| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "His gaze dropped briefly [briefly]" |
| | dialogueSentences | 12 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 86.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1477 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "gently" | | 2 | "sweetly" | | 3 | "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) | |
| 89.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1477 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "chilled" | | 1 | "mechanical" | | 2 | "scanning" |
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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 | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1501 | | ratio | 0 | | matches | (empty) | |
| 93.75% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 1 | | matches | | 0 | "Her phone showed no service — normal, underground, she told herself — but the clock on the screen read 00:00 and would n…" |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1460 | | uniqueNames | 20 | | maxNameDensity | 0.68 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 1 | | High | 2 | | Street | 3 | | Tomás | 2 | | Herrera | 9 | | Quinn | 10 | | Raven | 1 | | Nest | 2 | | Brewer | 1 | | Silas | 1 | | Seville | 1 | | Mornington | 1 | | Crescent | 1 | | Saint | 1 | | Christopher | 1 | | Barcelona | 2 | | Job | 1 | | Whitechapel | 1 | | Morris | 5 | | Three | 3 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Raven" | | 4 | "Silas" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Brewer" | | 4 | "Seville" | | 5 | "Mornington" | | 6 | "Crescent" | | 7 | "Barcelona" | | 8 | "Job" | | 9 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 1 | | matches | | 0 | "as if remembering a hand" |
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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 | 1501 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 41.69 | | std | 36.35 | | cv | 0.872 | | sampleLengths | | 0 | 33 | | 1 | 121 | | 2 | 9 | | 3 | 37 | | 4 | 12 | | 5 | 10 | | 6 | 102 | | 7 | 80 | | 8 | 50 | | 9 | 101 | | 10 | 7 | | 11 | 63 | | 12 | 9 | | 13 | 16 | | 14 | 31 | | 15 | 1 | | 16 | 59 | | 17 | 34 | | 18 | 2 | | 19 | 15 | | 20 | 10 | | 21 | 138 | | 22 | 22 | | 23 | 101 | | 24 | 21 | | 25 | 28 | | 26 | 22 | | 27 | 79 | | 28 | 47 | | 29 | 59 | | 30 | 11 | | 31 | 77 | | 32 | 8 | | 33 | 8 | | 34 | 52 | | 35 | 26 |
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| 93.66% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 121 | | matches | | 0 | "been painted" | | 1 | "was waved" | | 2 | "were boarded" | | 3 | "were hung" | | 4 | "was disturbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 247 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 24 | | semicolonCount | 0 | | flaggedSentences | 17 | | totalSentences | 127 | | ratio | 0.134 | | matches | | 0 | "Three weeks of logging Silas's crowd in and out of that bar, and Herrera was the softest target of the lot — Tomás Herrera, twenty-nine, born in Seville, struck off the medical register for administering treatments nobody would put a name to in the paperwork." | | 1 | "Rain had flattened his dark curls to his forehead, and the little gold medallion at his throat jumped as he ran — Saint Christopher, patron of travellers." | | 2 | "Herrera vaulted a chain strung across an alley mouth, and as his sleeve rode up she caught the pale line of the scar along his left forearm — the one in his file photograph, the knife wound." | | 3 | "Above them, through a tear in the cloud, the moon slid out — a day shy of full — and slid back again." | | 4 | "Something fell from his fist and clicked across the threshold — small, white." | | 5 | "On the wall, a roundel had been painted over in black, the station's name erased, and Quinn — who had grown up on this city's geography the way other people grew up on scripture — could not have said what station this was, or that it had ever existed." | | 6 | "And light — lamplight, by the way it breathed." | | 7 | "Tall — Quinn was five-nine in her socks and used to looking level at the world, and he made her look up." | | 8 | "He looked at it the way she might look at a bus ticket in Barcelona — an object whose entire category had