| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1476 | | 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) | |
| 86.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1476 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "clandestine" | | 1 | "glinting" | | 2 | "warmth" | | 3 | "whisper" |
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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 | 45 | | matches | (empty) | |
| 47.62% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 45 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 53 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 101 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1476 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1012 | | uniqueNames | 21 | | maxNameDensity | 0.99 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Metropolitan | 1 | | Police | 1 | | Raven | 3 | | Nest | 4 | | Soho | 1 | | Jim | 4 | | Morris | 2 | | Paddington | 1 | | Station | 1 | | Oxford | 1 | | Street | 2 | | Camden | 2 | | High | 1 | | Harlow | 10 | | Veil | 1 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 4 | | Luka | 1 |
| | persons | | 0 | "Police" | | 1 | "Jim" | | 2 | "Morris" | | 3 | "Harlow" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Tomás" | | 8 | "Luka" |
| | places | | 0 | "Nest" | | 1 | "Soho" | | 2 | "Oxford" | | 3 | "Street" | | 4 | "Camden" | | 5 | "High" | | 6 | "Veil" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1476 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 86.82 | | std | 54.89 | | cv | 0.632 | | sampleLengths | | 0 | 141 | | 1 | 223 | | 2 | 106 | | 3 | 158 | | 4 | 56 | | 5 | 7 | | 6 | 116 | | 7 | 10 | | 8 | 50 | | 9 | 120 | | 10 | 51 | | 11 | 111 | | 12 | 38 | | 13 | 99 | | 14 | 85 | | 15 | 30 | | 16 | 75 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 170 | | matches | | 0 | "was suffering" | | 1 | "was calming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 881 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.0170261066969353 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004540295119182747 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 53 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 53 | | mean | 27.85 | | std | 18.68 | | cv | 0.671 | | sampleLengths | | 0 | 20 | | 1 | 36 | | 2 | 36 | | 3 | 23 | | 4 | 26 | | 5 | 19 | | 6 | 34 | | 7 | 30 | | 8 | 19 | | 9 | 33 | | 10 | 34 | | 11 | 41 | | 12 | 13 | | 13 | 22 | | 14 | 27 | | 15 | 5 | | 16 | 36 | | 17 | 10 | | 18 | 6 | | 19 | 66 | | 20 | 23 | | 21 | 24 | | 22 | 11 | | 23 | 5 | | 24 | 29 | | 25 | 26 | | 26 | 30 | | 27 | 7 | | 28 | 38 | | 29 | 18 | | 30 | 24 | | 31 | 36 | | 32 | 10 | | 33 | 21 | | 34 | 29 | | 35 | 47 | | 36 | 16 | | 37 | 57 | | 38 | 16 | | 39 | 35 | | 40 | 10 | | 41 | 101 | | 42 | 16 | | 43 | 22 | | 44 | 99 | | 45 | 21 | | 46 | 16 | | 47 | 26 | | 48 | 22 | | 49 | 30 |
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| 55.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.37735849056603776 | | totalSentences | 53 | | uniqueOpeners | 20 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 68.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 45 | | matches | | 0 | "Her boot skidded on a" | | 1 | "She’d spent 18 years on" | | 2 | "She’d picked the kid up" | | 3 | "She’d watched him slip out" | | 4 | "She’d called in sick that" | | 5 | "She didn’t fully understand it" | | 6 | "She couldn’t call for backup." | | 7 | "She was alone, and that" | | 8 | "She kept the kid in" | | 9 | "She’d seen those fragments before," | | 10 | "She’d planned for that, knew" | | 11 | "She spun, taser drawn before" | | 12 | "She’d flagged him as a" | | 13 | "She didn’t lower the weapon," | | 14 | "She was five foot nine," | | 15 | "Its jaws gaped open, dripping" | | 16 | "She fumbled for her service" |
| | ratio | 0.378 | |
| 4.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 45 | | matches | | 0 | "Her boot skidded on a" | | 1 | "Harlow caught herself on a" | | 2 | "She’d spent 18 years on" | | 3 | "The taste of copper coated" | | 4 | "She’d picked the kid up" | | 5 | "She’d watched him slip out" | | 6 | "She’d called in sick that" | | 7 | "Everyone at the station treated" | | 8 | "Harlow had spent every day" | | 9 | "She didn’t fully understand it" | | 10 | "The kid darted across Oxford" | | 11 | "Harlow followed, ignoring the cabbie’s" | | 12 | "She couldn’t call for backup." | | 13 | "The last time she’d tried" | | 14 | "She was alone, and that" | | 15 | "She kept the kid in" | | 16 | "Harlow ducked behind a skip," | | 17 | "She’d seen those fragments before," | | 18 | "The underground supernatural black market" | | 19 | "Tonight was the full moon." |
| | ratio | 0.911 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 9 | | matches | | 0 | "The taste of copper coated her tongue, and she pressed a hand to her side to stanch the stitch that threatened to fold her in half." | | 1 | "She’d watched him slip out of the hidden back exit, the swinging bookshelf that led to the Nest’s clandestine meeting room closing soft behind him, and she’d mo…" | | 2 | "Everyone at the station treated her like a broken toy, the detective who couldn’t let her dead partner go." | | 3 | "No one believed her when she said the clique was more than a simple drug ring, that the things she’d found in Morris’s notes, the carved bone fragments and the …" | | 4 | "Harlow followed, ignoring the cabbie’s yell of obscene abuse, her hand clamped around the strap of her shoulder bag that held her badge and her service pistol." | | 5 | "The underground supernatural black market that moved locations every full moon." | | 6 | "She’d flagged him as a clique associate six months prior, the man who provided off-the-books medical care for anyone tied to the Raven’s Nest." | | 7 | "The scar that ran along his left forearm, the one he’d earned in a knife attack a year prior, caught the light as he held his hands up, slow, like he was calmin…" | | 8 | "Its jaws gaped open, dripping black saliva that hissed when it hit the ground, and it lowered its head, locking its eyes on Harlow." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow took, the taser still raised" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |