| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1032 | | 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) | |
| 75.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1032 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "familiar" | | 1 | "warmth" | | 2 | "echoing" | | 3 | "crystal" | | 4 | "could feel" |
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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 | 1 | | narrationSentences | 83 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 1032 | | ratio | 0.006 | | matches | | 0 | "bus pass" | | 1 | "don't be stupid, Harl." |
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| 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 | 23 | | wordCount | 884 | | uniqueNames | 16 | | maxNameDensity | 0.45 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Old | 2 | | Compton | 1 | | Street | 2 | | Quinn | 4 | | Wardour | 1 | | Cambridge | 1 | | Circus | 1 | | Dean | 1 | | Scotland | 1 | | Morris | 3 | | Russell | 1 | | Square | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Russell" | | 3 | "Square" | | 4 | "Market" | | 5 | "Camden" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Cambridge" | | 4 | "Dean" | | 5 | "Scotland" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1032 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 24.57 | | std | 18.37 | | cv | 0.748 | | sampleLengths | | 0 | 16 | | 1 | 27 | | 2 | 40 | | 3 | 12 | | 4 | 46 | | 5 | 7 | | 6 | 43 | | 7 | 6 | | 8 | 24 | | 9 | 37 | | 10 | 42 | | 11 | 31 | | 12 | 20 | | 13 | 5 | | 14 | 14 | | 15 | 23 | | 16 | 8 | | 17 | 4 | | 18 | 49 | | 19 | 7 | | 20 | 6 | | 21 | 13 | | 22 | 14 | | 23 | 1 | | 24 | 43 | | 25 | 5 | | 26 | 47 | | 27 | 52 | | 28 | 30 | | 29 | 14 | | 30 | 12 | | 31 | 77 | | 32 | 17 | | 33 | 29 | | 34 | 5 | | 35 | 50 | | 36 | 41 | | 37 | 61 | | 38 | 25 | | 39 | 17 | | 40 | 6 | | 41 | 6 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "been closed" | | 1 | "been decked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 1 | | totalSentences | 93 | | ratio | 0.011 | | matches | | 0 | "Every stall sold something she couldn't name at a glance: jars of teeth that blinked; a whole wall of keys, each humming a different note; a woman selling perfume from crystal decanters while the bottles leaned toward her wrist like flowers to sun." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 887 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.030439684329199548 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005636978579481398 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 11.1 | | std | 9.83 | | cv | 0.886 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 10 | | 3 | 4 | | 4 | 3 | | 5 | 9 | | 6 | 4 | | 7 | 12 | | 8 | 12 | | 9 | 1 | | 10 | 11 | | 11 | 20 | | 12 | 4 | | 13 | 3 | | 14 | 19 | | 15 | 7 | | 16 | 17 | | 17 | 1 | | 18 | 18 | | 19 | 7 | | 20 | 6 | | 21 | 4 | | 22 | 20 | | 23 | 18 | | 24 | 5 | | 25 | 14 | | 26 | 7 | | 27 | 35 | | 28 | 4 | | 29 | 27 | | 30 | 2 | | 31 | 3 | | 32 | 2 | | 33 | 4 | | 34 | 9 | | 35 | 5 | | 36 | 3 | | 37 | 4 | | 38 | 5 | | 39 | 2 | | 40 | 23 | | 41 | 8 | | 42 | 4 | | 43 | 24 | | 44 | 25 | | 45 | 7 | | 46 | 6 | | 47 | 5 | | 48 | 3 | | 49 | 5 |
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| 91.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.6021505376344086 | | totalSentences | 93 | | uniqueOpeners | 56 | |
| 93.90% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 71 | | matches | | 0 | "Then he dropped through the" | | 1 | "Then the boy bolted again," |
| | ratio | 0.028 | |
| 90.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 71 | | matches | | 0 | "She hit the stall's counter," | | 1 | "She vaulted after him." | | 2 | "He was fast." | | 3 | "He'd cut right at Wardour," | | 4 | "His coat snapped wet in" | | 5 | "She tasted grit." | | 6 | "He stopped, actually stopped, ten" | | 7 | "She raised it." | | 8 | "She put the radio away." | | 9 | "She touched the pavement." | | 10 | "He said it like *bus" | | 11 | "He produced from his coat" | | 12 | "She looked at the pavement." | | 13 | "He smiled with too many" | | 14 | "His smile narrowed." | | 15 | "He wheeled the bicycle along" | | 16 | "She followed, gun heavy under" | | 17 | "Her guide parked the bike" | | 18 | "She descended past him." | | 19 | "her guide said behind her" |
| | ratio | 0.324 | |
| 93.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 71 | | matches | | 0 | "The suspect vaulted the burger" | | 1 | "She hit the stall's counter," | | 2 | "The vendor shouted something about" | | 3 | "She vaulted after him." | | 4 | "He was fast." | | 5 | "He'd cut right at Wardour," | | 6 | "Doubling was for people without" | | 7 | "His coat snapped wet in" | | 8 | "Rain stung her face." | | 9 | "She tasted grit." | | 10 | "The leather strap of her" | | 11 | "He stopped, actually stopped, ten" | | 12 | "Amber, flat, like a dog's" | | 13 | "Quinn's legs carried her three" | | 14 | "The pavement looked like pavement." | | 15 | "Puddles, chewing gum fossils, a" | | 16 | "The pavement rippled again." | | 17 | "A stain of darkness spread" | | 18 | "She raised it." | | 19 | "She put the radio away." |
| | ratio | 0.732 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 34.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 5 | | matches | | 0 | "A stain of darkness spread where he'd gone, and from somewhere below came music, tinny and wrong, and voices in a language that made her molars ache." | | 1 | "Her guide parked the bike against a railing, tapped the bone twice against the tiled wall, and the tiles swung inward like a door that had been waiting." | | 2 | "Every stall sold something she couldn't name at a glance: jars of teeth that blinked; a whole wall of keys, each humming a different note; a woman selling perfu…" | | 3 | "Shoulder to shoulder with things that smelled of wet earth and woodsmoke, that counted her change in coins stamped with moons, that looked at her copper-soaked …" | | 4 | "The vendor there was tall, wearing an apron stained like a butcher's, and when the boy grabbed his sleeve, the vendor's head turned toward Quinn with the unhurr…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.25 | | effectiveRatio | 0.111 | |