failed to arrive." | | 9 | "Her hand went into her pocket and found the knucklebone — still warm, absurdly warm — and she laid it in his palm before she'd fully decided to." | | 10 | "Quinn's trained eye did what it always did — catalogued, assessed — and then, item by item, began to fail." | | 11 | "Her phone showed no service — normal, underground, she told herself — but the clock on the screen read 00:00 and would not change." | | 12 | "She moved along the platform, scanning for a black bag, a gold medallion, olive skin, a scarred forearm — and stopped." | | 13 | "Whatever the clique was into — whatever Herrera was into — it had touched Morris." | | 14 | "He glanced back — she was sure he saw her, sure his face changed — and then he was through the chains and gone, the curtain swaying in his wake." | | 15 | "Return at dawn with a team — and find what?" | | 16 | "Checked the watch — 00:11, the second hand steady, one sane thing in a place that had misplaced its sanity." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1443 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.029106029106029108 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005544005544005544 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 11.82 | | std | 10.05 | | cv | 0.851 | | sampleLengths | | 0 | 33 | | 1 | 35 | | 2 | 9 | | 3 | 45 | | 4 | 3 | | 5 | 12 | | 6 | 17 | | 7 | 3 | | 8 | 6 | | 9 | 4 | | 10 | 27 | | 11 | 6 | | 12 | 8 | | 13 | 4 | | 14 | 10 | | 15 | 30 | | 16 | 10 | | 17 | 37 | | 18 | 5 | | 19 | 9 | | 20 | 2 | | 21 | 1 | | 22 | 8 | | 23 | 3 | | 24 | 23 | | 25 | 21 | | 26 | 3 | | 27 | 30 | | 28 | 3 | | 29 | 13 | | 30 | 13 | | 31 | 9 | | 32 | 6 | | 33 | 6 | | 34 | 10 | | 35 | 8 | | 36 | 49 | | 37 | 8 | | 38 | 4 | | 39 | 2 | | 40 | 11 | | 41 | 9 | | 42 | 7 | | 43 | 13 | | 44 | 22 | | 45 | 7 | | 46 | 21 | | 47 | 4 | | 48 | 5 | | 49 | 3 |
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| 78.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5039370078740157 | | totalSentences | 127 | | uniqueOpeners | 64 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 105 | | matches | | 0 | "Then he'd made her tail" | | 1 | "Then she was through, and" | | 2 | "Then, at the far end" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 105 | | matches | | 0 | "She'd watched him leave the" | | 1 | "She'd followed his cab north," | | 2 | "He looked back once." | | 3 | "He was about to need" | | 4 | "he called over his shoulder" | | 5 | "He cut left off the" | | 6 | "He ran like a man" | | 7 | "She pocketed it and went" | | 8 | "Her earpiece crackled once and" | | 9 | "His gloved hand was extended," | | 10 | "His eyes caught the lamplight" | | 11 | "She showed him her warrant" | | 12 | "He looked at it the" | | 13 | "Her hand went into her" | | 14 | "He turned it over." | | 15 | "His gaze dropped, briefly, to" | | 16 | "He smiled, and she wished" | | 17 | "It had been a platform" | | 18 | "She held on to that" | | 19 | "Her phone showed no service" |
| | ratio | 0.257 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 105 | | matches | | 0 | "The minicab had dropped him" | | 1 | "She'd watched him leave the" | | 2 | "The clique's patch-man." | | 3 | "She'd followed his cab north," | | 4 | "He looked back once." | | 5 | "Rain had flattened his dark" | | 6 | "He was about to need" | | 7 | "he called over his shoulder" | | 8 | "The please chilled her more" | | 9 | "He cut left off the" | | 10 | "Tarpaulins cracked like sails in" | | 11 | "Herrera vaulted a chain strung" | | 12 | "Confirmation, if she'd needed it." | | 13 | "He ran like a man" | | 14 | "That frightened her more than" | | 15 | "The alley narrowed." | | 16 | "Tonight it stood ajar, and" | | 17 | "Herrera ducked through." | | 18 | "Something fell from his fist" | | 19 | "Quinn's hand closed on it" |
| | ratio | 0.714 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 2 | | matches | | 0 | "On the wall, a roundel had been painted over in black, the station's name erased, and Quinn — who had grown up on this city's geography the way other people gre…" | | 1 | "A man selling single gloves, each one flexing gently, as if remembering a hand." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.2 | | effectiveRatio | 0.167 | |