| 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 | 1391 | | 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) | |
| 56.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "traced" | | 2 | "flickered" | | 3 | "whisper" | | 4 | "silence" | | 5 | "framework" | | 6 | "mechanical" | | 7 | "vibrated" | | 8 | "echoing" | | 9 | "resolved" | | 10 | "stomach" |
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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 | 122 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 122 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | 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 | 1405 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1405 | | uniqueNames | 16 | | maxNameDensity | 0.57 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Quinn | 8 | | Herrera | 6 | | Greek | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | London | 2 | | Raven | 1 | | Nest | 1 | | Tube | 2 | | Morris | 5 | | Veil | 1 | | Market | 1 | | Tomás | 2 | | Former | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Tomás" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Camden" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 2 | | matches | | 0 | "quite fear" | | 1 | "not quite anything else" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1405 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 43.91 | | std | 31.74 | | cv | 0.723 | | sampleLengths | | 0 | 7 | | 1 | 92 | | 2 | 56 | | 3 | 76 | | 4 | 66 | | 5 | 123 | | 6 | 7 | | 7 | 70 | | 8 | 3 | | 9 | 62 | | 10 | 4 | | 11 | 66 | | 12 | 5 | | 13 | 74 | | 14 | 34 | | 15 | 48 | | 16 | 7 | | 17 | 73 | | 18 | 44 | | 19 | 17 | | 20 | 5 | | 21 | 3 | | 22 | 70 | | 23 | 52 | | 24 | 30 | | 25 | 41 | | 26 | 57 | | 27 | 92 | | 28 | 28 | | 29 | 61 | | 30 | 29 | | 31 | 3 |
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| 93.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 122 | | matches | | 0 | "been terrified" | | 1 | "been terrified" | | 2 | "was gone" | | 3 | "been terrified" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 11 | | totalVerbs | 242 | | matches | | 0 | "was trying" | | 1 | "wasn't doing" | | 2 | "was doing" | | 3 | "was heading" | | 4 | "was pulling" | | 5 | "was running" | | 6 | "was leading" | | 7 | "was pulling" | | 8 | "was standing" | | 9 | "was doing" | | 10 | "was trying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 122 | | ratio | 0.098 | | matches | | 0 | "The body had a way of sorting its priorities, and right now every nerve, every synapse, every drop of adrenaline was pointed forward — at the man running from her." | | 1 | "She'd spent eighteen years learning the geography of London's underworld — every bolt-hole, every safe house, every unmarked door that led somewhere it shouldn't." | | 2 | "But this direction, this trajectory — it was pulling at something in her memory." | | 3 | "Her knee protested — old injury, never properly healed — but she pushed through it, lengthening her stride." | | 4 | "She could hear the city above and behind her — sirens, distant, the groan of a night bus on a main road, the relentless percussion of rain on metal and stone." | | 5 | "The screen reflected her own face back at her — rain-streaked, sharp-jawed, eyes dark with something she refused to name." | | 6 | "The concrete walls closed in around her, slick with moisture and something else — something that glistened faintly with a luminescence that had no business existing in an abandoned Tube station." | | 7 | "And then she heard it — the murmur of a crowd." | | 8 | "Everything about her was steady, because that was what military precision bought you — the ability to hold yourself together when the world stopped making sense." | | 9 | "The murmur grew louder, resolved into individual voices — haggling, calling out prices, arguing in languages she didn't recognize." | | 10 | "Stalls lined the old platforms of the abandoned station, their awnings made of materials she couldn't identify — some translucent, some shifting with color, some that appeared to be breathing." | | 11 | "She caught a glimpse of the medallion — that flash of gold against his chest — as he slipped between a vendor selling glass eyes that blinked and a woman draped in what appeared to be living shadow." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1403 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.029223093371347115 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004989308624376337 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 11.52 | | std | 9.43 | | cv | 0.818 | | sampleLengths | | 0 | 7 | | 1 | 25 | | 2 | 10 | | 3 | 20 | | 4 | 7 | | 5 | 30 | | 6 | 2 | | 7 | 1 | | 8 | 2 | | 9 | 3 | | 10 | 15 | | 11 | 7 | | 12 | 16 | | 13 | 10 | | 14 | 23 | | 15 | 20 | | 16 | 25 | | 17 | 8 | | 18 | 24 | | 19 | 15 | | 20 | 4 | | 21 | 3 | | 22 | 5 | | 23 | 13 | | 24 | 2 | | 25 | 5 | | 26 | 24 | | 27 | 14 | | 28 | 2 | | 29 | 29 | | 30 | 9 | | 31 | 22 | | 32 | 18 | | 33 | 7 | | 34 | 32 | | 35 | 25 | | 36 | 13 | | 37 | 3 | | 38 | 11 | | 39 | 3 | | 40 | 5 | | 41 | 5 | | 42 | 22 | | 43 | 10 | | 44 | 6 | | 45 | 4 | | 46 | 14 | | 47 | 18 | | 48 | 5 | | 49 | 29 |
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| 54.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4016393442622951 | | totalSentences | 122 | | uniqueOpeners | 49 | |
| 30.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 109 | | matches | | | ratio | 0.009 | |
| 80.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 109 | | matches | | 0 | "She never felt anything during" | | 1 | "He was fast, she'd give" | | 2 | "He ducked left at the" | | 3 | "She almost laughed." | | 4 | "He wasn't doing much traveling." | | 5 | "He was doing a lot" | | 6 | "She knew what lay south." | | 7 | "She'd spent eighteen years learning" | | 8 | "He'd been terrified of the" | | 9 | "He'd mentioned a bone token," | | 10 | "His left arm caught the" | | 11 | "He stumbled on the landing" | | 12 | "Their eyes met." | | 13 | "His were warm brown, wide" | | 14 | "It was recognition." | | 15 | "He knew she was police." | | 16 | "He knew what she wanted." | | 17 | "She scaled the railing and" | | 18 | "Her knee protested — old" | | 19 | "He disappeared down a set" |
| | ratio | 0.349 | |
| 70.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 109 | | matches | | 0 | "The rain came down like" | | 1 | "Harlow Quinn cut through it" | | 2 | "Salt-and-pepper hair clung to her" | | 3 | "Water ran beneath the collar" | | 4 | "She never felt anything during" | | 5 | "The body had a way" | | 6 | "He was fast, she'd give" | | 7 | "He ducked left at the" | | 8 | "Quinn followed, one hand bracing" | | 9 | "The alley was narrow, lit" | | 10 | "Puddles reflected the light back" | | 11 | "Herrera was fifteen meters ahead," | | 12 | "The medallion swung wildly as" | | 13 | "Patron saint of travelers." | | 14 | "She almost laughed." | | 15 | "He wasn't doing much traveling." | | 16 | "He was doing a lot" | | 17 | "She knew what lay south." | | 18 | "She'd spent eighteen years learning" | | 19 | "A whisper from an informant" |
| | ratio | 0.78 | |
| 45.87% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 109 | | matches | | 0 | "Now Herrera was running straight" |
| | ratio | 0.009 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 11 | | matches | | 0 | "Harlow Quinn cut through it without slowing, her boots striking the wet pavement in the same measured rhythm she'd maintained for the last six blocks." | | 1 | "Faster than she'd expected from someone who spent his nights patching up criminals in back rooms." | | 2 | "The alley was narrow, lit only by the sickly orange bleed of a security light that buzzed and flickered like it was trying to die." | | 3 | "She'd spent eighteen years learning the geography of London's underworld — every bolt-hole, every safe house, every unmarked door that led somewhere it shouldn'…" | | 4 | "He'd been terrified of the place he'd described: an abandoned Tube station, a market that sold things you couldn't buy anywhere else." | | 5 | "The alley opened onto a wider street, and Quinn burst out just in time to see him vault a low iron railing with the desperate grace of someone who'd done it bef…" | | 6 | "His left arm caught the rail as he went over, and the old knife scar that ran along his forearm stretched taut against his skin." | | 7 | "The concrete walls closed in around her, slick with moisture and something else — something that glistened faintly with a luminescence that had no business exis…" | | 8 | "Vendors stood behind tables laden with objects that defied immediate comprehension: bottles filled with light, bundles of dried herbs that moved on their own, s…" | | 9 | "And somewhere in that crowd, Tomás Herrera was moving through the stalls with the ease of someone who'd done it many times before." | | 10 | "She stood at the threshold of the market, rain-soaked and trembling with something that wasn't cold, and she made her choice." |
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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 | |