| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1239 | | totalAiIsmAdverbs | 3 | | 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) | |
| 51.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1239 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulse" | | 2 | "measured" | | 3 | "flicker" | | 4 | "weight" | | 5 | "echoed" | | 6 | "pulsed" | | 7 | "sense of" | | 8 | "velvet" |
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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 | 178 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 178 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 196 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 15 | | markdownWords | 43 | | totalWords | 1216 | | ratio | 0.035 | | matches | | 0 | "The Raven’s Nest" | | 1 | "“He’s wearing Morris’s face, but it’s not him.”" | | 2 | "click" | | 3 | "want" | | 4 | "Older" | | 5 | "The Veil Market" | | 6 | "names" | | 7 | "“I don’t know how to sign it.”" | | 8 | "saw" | | 9 | "am" | | 10 | "testing" | | 11 | "knew" | | 12 | "“Keep it safe,”" | | 13 | "“It might be your way in… or your way out.”" | | 14 | "sing" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1049 | | uniqueNames | 10 | | maxNameDensity | 0.76 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 8 | | Morris | 5 | | Latin | 1 | | Raven | 1 | | Silas | 1 | | Voice | 1 | | Camden | 1 | | Veil | 1 | | Met | 1 | | Harlow | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Harlow" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.822 | | wordCount | 1216 | | matches | | 0 | "Not from exhaustion, but recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 196 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 17.37 | | std | 16.06 | | cv | 0.925 | | sampleLengths | | 0 | 12 | | 1 | 3 | | 2 | 82 | | 3 | 11 | | 4 | 53 | | 5 | 29 | | 6 | 12 | | 7 | 49 | | 8 | 9 | | 9 | 3 | | 10 | 43 | | 11 | 13 | | 12 | 9 | | 13 | 9 | | 14 | 20 | | 15 | 25 | | 16 | 15 | | 17 | 39 | | 18 | 9 | | 19 | 47 | | 20 | 8 | | 21 | 3 | | 22 | 11 | | 23 | 3 | | 24 | 17 | | 25 | 8 | | 26 | 35 | | 27 | 12 | | 28 | 2 | | 29 | 32 | | 30 | 28 | | 31 | 2 | | 32 | 3 | | 33 | 53 | | 34 | 7 | | 35 | 6 | | 36 | 3 | | 37 | 45 | | 38 | 5 | | 39 | 19 | | 40 | 4 | | 41 | 38 | | 42 | 2 | | 43 | 40 | | 44 | 7 | | 45 | 21 | | 46 | 12 | | 47 | 5 | | 48 | 7 | | 49 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 178 | | matches | | 0 | "being hunted" | | 1 | "got killed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 189 | | matches | | 0 | "wasn’t warning" | | 1 | "was *testing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 24 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 196 | | ratio | 0.082 | | matches | | 0 | "The figure ahead—dark coat flapping, hands tucked in—dove between two shuttered shops." | | 1 | "And beneath it all—this same rhythm of retreat." | | 2 | "Surveillance footage from the night Morris died had an anomaly—an image glitch in a pub corner." | | 3 | "The barman—tall, gaunt, eyes like carved holes—didn’t look up as she crossed the floor." | | 4 | "Just walked to the bookshelf along the wall—antique oak, filled with fake spines—and pressed two books inward." | | 5 | "Then light—faint, shifting, like embers under ash." | | 6 | "The one ahead—fresher—still damp from rain." | | 7 | "The scent of candle wax and something sour—fermented roots, maybe—curled around her collar." | | 8 | "The space opened into a cavern—natural or dug, impossible to tell." | | 9 | "A rogue informant once claimed it traded in *names*—that if you sold yours, you’d forget who you’d been." | | 10 | "A table displayed teeth—human, animal, some with roots too long." | | 11 | "Or worse—everyone saw, but chose not to." | | 12 | "Its face—a mask of stitched leather—tilted slowly." | | 13 | "But the eyes—blackened at the edges, veins spidering beneath—weren’t his." | | 14 | "But the cold, sick sense of a door opening inside her skull—one she’d kept locked since the funeral." | | 15 | "Morris—or whatever wore him—lifted the vial." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1075 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.029767441860465118 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0037209302325581397 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 196 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 196 | | mean | 6.2 | | std | 4.78 | | cv | 0.77 | | sampleLengths | | 0 | 12 | | 1 | 3 | | 2 | 15 | | 3 | 12 | | 4 | 3 | | 5 | 26 | | 6 | 26 | | 7 | 5 | | 8 | 4 | | 9 | 2 | | 10 | 15 | | 11 | 16 | | 12 | 8 | | 13 | 1 | | 14 | 1 | | 15 | 12 | | 16 | 12 | | 17 | 9 | | 18 | 1 | | 19 | 7 | | 20 | 5 | | 21 | 2 | | 22 | 5 | | 23 | 6 | | 24 | 6 | | 25 | 3 | | 26 | 4 | | 27 | 16 | | 28 | 6 | | 29 | 3 | | 30 | 2 | | 31 | 3 | | 32 | 6 | | 33 | 3 | | 34 | 3 | | 35 | 14 | | 36 | 2 | | 37 | 13 | | 38 | 14 | | 39 | 8 | | 40 | 2 | | 41 | 3 | | 42 | 5 | | 43 | 4 | | 44 | 9 | | 45 | 4 | | 46 | 9 | | 47 | 7 | | 48 | 13 | | 49 | 12 |
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| 65.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.42857142857142855 | | totalSentences | 196 | | uniqueOpeners | 84 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 133 | | matches | | 0 | "Just one letter short of" | | 1 | "Just low murmurs from a" | | 2 | "Then, with deliberate slowness, he" | | 3 | "Just walked to the bookshelf" | | 4 | "Then light—faint, shifting, like embers" | | 5 | "Then she saw him." | | 6 | "Even the wind died." |
| | ratio | 0.053 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 133 | | matches | | 0 | "Her oxfords slipped on wet" | | 1 | "She vaulted over a bin," | | 2 | "She’d seen that gait before." | | 3 | "It had been dripping water," | | 4 | "She’d been here six months" | | 5 | "It got killed." | | 6 | "She stepped inside." | | 7 | "He wiped a glass slowly." | | 8 | "She didn’t blink." | | 9 | "Her fingers twitched toward her" | | 10 | "She stepped past him." | | 11 | "It opened into a descending" | | 12 | "She stepped forward." | | 13 | "He committed suicide three days" | | 14 | "Her hand went to her" | | 15 | "She was still Harlow Quinn." | | 16 | "He handed over a small" | | 17 | "She kept low, weaving between" | | 18 | "Its face—a mask of stitched" | | 19 | "it said, voice like paper" |
| | ratio | 0.218 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 133 | | matches | | 0 | "The umbrella snapped sideways in" | | 1 | "Detective Quinn ran." | | 2 | "Her oxfords slipped on wet" | | 3 | "The figure ahead—dark coat flapping," | | 4 | "Harlow didn’t slow." | | 5 | "She vaulted over a bin," | | 6 | "She’d seen that gait before." | | 7 | "It had been dripping water," | | 8 | "The green neon sign above" | | 9 | "Quinn skidded to a halt." | | 10 | "She’d been here six months" | | 11 | "Owner’s name: Silas." | | 12 | "Surveillance footage from the night" | | 13 | "The bar’s logo in the" | | 14 | "A green raven." | | 15 | "Coincidence didn’t wear out its" | | 16 | "It got killed." | | 17 | "She stepped inside." | | 18 | "The air was warm, thick" | | 19 | "The barman—tall, gaunt, eyes like" |
| | ratio | 0.602 | |
| 37.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 133 | | matches | | | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "She vaulted over a bin, the movement sharp from muscle memory, her left hand brushing the worn leather strap of her watch as if checking pulse." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "stride she whispered (whisper)" |
| | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0.111 | | effectiveRatio | 0.063 | |