| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1074 | | 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) | |
| 62.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1074 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "pulsed" | | 1 | "measured" | | 2 | "glint" | | 3 | "mosaic" | | 4 | "vibrated" | | 5 | "scanning" | | 6 | "methodical" | | 7 | "stomach" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1066 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1054 | | uniqueNames | 17 | | maxNameDensity | 1.04 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 11 | | Quinn | 1 | | Soho | 2 | | Berwick | 2 | | Street | 2 | | Tomás | 1 | | Herrera | 8 | | Saint | 2 | | Christopher | 2 | | Limehouse | 1 | | Colin | 1 | | Morris | 2 | | Wardour | 2 | | Raven | 2 | | Nest | 2 | | London | 1 | | Underground | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Colin" | | 7 | "Morris" | | 8 | "Raven" |
| | places | | 0 | "Soho" | | 1 | "Berwick" | | 2 | "Street" | | 3 | "Wardour" | | 4 | "London" |
| | globalScore | 0.978 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1066 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 50.76 | | std | 34.72 | | cv | 0.684 | | sampleLengths | | 0 | 106 | | 1 | 127 | | 2 | 14 | | 3 | 18 | | 4 | 25 | | 5 | 32 | | 6 | 80 | | 7 | 5 | | 8 | 26 | | 9 | 67 | | 10 | 81 | | 11 | 83 | | 12 | 45 | | 13 | 75 | | 14 | 72 | | 15 | 72 | | 16 | 72 | | 17 | 22 | | 18 | 33 | | 19 | 8 | | 20 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 167 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 3 | | flaggedSentences | 10 | | totalSentences | 101 | | ratio | 0.099 | | matches | | 0 | "Harlow could make out the Saint Christopher medallion—a tarnished silver oval that bobbed with every shift of his shoulders." | | 1 | "She knew; she’d watched him for three weeks." | | 2 | "The NHS had fired him three years back; now he stitched up gangsters or worse." | | 3 | "His hand flew to the medallion—an instinctive clutch, not flight." | | 4 | "She focused on that medallion—a glint in the dark—and closed." | | 5 | "Through its front window, faces turned—shapes caught in candlelight and cigarette smoke." | | 6 | "A black cab’s horn screamed; its tires slid on the wet surface." | | 7 | "Harlow had heard stories—junior officers swapping tales of a black market that moved every full moon, an impossible bazaar beneath the abandoned platforms." | | 8 | "It had no handle, no keyhole—only a horizontal slot at waist height, and around its edges, a filigree of symbols that hurt to see." | | 9 | "It was warm—warmer than her own body." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1067 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.014058106841611996 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004686035613870665 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 10.55 | | std | 6.88 | | cv | 0.652 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 21 | | 3 | 16 | | 4 | 19 | | 5 | 5 | | 6 | 8 | | 7 | 26 | | 8 | 4 | | 9 | 9 | | 10 | 5 | | 11 | 28 | | 12 | 15 | | 13 | 11 | | 14 | 2 | | 15 | 2 | | 16 | 25 | | 17 | 5 | | 18 | 1 | | 19 | 8 | | 20 | 14 | | 21 | 4 | | 22 | 3 | | 23 | 9 | | 24 | 10 | | 25 | 3 | | 26 | 8 | | 27 | 4 | | 28 | 14 | | 29 | 4 | | 30 | 2 | | 31 | 9 | | 32 | 13 | | 33 | 15 | | 34 | 12 | | 35 | 18 | | 36 | 13 | | 37 | 5 | | 38 | 3 | | 39 | 13 | | 40 | 10 | | 41 | 5 | | 42 | 19 | | 43 | 12 | | 44 | 10 | | 45 | 21 | | 46 | 6 | | 47 | 12 | | 48 | 16 | | 49 | 15 |
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| 59.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.37623762376237624 | | totalSentences | 101 | | uniqueOpeners | 38 | |
| 36.23% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 92 | | matches | | | ratio | 0.011 | |
| 63.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 92 | | matches | | 0 | "He never took it off." | | 1 | "She knew; she’d watched him" | | 2 | "Her own left wrist bore" | | 3 | "She checked it: 11:48 p.m." | | 4 | "She had been on him" | | 5 | "She shook the thought off." | | 6 | "She stepped from the alley" | | 7 | "Her voice cut through the" | | 8 | "He looked up." | | 9 | "His hand flew to the" | | 10 | "She couldn’t lose him." | | 11 | "He vaulted a bin, jinked" | | 12 | "She didn’t feel the burn" | | 13 | "She felt only the distance" | | 14 | "He didn’t acknowledge." | | 15 | "His feet found a rhythm," | | 16 | "She focused on that medallion—a" | | 17 | "They burst onto Wardour Street." | | 18 | "He made the far pavement" | | 19 | "She had dismissed them as" |
| | ratio | 0.391 | |
| 52.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 92 | | matches | | 0 | "Detective Harlow Quinn pressed herself" | | 1 | "The rain came down in" | | 2 | "The green cross of a" | | 3 | "Harlow could make out the" | | 4 | "He never took it off." | | 5 | "She knew; she’d watched him" | | 6 | "The scar on his left" | | 7 | "Harlow flexed her fingers." | | 8 | "Her own left wrist bore" | | 9 | "She checked it: 11:48 p.m." | | 10 | "She had been on him" | | 11 | "The NHS had fired him" | | 12 | "The same impossible wrongness she’d" | | 13 | "She shook the thought off." | | 14 | "She stepped from the alley" | | 15 | "Her voice cut through the" | | 16 | "He looked up." | | 17 | "His hand flew to the" | | 18 | "Harlow launched after him, boots" | | 19 | "Comms were unreliable in this" |
| | ratio | 0.815 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 92 | | matches | | 0 | "Even through the distance, the" | | 1 | "By the time she was" | | 2 | "If she waited for backup," |
| | ratio | 0.033 | |
| 66.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 5 | | matches | | 0 | "Harlow could make out the Saint Christopher medallion—a tarnished silver oval that bobbed with every shift of his shoulders." | | 1 | "The same impossible wrongness she’d felt three years ago, the night her partner, Colin Morris, took a bullet in a basement that shouldn’t have existed." | | 2 | "Harlow had heard stories—junior officers swapping tales of a black market that moved every full moon, an impossible bazaar beneath the abandoned platforms." | | 3 | "The rain stopped at the third step, replaced by a cold, mineral stillness that smelled of wet rock and old iron." | | 4 | "Stay in the world she understood, or follow a suspect through a doorway that led to something unexplainable." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |