| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1291 | | 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) | |
| 41.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1291 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "lurching" | | 1 | "gloom" | | 2 | "beacon" | | 3 | "maw" | | 4 | "churned" | | 5 | "silence" | | 6 | "constructed" | | 7 | "velvet" | | 8 | "sentinel" | | 9 | "pulse" | | 10 | "grave" | | 11 | "scanned" |
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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 | 1 | | narrationSentences | 76 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 77 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1285 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1256 | | uniqueNames | 19 | | maxNameDensity | 1.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Soho | 1 | | Harlow | 2 | | Quinn | 13 | | Tomás | 1 | | Herrera | 10 | | Morris | 3 | | Euston | 1 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Victorian | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 | | Looking | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Raven" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" | | 9 | "Looking" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Euston" |
| | globalScore | 0.982 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1285 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 77 | | matches | | 0 | "crossed that threshold" |
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| 84.64% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 49.42 | | std | 22.05 | | cv | 0.446 | | sampleLengths | | 0 | 52 | | 1 | 95 | | 2 | 48 | | 3 | 91 | | 4 | 59 | | 5 | 52 | | 6 | 17 | | 7 | 25 | | 8 | 39 | | 9 | 45 | | 10 | 60 | | 11 | 54 | | 12 | 55 | | 13 | 15 | | 14 | 22 | | 15 | 5 | | 16 | 62 | | 17 | 63 | | 18 | 71 | | 19 | 56 | | 20 | 82 | | 21 | 60 | | 22 | 21 | | 23 | 45 | | 24 | 46 | | 25 | 45 |
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| 82.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 76 | | matches | | 0 | "was churned" | | 1 | "was replaced" | | 2 | "was lit" | | 3 | "being held" | | 4 | "was destined" |
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| 63.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 196 | | matches | | 0 | "was standing" | | 1 | "was speaking" | | 2 | "was looking" | | 3 | "was entering" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 2 | | flaggedSentences | 5 | | totalSentences | 77 | | ratio | 0.065 | | matches | | 0 | "He didn't run—not yet—but his pace doubled into a desperate, lurching jog." | | 1 | "She was standing in the subterranean guts of London—specifically, the decommissioned tunnels of an old Tube station." | | 2 | "The silence of the abandoned station was replaced by a low, vibrating hum—a chorus of hushed, urgent bartering in languages she didn't recognize." | | 3 | "She saw hands with too many knuckles trading velvet pouches; she saw bottles of thick, iridescent liquids being held up to the candlelight; she saw dried, unrecognizable specimens hanging from copper hooks like cured meats." | | 4 | "To enter, she saw a sentinel standing at the archway—a massive figure draped in a patchwork trench coat, face obscured by a wide-brimmed felt hat." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 751 | | adjectiveStacks | 1 | | stackExamples | | 0 | "cold, petrol-washed scent" |
| | adverbCount | 15 | | adverbRatio | 0.019973368841544607 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.010652463382157125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 77 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 77 | | mean | 16.69 | | std | 9.55 | | cv | 0.572 | | sampleLengths | | 0 | 33 | | 1 | 19 | | 2 | 4 | | 3 | 28 | | 4 | 36 | | 5 | 27 | | 6 | 5 | | 7 | 18 | | 8 | 25 | | 9 | 17 | | 10 | 15 | | 11 | 14 | | 12 | 21 | | 13 | 24 | | 14 | 15 | | 15 | 16 | | 16 | 5 | | 17 | 20 | | 18 | 3 | | 19 | 12 | | 20 | 30 | | 21 | 10 | | 22 | 14 | | 23 | 3 | | 24 | 3 | | 25 | 22 | | 26 | 10 | | 27 | 29 | | 28 | 14 | | 29 | 17 | | 30 | 14 | | 31 | 11 | | 32 | 28 | | 33 | 21 | | 34 | 5 | | 35 | 29 | | 36 | 20 | | 37 | 13 | | 38 | 18 | | 39 | 24 | | 40 | 15 | | 41 | 8 | | 42 | 14 | | 43 | 5 | | 44 | 36 | | 45 | 26 | | 46 | 23 | | 47 | 21 | | 48 | 19 | | 49 | 8 |
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| 49.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.37662337662337664 | | totalSentences | 77 | | uniqueOpeners | 29 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Instead, Herrera bypassed the entrance" | | 1 | "Just her shield, her service" |
| | ratio | 0.027 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 75 | | matches | | 0 | "It was Tomás Herrera." | | 1 | "She knew the slouch of" | | 2 | "He was the medic for" | | 3 | "She had followed him from" | | 4 | "She had half an expectation" | | 5 | "She picking up her pace," | | 6 | "Her five-foot-nine frame moved with" | | 7 | "He saw her." | | 8 | "He didn't run—not yet—but his" | | 9 | "He clutched his left forearm" | | 10 | "It was a nervous tic" | | 11 | "He scrambled down a flight" | | 12 | "She descended into the dark," | | 13 | "She was standing in the" | | 14 | "It was pitch black save" | | 15 | "She pulled her tactical torch" | | 16 | "It was the same symbol" | | 17 | "She crept through the gap" | | 18 | "She stepped behind the shadow" | | 19 | "She had heard whispers of" |
| | ratio | 0.4 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 75 | | matches | | 0 | "The rain in London did" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "It was Tomás Herrera." | | 3 | "She knew the slouch of" | | 4 | "A former paramedic who had" | | 5 | "He was the medic for" | | 6 | "Quinn checked her left wrist." | | 7 | "The worn leather strap of" | | 8 | "She had followed him from" | | 9 | "Herrera veered hard to the" | | 10 | "Quinn paused under the dripping" | | 11 | "The bar was infamous, a" | | 12 | "She had half an expectation" | | 13 | "She picking up her pace," | | 14 | "Her five-foot-nine frame moved with" | | 15 | "The pale glow of a" | | 16 | "He saw her." | | 17 | "He didn't run—not yet—but his" | | 18 | "He clutched his left forearm" | | 19 | "It was a nervous tic" |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 75 | | matches | | 0 | "To enter, she saw a" | | 1 | "If she crossed that threshold," |
| | ratio | 0.027 | |
| 51.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 7 | | matches | | 0 | "The rain in London did not fall so much as it drifted, a heavy, greasy mist that coated the cobblestones and blurred the neon glare of Soho into smears of pink …" | | 1 | "She knew the slouch of his shoulders, the hurried, limping gait that favored his left leg, and the dark, curly hair plastered to his forehead by the elements." | | 2 | "Instead, Herrera bypassed the entrance entirely, darting down a narrow pedestrian trench that led toward the canal and the dark boundaries of Camden beyond." | | 3 | "She picking up her pace, her boots striking the wet pavement with quiet, practiced precision." | | 4 | "Hanging from a twisted wire on the gate was a small wooden placard, hand-carved with a crude sigil that made the skin on the back of her neck prickle." | | 5 | "There was an odor here that didn't belong in a disused train line: the scent of crushed cloves, burnt fat, and wet animal fur." | | 6 | "He was speaking rapidly to the merchant, his hands gesturing wildly, his face pale and slick with cold sweat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |