| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1086 | | totalAiIsmAdverbs | 1 | | 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) | |
| 67.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1086 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "measured" | | 1 | "gloom" | | 2 | "echoed" | | 3 | "flickered" | | 4 | "depths" | | 5 | "comforting" | | 6 | "mosaic" |
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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 | 95 | | matches | (empty) | |
| 82.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1086 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.23% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1045 | | uniqueNames | 27 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Neal | 2 | | Street | 3 | | Soho | 1 | | Harlow | 1 | | Quinn | 15 | | Raven | 2 | | Nest | 2 | | Covent | 1 | | Garden | 1 | | Long | 1 | | Acre | 1 | | Yard | 1 | | Thai | 1 | | Monmouth | 1 | | Tottenham | 1 | | Court | 1 | | Bloomsbury | 1 | | Road | 1 | | Tube | 1 | | Veil | 1 | | Market | 2 | | London | 1 | | Morris | 1 | | Glock | 1 | | Metropolitan | 1 | | Police | 1 | | Water | 3 |
| | persons | | 0 | "Neal" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Thai" | | 6 | "Court" | | 7 | "Market" | | 8 | "Morris" | | 9 | "Police" | | 10 | "Water" |
| | places | | 0 | "Street" | | 1 | "Soho" | | 2 | "Covent" | | 3 | "Garden" | | 4 | "Monmouth" | | 5 | "Tottenham" | | 6 | "Road" | | 7 | "London" |
| | globalScore | 0.782 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1086 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 51.71 | | std | 27.56 | | cv | 0.533 | | sampleLengths | | 0 | 84 | | 1 | 72 | | 2 | 3 | | 3 | 76 | | 4 | 70 | | 5 | 4 | | 6 | 57 | | 7 | 69 | | 8 | 10 | | 9 | 19 | | 10 | 68 | | 11 | 64 | | 12 | 80 | | 13 | 62 | | 14 | 15 | | 15 | 67 | | 16 | 74 | | 17 | 70 | | 18 | 40 | | 19 | 12 | | 20 | 70 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 179 | | matches | | |
| 27.42% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 4 | | flaggedSentences | 4 | | totalSentences | 99 | | ratio | 0.04 | | matches | | 0 | "Water streamed from her closely cropped salt-and-pepper hair into her eyes; she blinksed once, hard, and kept the pursuit." | | 1 | "A barrow of wilted flowers overturned; petals plastered themselves to Quinn's coat like wet confetti." | | 2 | "One cab shaved the kerb beside Quinn's left boot; the wing mirror clipped her sleeve." | | 3 | "Quinn hit it two heartbeats later, shoulder first; the latch sprung under military force." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1054 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same half-explained stains" |
| | adverbCount | 32 | | adverbRatio | 0.030360531309297913 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007590132827324478 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.97 | | std | 6.5 | | cv | 0.592 | | sampleLengths | | 0 | 19 | | 1 | 28 | | 2 | 19 | | 3 | 18 | | 4 | 16 | | 5 | 19 | | 6 | 9 | | 7 | 9 | | 8 | 19 | | 9 | 3 | | 10 | 6 | | 11 | 4 | | 12 | 22 | | 13 | 8 | | 14 | 14 | | 15 | 15 | | 16 | 7 | | 17 | 18 | | 18 | 3 | | 19 | 15 | | 20 | 14 | | 21 | 10 | | 22 | 10 | | 23 | 4 | | 24 | 4 | | 25 | 15 | | 26 | 6 | | 27 | 14 | | 28 | 18 | | 29 | 8 | | 30 | 15 | | 31 | 6 | | 32 | 10 | | 33 | 12 | | 34 | 6 | | 35 | 7 | | 36 | 5 | | 37 | 8 | | 38 | 2 | | 39 | 19 | | 40 | 9 | | 41 | 8 | | 42 | 20 | | 43 | 9 | | 44 | 2 | | 45 | 9 | | 46 | 11 | | 47 | 13 | | 48 | 4 | | 49 | 12 |
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| 67.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.41414141414141414 | | totalSentences | 99 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 91 | | matches | | 0 | "Instead the hood twisted just" | | 1 | "Just the motion of flight." | | 2 | "Only rain answered before the" | | 3 | "Instantly the lighting thinned." | | 4 | "Only the image of DS" |
| | ratio | 0.055 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 91 | | matches | | 0 | "She drove forward, closing the" | | 1 | "Her sharp jaw set and" | | 2 | "She vaulted the mess without" | | 3 | "She spun free, never losing" | | 4 | "She stumbled through, regainoing balance" | | 5 | "She drew her radio but" | | 6 | "She released the transmit key" | | 7 | "She rolled across its bonnet," | | 8 | "Her cropped hair plastered dark" | | 9 | "She closed again to fifteen" | | 10 | "She gripped the rail, cold" | | 11 | "Her boots found the first" | | 12 | "She descended three steps further." | | 13 | "Her leather watch face caught" | | 14 | "She had no token." | | 15 | "She pressed her shoulder to" | | 16 | "Her radio clicked once more," | | 17 | "Her right hand rested against" |
| | ratio | 0.198 | |
| 53.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 91 | | matches | | 0 | "Detective Harlow Quinn kicked off" | | 1 | "Water streamed from her closely" | | 2 | "The leather strap of her" | | 3 | "The figure cut left into" | | 4 | "Quinn followed on the same" | | 5 | "A dustbin lid clattered away" | | 6 | "She drove forward, closing the" | | 7 | "Her sharp jaw set and" | | 8 | "The words cut across the" | | 9 | "The figure never slowed." | | 10 | "Tourists in plastic ponchos scattered" | | 11 | "A barrow of wilted flowers" | | 12 | "She vaulted the mess without" | | 13 | "The suspect's path wove past" | | 14 | "Taxi horns blared." | | 15 | "She spun free, never losing" | | 16 | "The tiny courtyard stank of" | | 17 | "A cook in a stained" | | 18 | "Quinn shouldered past him." | | 19 | "The figure ripped open a" |
| | ratio | 0.813 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 69.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn followed on the same path, boots finding purchase on the rain-slicked stone that should have betrayed any civilian." | | 1 | "Her sharp jaw set and her breath came steady, measured, as if drilled on parade ground rather than street." | | 2 | "The suspect hit the road surface of Tottenham Court, weaving through night traffic like a man who owned the gaps." | | 3 | "Quinn's boots punched through ankle-deep water that tried to suck her down." | | 4 | "Beyond lay The Veil Market, an abandoned station deeper still, bones and stalls and people who carried bone tokens as keys." |
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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 | |