| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.7 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1758 | | 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) | |
| 74.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1758 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "scanning" | | 1 | "measured" | | 2 | "weight" | | 3 | "grave" | | 4 | "silence" | | 5 | "vibrated" | | 6 | "mosaic" | | 7 | "glinting" | | 8 | "electric" |
| |
| 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 | 127 | | matches | (empty) | |
| 86.61% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 127 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1755 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 82 | | wordCount | 1665 | | uniqueNames | 31 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 14 | | Raven | 1 | | Nest | 2 | | Herrera | 12 | | Tomás | 2 | | Tommy | 2 | | Seville | 2 | | Saint | 2 | | Christopher | 2 | | Old | 1 | | Compton | 1 | | Street | 3 | | Morris | 5 | | Dean | 1 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Tottenham | 1 | | Court | 1 | | High | 1 | | Met | 2 | | Underground | 1 | | Veil | 4 | | Market | 4 | | Tube | 2 | | Camden | 3 | | Control | 1 | | Hackney | 1 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Herrera" | | 4 | "Tomás" | | 5 | "Tommy" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Met" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Seville" | | 3 | "Old" | | 4 | "Compton" | | 5 | "Street" | | 6 | "Dean" | | 7 | "Charing" | | 8 | "Cross" | | 9 | "Road" | | 10 | "Tottenham" | | 11 | "Court" | | 12 | "High" | | 13 | "Veil" | | 14 | "Market" | | 15 | "Camden" | | 16 | "Hackney" | | 17 | "Three" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | 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 | 1755 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 54.84 | | std | 41.28 | | cv | 0.753 | | sampleLengths | | 0 | 161 | | 1 | 40 | | 2 | 148 | | 3 | 33 | | 4 | 12 | | 5 | 32 | | 6 | 71 | | 7 | 53 | | 8 | 115 | | 9 | 63 | | 10 | 6 | | 11 | 97 | | 12 | 65 | | 13 | 16 | | 14 | 73 | | 15 | 51 | | 16 | 21 | | 17 | 81 | | 18 | 1 | | 19 | 123 | | 20 | 9 | | 21 | 72 | | 22 | 37 | | 23 | 3 | | 24 | 93 | | 25 | 2 | | 26 | 67 | | 27 | 57 | | 28 | 27 | | 29 | 68 | | 30 | 29 | | 31 | 29 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 271 | | matches | | 0 | "were trying" | | 1 | "was crossing" | | 2 | "was listening" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 6 | | flaggedSentences | 8 | | totalSentences | 130 | | ratio | 0.062 | | matches | | 0 | "At five-foot-nine she carried herself with a military precision that had survived eighteen years of decorated service; rain dripped from her cropped salt-and-pepper hair and tracked down the sharp line of her jaw." | | 1 | "A bus hissed past, throwing a sheet of spray; she turned her face into her collar and tasted ozone and wet wool." | | 2 | "She had suspected the clique for months—protection, extortion, worse." | | 3 | "He drew out a thong; strung on it was a sliver of bone the length of a finger, carved with symbols that made Quinn's eyes ache if she looked too long." | | 4 | "No backup; she'd cut Control off ten minutes ago because she didn't know who was listening." | | 5 | "Her father had been a soldier; she had joined the Met at twenty-three with a soldier's spine and a soldier's certainty." | | 6 | "The gate's closer hissed; the gap narrowed to a hand's width." | | 7 | "She hit the top step shoulder-first, the iron grinding against her leather jacket, and got her left wrist—watch and all—into the gap." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1682 | | adjectiveStacks | 2 | | stackExamples | | 0 | "faded green-tiled lintel" | | 1 | "vast, pressing against her" |
| | adverbCount | 32 | | adverbRatio | 0.019024970273483946 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.002972651605231867 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 13.5 | | std | 9.02 | | cv | 0.668 | | sampleLengths | | 0 | 22 | | 1 | 27 | | 2 | 22 | | 3 | 16 | | 4 | 20 | | 5 | 33 | | 6 | 12 | | 7 | 7 | | 8 | 2 | | 9 | 16 | | 10 | 17 | | 11 | 7 | | 12 | 3 | | 13 | 44 | | 14 | 2 | | 15 | 13 | | 16 | 18 | | 17 | 20 | | 18 | 13 | | 19 | 21 | | 20 | 14 | | 21 | 7 | | 22 | 26 | | 23 | 5 | | 24 | 7 | | 25 | 4 | | 26 | 28 | | 27 | 7 | | 28 | 15 | | 29 | 27 | | 30 | 22 | | 31 | 9 | | 32 | 17 | | 33 | 1 | | 34 | 1 | | 35 | 1 | | 36 | 24 | | 37 | 21 | | 38 | 11 | | 39 | 12 | | 40 | 19 | | 41 | 16 | | 42 | 14 | | 43 | 11 | | 44 | 11 | | 45 | 8 | | 46 | 9 | | 47 | 25 | | 48 | 21 | | 49 | 6 |
| |
| 57.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3923076923076923 | | totalSentences | 130 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 113 | | matches | | 0 | "Then the door of the" | | 1 | "Then he turned up his" | | 2 | "Somewhere deeper in, something rattled" | | 3 | "Instead she set her jaw" |
| | ratio | 0.035 | |
| 81.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 113 | | matches | | 0 | "Her left wrist itched under" | | 1 | "She thumbed the face out" | | 2 | "She knew him from the" | | 3 | "He paused on the step," | | 4 | "She cut him off." | | 5 | "She clicked the mic off" | | 6 | "She let Herrera get half" | | 7 | "He moved like a man" | | 8 | "He led her away from" | | 9 | "He adjusted the duffel, touched" | | 10 | "She had suspected the clique" | | 11 | "She suspected they were behind" | | 12 | "He stopped at a recessed" | | 13 | "He drew out a thong;" | | 14 | "He lifted the phone to" | | 15 | "he said, the Seville accent" | | 16 | "He listened, and his mouth" | | 17 | "He slid the phone away," | | 18 | "It swung inward with a" | | 19 | "She knew the whispers: a" |
| | ratio | 0.345 | |
| 75.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 113 | | matches | | 0 | "Rain fell the way it" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "The bar's green neon sign" | | 3 | "Her left wrist itched under" | | 4 | "She thumbed the face out" | | 5 | "Tomás Herrera stepped out into" | | 6 | "Quinn didn't move." | | 7 | "She knew him from the" | | 8 | "The rain plastered his short" | | 9 | "The puckered scar running along" | | 10 | "A Saint Christopher medallion glinted" | | 11 | "He paused on the step," | | 12 | "Quinn pressed a finger to" | | 13 | "A tinny voice crackled back." | | 14 | "She cut him off." | | 15 | "She clicked the mic off" | | 16 | "She let Herrera get half" | | 17 | "He moved like a man" | | 18 | "Quinn kept to the crown" | | 19 | "A bus hissed past, throwing" |
| | ratio | 0.77 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 113 | | matches | | 0 | "Now he stitched up the" | | 1 | "If she stayed, Herrera would" | | 2 | "If she followed, she stepped" |
| | ratio | 0.027 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 72 | | technicalSentenceCount | 8 | | matches | | 0 | "The bar's green neon sign buzzed overhead, a raven with wings spread, its light leaking into the gutter in trembling green pools." | | 1 | "Three hours she'd been here, her brown eyes tracking every soul who pushed through the door." | | 2 | "At five-foot-nine she carried herself with a military precision that had survived eighteen years of decorated service; rain dripped from her cropped salt-and-pe…" | | 3 | "She knew him from the file and from three weeks of watching the bar: Tomás Herrera, Tommy to the ones who trusted him, former NHS paramedic, born in Seville, lo…" | | 4 | "He moved like a man who knew the streets, shoulders loose, duffel bumping his hip." | | 5 | "Herrera was the closest she had come to the clique's heart: a medic who patched their wounds and kept their secrets." | | 6 | "She knew the whispers: a hidden supernatural black market that sold enchanted goods, banned alchemical substances, and information." | | 7 | "Down there, Herrera's medal would be glinting as he walked, patron of travelers guiding a man who had long ago lost his license for treating things that should …" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, the Seville accent thickening the vowels" | | 1 | "She'd, his lips blue, whispering," |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "She clicked (click)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.667 | | effectiveRatio | 0.4 | |