| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.542 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1184 | | totalAiIsmAdverbs | 2 | | 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) | |
| 78.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1184 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "glistening" | | 1 | "looms" | | 2 | "familiar" | | 3 | "echoes" | | 4 | "silence" |
| |
| 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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 18 | | markdownWords | 24 | | totalWords | 1160 | | ratio | 0.021 | | matches | | 0 | "Sorry" | | 1 | "“This way.”" | | 2 | "old" | | 3 | "Eliot" | | 4 | "Prove him wrong" | | 5 | "Veil Market" | | 6 | "evolutions" | | 7 | "chime" | | 8 | "hear" | | 9 | "purification" | | 10 | "really" | | 11 | "watch" | | 12 | "familiar" | | 13 | "sandalwood" | | 14 | "walk" | | 15 | "so" | | 16 | "Tomás" | | 17 | "Who is he?" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 988 | | uniqueNames | 13 | | maxNameDensity | 1.21 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | Brewer | 1 | | Street | 1 | | Chinese | 1 | | Tube | 1 | | Camden | 1 | | Morris | 3 | | Rain | 3 | | Seville | 1 | | Tomás | 4 | | Eliot | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Eliot" |
| | places | | 0 | "Soho" | | 1 | "Brewer" | | 2 | "Street" | | 3 | "Seville" |
| | globalScore | 0.893 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | 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 | 1160 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 26.36 | | std | 23.67 | | cv | 0.898 | | sampleLengths | | 0 | 57 | | 1 | 74 | | 2 | 82 | | 3 | 30 | | 4 | 44 | | 5 | 11 | | 6 | 61 | | 7 | 77 | | 8 | 12 | | 9 | 20 | | 10 | 60 | | 11 | 6 | | 12 | 65 | | 13 | 43 | | 14 | 15 | | 15 | 48 | | 16 | 34 | | 17 | 25 | | 18 | 22 | | 19 | 8 | | 20 | 25 | | 21 | 11 | | 22 | 7 | | 23 | 14 | | 24 | 44 | | 25 | 3 | | 26 | 2 | | 27 | 50 | | 28 | 50 | | 29 | 6 | | 30 | 6 | | 31 | 1 | | 32 | 2 | | 33 | 48 | | 34 | 10 | | 35 | 6 | | 36 | 15 | | 37 | 12 | | 38 | 4 | | 39 | 3 | | 40 | 6 | | 41 | 34 | | 42 | 4 | | 43 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 111 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 23 | | semicolonCount | 1 | | flaggedSentences | 15 | | totalSentences | 121 | | ratio | 0.124 | | matches | | 0 | "The suspect—hood low, coat dragging—cuts left onto Brewer Street, but she stays right, shoulder-checking a bystander who yelps and clutches a takeaway coffee." | | 1 | "A voice—high, panicked—whispers, *“This way.”* She follows, squeezing between metal and rust, and stumbles into a broader gap, the air suddenly sharper, colder." | | 2 | "It’s a sketch of a Tube entrance, Camden, annotated with a symbol—three interlocking circles—that matches the scratch-marks on Morris’s file." | | 3 | "The woman’s eyes are milky—blind?—but her smile is too wide." | | 4 | "A bone—ivory, ridged—falls from the rungs." | | 5 | "Steam blurs the walls, which are slick with moss and something glistening—mold?" | | 6 | "slime?—and above, a faint hum vibrates in her bones." | | 7 | "His coat is gone; he wears a vest covered in pouches, each bulging with odds and ends." | | 8 | "A woman with a tail—scaly, purple—sells fruit that hums." | | 9 | "A figure moves between the stalls—tall, cloaked, their face obscured by shadow." | | 10 | "The cloaked figure turns, and for a split second, Quinn sees a flash of brown eyes—warm, *familiar*—before they melt into the dark." | | 11 | "The cloaked figure is back, closer now—she can smell them: iron, rain, *sandalwood*—and their hand moves toward a pocket, where a knife glints." | | 12 | "The cloaked figure is ten paces ahead, but they don’t run—they *walk*, as if the tunnels are their own." | | 13 | "For a moment, their eyes lock—warm brown, *so* warm—and something shifts, a memory flickers: a clinic in Seville, a man with a scar on his forearm, a medallion around his neck." | | 14 | "The cloaks are gone—only a single figure remains: a woman, her face a mask of bone, eyes black pits." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1013 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.012833168805528134 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003948667324777887 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 9.58 | | std | 7.09 | | cv | 0.74 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 23 | | 3 | 1 | | 4 | 2 | | 5 | 7 | | 6 | 23 | | 7 | 21 | | 8 | 23 | | 9 | 4 | | 10 | 9 | | 11 | 13 | | 12 | 13 | | 13 | 20 | | 14 | 6 | | 15 | 1 | | 16 | 2 | | 17 | 14 | | 18 | 4 | | 19 | 19 | | 20 | 7 | | 21 | 3 | | 22 | 10 | | 23 | 9 | | 24 | 22 | | 25 | 5 | | 26 | 6 | | 27 | 2 | | 28 | 16 | | 29 | 3 | | 30 | 7 | | 31 | 10 | | 32 | 6 | | 33 | 15 | | 34 | 2 | | 35 | 6 | | 36 | 10 | | 37 | 12 | | 38 | 9 | | 39 | 16 | | 40 | 7 | | 41 | 17 | | 42 | 4 | | 43 | 8 | | 44 | 8 | | 45 | 5 | | 46 | 7 | | 47 | 7 | | 48 | 27 | | 49 | 21 |
| |
| 59.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.39669421487603307 | | totalSentences | 121 | | uniqueOpeners | 48 | |
| 67.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 99 | | matches | | 0 | "Just for a second." | | 1 | "Somewhere, a door closes." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 99 | | matches | | 0 | "She kneels, brushes rain from" | | 1 | "It’s a sketch of a" | | 2 | "Her fingers curl into a" | | 3 | "She spins, hand on her" | | 4 | "she says, tilting her head" | | 5 | "She nods toward a grate" | | 6 | "She steps onto the grate," | | 7 | "It shifts, hinges groaning, and" | | 8 | "She picks it up, runs" | | 9 | "She climbs, her heart thudding," | | 10 | "His coat is gone; he" | | 11 | "he grins, turning" | | 12 | "she calls, closing the distance" | | 13 | "Her boots clink on metal." | | 14 | "He pauses, key halfway in" | | 15 | "He unlocks the door, pushes" | | 16 | "He plucks a sprig of" | | 17 | "He tosses the herb into" | | 18 | "They pause, and Quinn sees" | | 19 | "He nods toward the door" |
| | ratio | 0.232 | |
| 40.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 99 | | matches | | 0 | "Detective Harlow Quinn’s breath comes" | | 1 | "The suspect—hood low, coat dragging—cuts" | | 2 | "The alley dead-ends at a" | | 3 | "Quinn lunges, sliding on a" | | 4 | "A voice—high, panicked—whispers, She follows," | | 5 | "The shadow is gone." | | 6 | "She kneels, brushes rain from" | | 7 | "A map scrawled on the" | | 8 | "It’s a sketch of a" | | 9 | "Her fingers curl into a" | | 10 | "The one who laughed when" | | 11 | "A creak behind her." | | 12 | "She spins, hand on her" | | 13 | "the woman asks, her voice" | | 14 | "Quinn stays still." | | 15 | "The woman’s eyes are milky—blind?—but" | | 16 | "she says, tilting her head" | | 17 | "She nods toward a grate" | | 18 | "The woman melts into the" | | 19 | "She steps onto the grate," |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "The suspect—hood low, coat dragging—cuts left onto Brewer Street, but she stays right, shoulder-checking a bystander who yelps and clutches a takeaway coffee." | | 1 | "It’s a sketch of a Tube entrance, Camden, annotated with a symbol—three interlocking circles—that matches the scratch-marks on Morris’s file." | | 2 | "Stalls line the tunnel, their shelves bowing under jars of glowing liquid, sacks of fine powder, and tools with runes carved into their handles." |
| |
| 48.08% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 2 | | matches | | 0 | "She nods, its metal warm, emitting a faint blue glow" | | 1 | "Tomás says, his voice tight" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0 | | effectiveRatio | 0 | |