| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.594 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1569 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "carefully" | | 1 | "slightly" | | 2 | "sharply" | | 3 | "suddenly" |
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| 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) | |
| 33.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1569 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "echoed" | | 1 | "rhythmic" | | 2 | "sanctuary" | | 3 | "depths" | | 4 | "standard" | | 5 | "silence" | | 6 | "constructed" | | 7 | "scanning" | | 8 | "velvet" | | 9 | "hulking" | | 10 | "pulsed" | | 11 | "silk" | | 12 | "echoing" | | 13 | "familiar" | | 14 | "porcelain" | | 15 | "vibrated" | | 16 | "shattered" | | 17 | "cacophony" | | 18 | "pulse" |
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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) | |
| 72.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 5 | | narrationSentences | 122 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1569 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1364 | | uniqueNames | 17 | | maxNameDensity | 1.61 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Saint | 2 | | Christopher | 2 | | Herrera | 11 | | Soho | 2 | | Morris | 1 | | Raven | 1 | | Nest | 1 | | Seville-born | 1 | | Tube | 1 | | Camden | 2 | | Veil | 1 | | Market | 2 | | Quinn | 22 | | Tommy | 2 | | Spanish | 1 | | Alchemist | 1 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Raven" | | 5 | "Market" | | 6 | "Quinn" | | 7 | "Tommy" | | 8 | "Alchemist" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Seville-born" |
| | globalScore | 0.694 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "mold that seemed to pulse in time with the growl" |
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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 | 1569 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 136 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 27.53 | | std | 20.95 | | cv | 0.761 | | sampleLengths | | 0 | 38 | | 1 | 19 | | 2 | 74 | | 3 | 60 | | 4 | 88 | | 5 | 11 | | 6 | 59 | | 7 | 65 | | 8 | 53 | | 9 | 4 | | 10 | 30 | | 11 | 72 | | 12 | 22 | | 13 | 4 | | 14 | 26 | | 15 | 6 | | 16 | 15 | | 17 | 11 | | 18 | 24 | | 19 | 24 | | 20 | 17 | | 21 | 33 | | 22 | 63 | | 23 | 34 | | 24 | 11 | | 25 | 20 | | 26 | 22 | | 27 | 38 | | 28 | 12 | | 29 | 4 | | 30 | 28 | | 31 | 13 | | 32 | 6 | | 33 | 13 | | 34 | 32 | | 35 | 45 | | 36 | 6 | | 37 | 43 | | 38 | 14 | | 39 | 50 | | 40 | 4 | | 41 | 18 | | 42 | 14 | | 43 | 12 | | 44 | 21 | | 45 | 69 | | 46 | 13 | | 47 | 4 | | 48 | 35 | | 49 | 25 |
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| 99.51% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 122 | | matches | | 0 | "being hauled" | | 1 | "were covered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 227 | | matches | | 0 | "was handing" | | 1 | "wasn't running" | | 2 | "was staring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 984 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.02032520325203252 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007113821138211382 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 11.54 | | std | 6.27 | | cv | 0.543 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 19 | | 3 | 6 | | 4 | 13 | | 5 | 17 | | 6 | 17 | | 7 | 21 | | 8 | 11 | | 9 | 20 | | 10 | 2 | | 11 | 3 | | 12 | 24 | | 13 | 16 | | 14 | 3 | | 15 | 10 | | 16 | 22 | | 17 | 2 | | 18 | 16 | | 19 | 19 | | 20 | 7 | | 21 | 4 | | 22 | 20 | | 23 | 20 | | 24 | 5 | | 25 | 14 | | 26 | 11 | | 27 | 11 | | 28 | 6 | | 29 | 13 | | 30 | 13 | | 31 | 10 | | 32 | 1 | | 33 | 10 | | 34 | 18 | | 35 | 10 | | 36 | 15 | | 37 | 4 | | 38 | 4 | | 39 | 21 | | 40 | 5 | | 41 | 22 | | 42 | 11 | | 43 | 14 | | 44 | 6 | | 45 | 19 | | 46 | 14 | | 47 | 8 | | 48 | 4 | | 49 | 5 |
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| 42.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3014705882352941 | | totalSentences | 136 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 114 | | matches | (empty) | | ratio | 0 | |
| 62.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 114 | | matches | | 0 | "He tumbled onto the grime-slicked" | | 1 | "She didn't wait for a" | | 2 | "She launched herself at the" | | 3 | "Her forty-one-year-old joints protested, but" | | 4 | "She cleared the top and" | | 5 | "He dived around a corner," | | 6 | "It was a known hub" | | 7 | "He kept running, his Seville-born" | | 8 | "He glanced back, his warm" | | 9 | "He didn't say a word." | | 10 | "He simply turned another corner" | | 11 | "She checked the worn leather" | | 12 | "She descended carefully, her hand" | | 13 | "She pushed it open." | | 14 | "He held out a gnarled" | | 15 | "She didn't have a bone" | | 16 | "She pulled out her warrant" | | 17 | "He leaned in, his breath" | | 18 | "He looked at the Saint" | | 19 | "She shoved past the man," |
| | ratio | 0.395 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 114 | | matches | | 0 | "Herrera's boot caught the top" | | 1 | "He tumbled onto the grime-slicked" | | 2 | "Quinn’s voice cut through the" | | 3 | "She didn't wait for a" | | 4 | "She launched herself at the" | | 5 | "Her forty-one-year-old joints protested, but" | | 6 | "She cleared the top and" | | 7 | "He dived around a corner," | | 8 | "Quinn sprinted after him, her" | | 9 | "The last time she called" | | 10 | "Herrera veered left, darting past" | | 11 | "The Raven's Nest." | | 12 | "The light cast long emerald" | | 13 | "Quinn caught a glimpse of" | | 14 | "It was a known hub" | | 15 | "He kept running, his Seville-born" | | 16 | "Quinn shouted, her chest heaving" | | 17 | "He glanced back, his warm" | | 18 | "The scar on his left" | | 19 | "He didn't say a word." |
| | ratio | 0.93 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 114 | | matches | (empty) | | ratio | 0 | |
| 28.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 11 | | matches | | 0 | "Herrera's boot caught the top of the chain-link fence, sending a jarring vibration through the metal that echoed down the narrow alley." | | 1 | "Herrera veered left, darting past a distinctive green neon sign that hummed with a low-frequency buzz." | | 2 | "He glanced back, his warm brown eyes wide with a mixture of terror and something that looked uncomfortably like pity." | | 3 | "She descended carefully, her hand gripping the cold iron railing." | | 4 | "Beyond the door, the abandoned station opened up into a cavernous space that shouldn't have existed beneath the foundations of Camden." | | 5 | "He let her pass, a low wheezing sound that might have been laughter following her into the throng." | | 6 | "Next to it, an old man arranged a collection of small delicate ivory flutes carved from things that weren't animal bones." | | 7 | "He wore a pinstriped suit that looked fifty years out of style, and his face was a mask of stretched porcelain." | | 8 | "She stumbled back, hitting a stall of glass vials that shattered in a cacophony of tinkling shards." | | 9 | "The liquid inside began to hiss as it touched the floor, releasing a thick white fog that blinded her." | | 10 | "The shadows at the end of the tunnel began to coalesce, forming a shape that was larger than the tunnel itself, a mass of shifting darkness and too many eyes." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 9 | | matches | | 0 | "Quinn shouted, her chest heaving" | | 1 | "Quinn said, her voice dropping to a dangerous register" | | 2 | "Quinn barked, her voice echoing off the curved station walls" | | 3 | "he called out, his Spanish accent thickening with stress" | | 4 | "Quinn moved, her boots clicking sharply on the platform" | | 5 | "Quinn asked, her hand moving to the handcuffs at her belt" | | 6 | "Herrera said, his voice trembling" | | 7 | "he replied, his voice hollowing out" | | 8 | "she said, though her heart hammered against her ribs like a trapped bird" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 10 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "the man grunted (grunt)" | | 2 | "Quinn barked (bark)" | | 3 | "he called out (call out)" | | 4 | "he whispered (whisper)" | | 5 | "she ordered (order)" | | 6 | "Quinn yelled (yell)" | | 7 | "he hissed (hiss)" | | 8 | "Quinn demanded (demand)" | | 9 | "Tommy whispered (whisper)" |
| | dialogueSentences | 32 | | tagDensity | 0.469 | | leniency | 0.938 | | rawRatio | 0.667 | | effectiveRatio | 0.625 | |