| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1077 | | 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) | |
| 30.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1077 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "flickered" | | 1 | "warmth" | | 2 | "scanned" | | 3 | "pulse" | | 4 | "quickened" | | 5 | "flicker" | | 6 | "tracing" | | 7 | "loomed" | | 8 | "stomach" | | 9 | "raced" | | 10 | "glinting" | | 11 | "depths" | | 12 | "measured" | | 13 | "weight" |
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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 | 111 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 13 | | totalWords | 1065 | | ratio | 0.012 | | matches | | 0 | "You shouldn’t be here." | | 1 | "alive" | | 2 | "Truth Serum—Guaranteed Results" | | 3 | "This is your last chance." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1042 | | uniqueNames | 16 | | maxNameDensity | 1.34 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Harlow | 14 | | Quinn | 1 | | Raven | 2 | | Nest | 2 | | Veil | 2 | | Market | 2 | | Tube | 1 | | Camden | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Serum | 1 | | Guaranteed | 1 | | Morris | 2 | | Tomás | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Camden" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" | | 8 | "Morris" | | 9 | "Tomás" |
| | places | | | globalScore | 0.828 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1065 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 96.51% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 39.44 | | std | 19.24 | | cv | 0.488 | | sampleLengths | | 0 | 78 | | 1 | 68 | | 2 | 43 | | 3 | 64 | | 4 | 39 | | 5 | 59 | | 6 | 59 | | 7 | 74 | | 8 | 45 | | 9 | 61 | | 10 | 45 | | 11 | 48 | | 12 | 9 | | 13 | 31 | | 14 | 15 | | 15 | 10 | | 16 | 15 | | 17 | 41 | | 18 | 35 | | 19 | 31 | | 20 | 41 | | 21 | 21 | | 22 | 29 | | 23 | 24 | | 24 | 18 | | 25 | 37 | | 26 | 25 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 111 | | matches | | 0 | "were lined" | | 1 | "been repurposed" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 182 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 115 | | ratio | 0.087 | | matches | | 0 | "The suspect—a wiry man in a dark coat—had darted inside moments ago, and she wasn’t about to lose him now." | | 1 | "Then—movement." | | 2 | "She’d heard rumors about The Raven’s Nest—whispers of a hidden room, of deals made in the dark." | | 3 | "Bone tokens, enchanted trinkets, vials of swirling liquid—this was the Veil Market." | | 4 | "She stepped forward, her boots crunching on something brittle—dried leaves, maybe, or old parchment." | | 5 | "She’d seen his name in reports—former paramedic, now a ghost in the system." | | 6 | "The label read *Truth Serum—Guaranteed Results*." | | 7 | "But this—this was different." | | 8 | "Then, a sound—a clink, like metal on stone." | | 9 | "But for now, she let the weight of the unseen press against her back as she climbed the steps, leaving the Veil Market—and its mysteries—behind." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1056 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.021780303030303032 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004734848484848485 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.26 | | std | 6.16 | | cv | 0.665 | | sampleLengths | | 0 | 15 | | 1 | 23 | | 2 | 17 | | 3 | 3 | | 4 | 20 | | 5 | 22 | | 6 | 11 | | 7 | 15 | | 8 | 20 | | 9 | 5 | | 10 | 8 | | 11 | 1 | | 12 | 9 | | 13 | 3 | | 14 | 17 | | 15 | 9 | | 16 | 17 | | 17 | 7 | | 18 | 11 | | 19 | 20 | | 20 | 10 | | 21 | 16 | | 22 | 3 | | 23 | 6 | | 24 | 4 | | 25 | 12 | | 26 | 14 | | 27 | 12 | | 28 | 21 | | 29 | 4 | | 30 | 13 | | 31 | 22 | | 32 | 6 | | 33 | 7 | | 34 | 7 | | 35 | 14 | | 36 | 9 | | 37 | 22 | | 38 | 2 | | 39 | 13 | | 40 | 14 | | 41 | 3 | | 42 | 10 | | 43 | 7 | | 44 | 17 | | 45 | 5 | | 46 | 3 | | 47 | 10 | | 48 | 6 | | 49 | 6 |
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| 40.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.2608695652173913 | | totalSentences | 115 | | uniqueOpeners | 30 | |
| 97.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 103 | | matches | | 0 | "Then, the tunnel opened into" | | 1 | "Then, a sound—a clink, like" | | 2 | "Just jerked his chin toward" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 103 | | matches | | 0 | "She didn’t slow." | | 1 | "Her leather watch snug against" | | 2 | "She scanned the room, her" | | 3 | "She moved with military precision," | | 4 | "She’d heard rumors about The" | | 5 | "Her suspect was here." | | 6 | "She spotted him near a" | | 7 | "He was talking to a" | | 8 | "She had no jurisdiction here," | | 9 | "She stepped forward, her boots" | | 10 | "She’d seen his name in" | | 11 | "He gave her a slight" | | 12 | "Her target was moving again," | | 13 | "She followed, her senses on" | | 14 | "It was *alive*." | | 15 | "She resisted the urge to" | | 16 | "She knew better." | | 17 | "She drew her weapon." | | 18 | "She’d faced down rioters, armed" | | 19 | "She took a step back," |
| | ratio | 0.262 | |
| 57.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 103 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The green neon sign of" | | 3 | "She didn’t slow." | | 4 | "The suspect—a wiry man in" | | 5 | "Her leather watch snug against" | | 6 | "The scent of aged whiskey" | | 7 | "The walls were lined with" | | 8 | "She scanned the room, her" | | 9 | "A shadow slipping past the" | | 10 | "Harlow didn’t hesitate." | | 11 | "She moved with military precision," | | 12 | "The bookshelf at the far" | | 13 | "She’d heard rumors about The" | | 14 | "The air here was thicker," | | 15 | "The corridor twisted, descending into" | | 16 | "The rain’s drumming faded, replaced" | | 17 | "Harlow’s pulse quickened." | | 18 | "This wasn’t just a back" | | 19 | "This was a descent." |
| | ratio | 0.806 | |
| 97.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 103 | | matches | | 0 | "Now, she saw the truth" | | 1 | "Before him loomed a figure" |
| | ratio | 0.019 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "She moved with military precision, weaving between tables, her hand resting on the holster at her hip." | | 1 | "He was talking to a woman draped in a cloak of raven feathers, her fingers tracing the edge of a bone token." | | 2 | "The tunnel sloped downward, the gas lamps giving way to torches that flickered with an eerie blue flame." | | 3 | "The suspect stood at its center, his back to her, his hands raised as if in surrender." | | 4 | "And standing in the tunnel’s mouth, his medallion glinting in the blue flame, was Tomás." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |