| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1295 | | 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) | |
| 76.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1295 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "scanned" | | 1 | "etched" | | 2 | "weight" | | 3 | "flicked" | | 4 | "tracing" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1293 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1293 | | uniqueNames | 26 | | maxNameDensity | 1.08 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Jax" | | discoveredNames | | Camden | 2 | | Tube | 2 | | Quinn | 1 | | Mills | 7 | | Hendon | 1 | | London | 2 | | Rainer | 1 | | Veil | 3 | | Market | 1 | | Jax | 10 | | Tottenham | 1 | | Compass | 1 | | Morris | 4 | | Kowalski | 1 | | Oxford | 1 | | Ancient | 1 | | History | 1 | | British | 1 | | Museum | 1 | | Harlow | 14 | | Met | 1 | | Eva | 10 | | Shade | 3 | | Green | 1 | | Developments | 1 | | You | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Mills" | | 2 | "Rainer" | | 3 | "Market" | | 4 | "Jax" | | 5 | "Compass" | | 6 | "Morris" | | 7 | "Kowalski" | | 8 | "History" | | 9 | "Museum" | | 10 | "Harlow" | | 11 | "Eva" | | 12 | "You" |
| | places | | 0 | "London" | | 1 | "Veil" | | 2 | "Ancient" | | 3 | "British" | | 4 | "Shade" |
| | globalScore | 0.959 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | 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 | 1293 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 64.65 | | std | 49.19 | | cv | 0.761 | | sampleLengths | | 0 | 108 | | 1 | 22 | | 2 | 111 | | 3 | 61 | | 4 | 55 | | 5 | 50 | | 6 | 173 | | 7 | 27 | | 8 | 4 | | 9 | 110 | | 10 | 10 | | 11 | 26 | | 12 | 46 | | 13 | 31 | | 14 | 149 | | 15 | 113 | | 16 | 25 | | 17 | 42 | | 18 | 11 | | 19 | 119 |
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| 82.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 106 | | matches | | 0 | "been assigned" | | 1 | "was supposed" | | 2 | "been found" | | 3 | "was convinced" | | 4 | "was supposed" | | 5 | "was tied" | | 6 | "tied" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 242 | | matches | | 0 | "was trying" | | 1 | "weren’t looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 106 | | ratio | 0.009 | | matches | | 0 | "Harlow’s voice was flat, the memory of Morris’s body—those same seared stab wounds splitting his uniform—flashing through her mind." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1303 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.013814274750575594 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023023791250959325 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 12.2 | | std | 8.65 | | cv | 0.709 | | sampleLengths | | 0 | 26 | | 1 | 18 | | 2 | 18 | | 3 | 26 | | 4 | 20 | | 5 | 2 | | 6 | 2 | | 7 | 3 | | 8 | 15 | | 9 | 30 | | 10 | 5 | | 11 | 25 | | 12 | 31 | | 13 | 5 | | 14 | 10 | | 15 | 5 | | 16 | 18 | | 17 | 10 | | 18 | 23 | | 19 | 10 | | 20 | 16 | | 21 | 10 | | 22 | 6 | | 23 | 10 | | 24 | 10 | | 25 | 3 | | 26 | 12 | | 27 | 27 | | 28 | 3 | | 29 | 5 | | 30 | 3 | | 31 | 8 | | 32 | 4 | | 33 | 15 | | 34 | 20 | | 35 | 16 | | 36 | 22 | | 37 | 21 | | 38 | 4 | | 39 | 19 | | 40 | 44 | | 41 | 14 | | 42 | 13 | | 43 | 2 | | 44 | 2 | | 45 | 12 | | 46 | 16 | | 47 | 20 | | 48 | 2 | | 49 | 5 |
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| 61.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4056603773584906 | | totalSentences | 106 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 98 | | matches | (empty) | | ratio | 0 | |
| 85.31% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 98 | | matches | | 0 | "Her salt-and-pepper crop clung to" | | 1 | "He’d been assigned to her" | | 2 | "She knew the dead man." | | 3 | "He was four hours early." | | 4 | "They’ve been muscling in on" | | 5 | "He leaned forward, eyes wide." | | 6 | "I missed that." | | 7 | "You looked for what you" | | 8 | "She crossed the 20 feet" | | 9 | "She flipped it over." | | 10 | "I didn’t kill him." | | 11 | "I was supposed to meet" | | 12 | "My bone token expired." | | 13 | "I was here to buy" | | 14 | "We’re taking you in for" | | 15 | "They’re from a Shade’s claws." | | 16 | "They sear flesh to stop" | | 17 | "I got a tip last" | | 18 | "They wanted to flip it" | | 19 | "She turned back to Jax’s" |
| | ratio | 0.337 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 98 | | matches | | 0 | "The rain dripped through the" | | 1 | "Harlow Quinn ducked under the" | | 2 | "Her salt-and-pepper crop clung to" | | 3 | "DC Mills, fresh out of" | | 4 | "He’d been assigned to her" | | 5 | "The same line the department" | | 6 | "Harlow’s left wrist brushed a" | | 7 | "She knew the dead man." | | 8 | "Jax Rainer, 34, two priors" | | 9 | "Harlow had spent 18 months" | | 10 | "Tonight was the full moon." | | 11 | "Jax was supposed to move" | | 12 | "He was four hours early." | | 13 | "Mills kicked a loose chunk" | | 14 | "Looks like a Tottenham crew" | | 15 | "They’ve been muscling in on" | | 16 | "Harlow nudged Jax’s left boot" | | 17 | "The leather split along the" | | 18 | "Mills’s flashlight stuttered." | | 19 | "He leaned forward, eyes wide." |
| | ratio | 0.816 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 98 | | matches | | 0 | "If the needle’s spinning, that" | | 1 | "Whoever killed Jax brought a" | | 2 | "If you steal the core," |
| | ratio | 0.031 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 6 | | matches | | 0 | "He’d been assigned to her six weeks prior, still programmed to spit the first convenient narrative that crossed his mind." | | 1 | "Jax Rainer, 34, two priors for fencing stolen museum artifacts, the man who’d guarded the entry to the Veil Market for the last two years." | | 2 | "Harlow had spent 18 months chasing the market, the hidden supernatural black market that shifted location every full moon, that only let people cross its thresh…" | | 3 | "Verdigris crawled up its casing, the face etched with looping sigils that made her eyes burn if she stared too long." | | 4 | "That was the Veil Compass, the item she’d read about in unredacted police reports from three years prior, the device that had been found 10 feet from her partne…" | | 5 | "She nodded at Mills, who moved to gather Eva’s scattered books, his hands shaking as he brushed glass off a 12th-century copy of *Liber Novus*." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |