| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1459 | | 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) | |
| 76.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1459 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "fractured" | | 1 | "streaming" | | 2 | "footsteps" | | 3 | "glint" | | 4 | "stomach" | | 5 | "silence" | | 6 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 109 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1459 | | 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 | 31 | | wordCount | 1459 | | uniqueNames | 14 | | maxNameDensity | 0.55 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 3 | | Raven | 1 | | Nest | 2 | | Quinn | 8 | | Silas | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Christopher | 1 | | Morris | 5 | | Camden | 1 | | Tube | 2 | | London | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Silas" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "London" | | 5 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 3 | | matches | | 0 | "not quite invisible" | | 1 | "something like it once before, in the hours" | | 2 | "objects that seemed to shift and glitter in the strange light" |
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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 | 1459 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 109 | | matches | | 0 | "believed that the" | | 1 | "Knew that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 52.11 | | std | 29.15 | | cv | 0.56 | | sampleLengths | | 0 | 63 | | 1 | 76 | | 2 | 13 | | 3 | 54 | | 4 | 48 | | 5 | 69 | | 6 | 99 | | 7 | 10 | | 8 | 47 | | 9 | 88 | | 10 | 4 | | 11 | 66 | | 12 | 53 | | 13 | 29 | | 14 | 81 | | 15 | 55 | | 16 | 55 | | 17 | 5 | | 18 | 52 | | 19 | 77 | | 20 | 95 | | 21 | 16 | | 22 | 99 | | 23 | 50 | | 24 | 68 | | 25 | 11 | | 26 | 66 | | 27 | 10 |
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| 92.39% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 109 | | matches | | 0 | "been found" | | 1 | "were tiled" | | 2 | "been welded" | | 3 | "been transformed" |
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| 31.22% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 237 | | matches | | 0 | "was being" | | 1 | "were burning" | | 2 | "was going" | | 3 | "wasn't running" | | 4 | "was leading" | | 5 | "was displaying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1468 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.022479564032697547 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005449591280653951 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 13.39 | | std | 8.81 | | cv | 0.658 | | sampleLengths | | 0 | 27 | | 1 | 20 | | 2 | 6 | | 3 | 10 | | 4 | 8 | | 5 | 18 | | 6 | 6 | | 7 | 20 | | 8 | 7 | | 9 | 5 | | 10 | 12 | | 11 | 2 | | 12 | 1 | | 13 | 10 | | 14 | 21 | | 15 | 21 | | 16 | 8 | | 17 | 4 | | 18 | 17 | | 19 | 1 | | 20 | 20 | | 21 | 10 | | 22 | 32 | | 23 | 15 | | 24 | 10 | | 25 | 8 | | 26 | 4 | | 27 | 3 | | 28 | 17 | | 29 | 20 | | 30 | 7 | | 31 | 3 | | 32 | 19 | | 33 | 5 | | 34 | 20 | | 35 | 5 | | 36 | 2 | | 37 | 4 | | 38 | 4 | | 39 | 32 | | 40 | 10 | | 41 | 5 | | 42 | 8 | | 43 | 3 | | 44 | 3 | | 45 | 15 | | 46 | 32 | | 47 | 27 | | 48 | 4 | | 49 | 15 |
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| 70.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.47706422018348627 | | totalSentences | 109 | | uniqueOpeners | 52 | |
| 64.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 103 | | matches | | 0 | "Then she spotted him again," | | 1 | "Just went down, down, into" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 103 | | matches | | 0 | "Her worn leather watch read" | | 1 | "She'd been watching Silas's place" | | 2 | "He'd gone in at half" | | 3 | "She'd called it survival." | | 4 | "She rounded the corner and" | | 5 | "Her lungs were burning, but" | | 6 | "He was fast." | | 7 | "His legs pumped with a" | | 8 | "She catalogued the details as" | | 9 | "She filed it away." | | 10 | "He was leading her somewhere." | | 11 | "She lost him for a" | | 12 | "He didn't pause at the" | | 13 | "Her chest heaved." | | 14 | "She should wait." | | 15 | "She should call it in." | | 16 | "She could recite them chapter" | | 17 | "She'd written them up after" | | 18 | "Her fingers found the watch" | | 19 | "She started down the stairs." |
| | ratio | 0.282 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 103 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn pressed her back" | | 2 | "Her worn leather watch read" | | 3 | "She'd been watching Silas's place" | | 4 | "Tonight, a new one had" | | 5 | "He'd gone in at half" | | 6 | "Quinn pushed off the wall" | | 7 | "Military precision, her training officer" | | 8 | "She'd called it survival." | | 9 | "The alley opened onto a" | | 10 | "Quinn broke into a sprint," | | 11 | "She rounded the corner and" | | 12 | "Quinn followed, ignoring the cab" | | 13 | "Her lungs were burning, but" | | 14 | "The rhythm of pursuit." | | 15 | "He was fast." | | 16 | "His legs pumped with a" | | 17 | "She catalogued the details as" | | 18 | "Knife wound, old but nasty." | | 19 | "A medallion on a chain." |
| | ratio | 0.67 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 15 | | matches | | 0 | "The rain came down in sheets, turning the Soho pavement into a slick mirror that reflected the green neon of the Raven's Nest sign in fractured ribbons." | | 1 | "Three weeks of stale coffee and cold takeaway, of cataloguing every face that passed through that green-lit doorway." | | 2 | "Young man, olive skin, dark curly hair, moved with the careful efficiency of someone who knew he was being watched." | | 3 | "And something else, something that caught the streetlight as he turned his head, a glint of metal against his chest." | | 4 | "The shops changed, became grittier, their windows boarded or displaying faded signs for businesses that had closed years ago." | | 5 | "One of the abandoned stations that dotted London's underground like forgotten graves." | | 6 | "Something herbal, almost medicinal, undercut by a darker note that made her think of copper and old stone." | | 7 | "The emergency lighting threw everything in a sickly pallor that made it difficult to judge distance." | | 8 | "A passage stretched ahead, wide enough for three people to walk abreast, lined with doors that had been welded shut." | | 9 | "Low murmurs, the clink of glass or metal, a burst of something that might have been laughter." | | 10 | "The sounds beyond the door were wrong in a way she couldn't articulate, a combination of familiar and alien that set her teeth on edge." | | 11 | "Peering around the doorframe, she saw a vast underground space that had no business existing beneath a closed Tube station." | | 12 | "She spotted her suspect near the center of the market, walking with apparent calm toward a stall where a figure in a hooded cloak was displaying small objects t…" | | 13 | "Quinn's jaw tightened, the sharp line of it set in a expression that had made suspects confess and colleagues flinch." | | 14 | "Ahead lay something else entirely, something that might finally give her answers about Morris or might swallow her whole." |
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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 | |