 | 22 | | 15 | 22 | | 16 | 18 | | 17 | 16 | | 18 | 4 | | 19 | 27 | | 20 | 14 | | 21 | 11 | | 22 | 13 | | 23 | 3 | | 24 | 20 | | 25 | 22 | | 26 | 2 | | 27 | 19 | | 28 | 1 | | 29 | 2 | | 30 | 24 | | 31 | 18 | | 32 | 4 | | 33 | 20 | | 34 | 3 | | 35 | 13 | | 36 | 13 | | 37 | 19 | | 38 | 9 | | 39 | 7 | | 40 | 38 | | 41 | 21 | | 42 | 7 | | 43 | 14 | | 44 | 3 | | 45 | 20 | | 46 | 32 | | 47 | 22 | | 48 | 11 | | 49 | 9 |
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| 48.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3706896551724138 | | totalSentences | 116 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 108 | | matches | (empty) | | ratio | 0 | |
| 57.04% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 108 | | matches | | 0 | "Her closely cropped salt and" | | 1 | "She barely registered the cold." | | 2 | "Her entire focus was locked" | | 3 | "He wasn't running randomly." | | 4 | "He was on a route." | | 5 | "She checked her wrist." | | 6 | "She had seventeen years of" | | 7 | "She had been chasing a" | | 8 | "She flanked a parked delivery" | | 9 | "He reached a heavy steel" | | 10 | "It held firm." | | 11 | "She scanned the brickwork, found" | | 12 | "She slipped through the gap," | | 13 | "She stepped inside." | | 14 | "It was the skeleton of" | | 15 | "Her target had merged into" | | 16 | "She spotted a stone archway" | | 17 | "They each placed a carved" | | 18 | "She wasn't equipped for this." | | 19 | "She thought of her partner," |
| | ratio | 0.407 | |
| 61.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 108 | | matches | | 0 | "Rain fell in cold, relentless" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "Her closely cropped salt and" | | 3 | "She barely registered the cold." | | 4 | "Her entire focus was locked" | | 5 | "He wasn't running randomly." | | 6 | "He was on a route." | | 7 | "She checked her wrist." | | 8 | "The worn leather watch on" | | 9 | "Time was bleeding out." | | 10 | "She had seventeen years of" | | 11 | "Ghosts who moved through Soho's" | | 12 | "She had been chasing a" | | 13 | "Tonight, she had finally caught" | | 14 | "The suspect ducked under a" | | 15 | "Quinn didn't break stride." | | 16 | "She flanked a parked delivery" | | 17 | "He reached a heavy steel" | | 18 | "The door slammed shut with" | | 19 | "Quinn hit it a second" |
| | ratio | 0.796 | |
| 92.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 108 | | matches | | 0 | "If you want the truth" | | 1 | "If you want to drag" |
| | ratio | 0.019 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 10 | | matches | | 0 | "Her closely cropped salt and pepper hair was plastered to her skull, water tracing sharp lines down her face before dripping off a jaw that had been set hard at…" | | 1 | "She had seventeen years of decorated service in the Met, a stack of commendations gathering dust on a home shelf, and a case file that had been bleeding her dry…" | | 2 | "Ghosts who moved through Soho's underbelly, trading in stolen alchemical vials, banned substances, and information that made seasoned police officers look away." | | 3 | "The corridor opened abruptly into a vast, cavernous space that defied municipal logic." | | 4 | "Shelves groaned under the weight of strange artifacts: jars of preserved botanical specimens that seemed to twist when unobserved, brass instruments that spun w…" | | 5 | "The air smelled of damp earth, crushed herbs, ozone, and something sweetly alien that clung to the back of her throat." | | 6 | "An underground trade network that relocated beneath the city with every full moon, accessible only to those who knew how to ask, and protected by rules that pre…" | | 7 | "But standing in the dark belly of a subterranean trade that defied every rule she lived by, the certainty began to fracture." | | 8 | "She thought of the secret meetings rumored to happen in back rooms behind false bookshelves in Soho bars, the unmarked graves of informants who knew too much, t…" | | 9 | "She closed her eyes, remembered Morris's laugh, the smell of his cedar cologne, the empty chair at the evidence board, the unexplained silence that had followed…" |
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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 | |