| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "someone called softly [softly]" |
| | dialogueSentences | 19 | | tagDensity | 0.684 | | leniency | 1 | | rawRatio | 0.077 | | effectiveRatio | 0.077 | |
| 93.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1549 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "seductively" | | count | 1 |
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| | 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) | |
| 25.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1549 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "measured" | | 1 | "scanned" | | 2 | "pulse" | | 3 | "flicked" | | 4 | "echoed" | | 5 | "glinting" | | 6 | "pounding" | | 7 | "tracing" | | 8 | "weight" | | 9 | "footsteps" | | 10 | "gleaming" | | 11 | "racing" | | 12 | "unravel" | | 13 | "flickered" | | 14 | "whisper" | | 15 | "chill" | | 16 | "eyebrow" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 159 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1531 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1393 | | uniqueNames | 15 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | Veil | 3 | | Market | 5 | | Camden | 1 | | Tube | 1 | | London | 2 | | One | 1 | | Slipping | 1 | | Silas | 3 | | Turn | 1 | | Rain | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Camden" | | 6 | "Tube" | | 7 | "One" | | 8 | "Silas" | | 9 | "Rain" |
| | places | | | globalScore | 0.997 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | 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 | 1531 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 159 | | matches | (empty) | |
| 97.48% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 41.38 | | std | 20.32 | | cv | 0.491 | | sampleLengths | | 0 | 64 | | 1 | 37 | | 2 | 22 | | 3 | 49 | | 4 | 61 | | 5 | 79 | | 6 | 59 | | 7 | 42 | | 8 | 48 | | 9 | 90 | | 10 | 28 | | 11 | 37 | | 12 | 82 | | 13 | 46 | | 14 | 7 | | 15 | 32 | | 16 | 18 | | 17 | 41 | | 18 | 16 | | 19 | 29 | | 20 | 53 | | 21 | 72 | | 22 | 17 | | 23 | 8 | | 24 | 41 | | 25 | 57 | | 26 | 60 | | 27 | 44 | | 28 | 35 | | 29 | 20 | | 30 | 43 | | 31 | 45 | | 32 | 13 | | 33 | 18 | | 34 | 39 | | 35 | 45 | | 36 | 34 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 152 | | matches | | 0 | "were scrawled" | | 1 | "were written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 268 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 3 | | flaggedSentences | 16 | | totalSentences | 159 | | ratio | 0.101 | | matches | | 0 | "Detective Harlow Quinn’s boots slapped against the wet asphalt, each step measured, precise—military precision drilled in during her early days as a recruit." | | 1 | "The suspect glanced back—heart-shaped profile, mask of surprise—then sprinted across the street." | | 2 | "She’d lost track of her partner, DS Morris, to this kind of darkness three years ago—shadows that moved like living things, unnatural forces she still didn’t understand." | | 3 | "She recognized the shape now: a bone token, the kind whispered about among occult rumors—an entry pass to the underground Veil Market." | | 4 | "She’d been here before—in the nightmares that followed Morris’s death—only in her mind." | | 5 | "Quinn felt the pulse of something else—voices, faint laughter, the scrape of metal on stone." | | 6 | "She drew her service pistol—steel weight reassuring in her grip—though pistol rounds wouldn’t stop everything she’d faced." | | 7 | "People moved between them: cloaked figures, faces hidden beneath hoods, hands gloved; one I saw clutching a live raven that tilted its head and croaked." | | 8 | "She’d heard rumors—Veil Market, the Market, the underground bazaar where forbidden trades happened." | | 9 | "He smiled—shy, unsettling." | | 10 | "One held a baroque dagger; the other pressed coins into a leather pouch." | | 11 | "She’d never taken the time to understand the occult underbelly; Morris’s death had always been an angry riddle she hadn’t wanted to unravel." | | 12 | "No signal came through her radio—dead frequencies, brick walls." | | 13 | "Her military training insisted she control the scene, gather evidence, call for backup—but her code badge meant nothing here." | | 14 | "Every instinct screamed no—but her stubbornness, her need for answers, screamed yes." | | 15 | "Beneath rain-soaked streets, in the hidden veins of London, he waited—the suspect, and whatever darkness he served." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 919 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.01632208922742111 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004352557127312296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 159 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 159 | | mean | 9.63 | | std | 6.24 | | cv | 0.648 | | sampleLengths | | 0 | 16 | | 1 | 23 | | 2 | 14 | | 3 | 7 | | 4 | 4 | | 5 | 21 | | 6 | 3 | | 7 | 1 | | 8 | 12 | | 9 | 10 | | 10 | 12 | | 11 | 3 | | 12 | 18 | | 13 | 5 | | 14 | 12 | | 15 | 11 | | 16 | 7 | | 17 | 6 | | 18 | 15 | | 19 | 27 | | 20 | 6 | | 21 | 8 | | 22 | 4 | | 23 | 15 | | 24 | 17 | | 25 | 7 | | 26 | 22 | | 27 | 6 | | 28 | 14 | | 29 | 8 | | 30 | 15 | | 31 | 10 | | 32 | 2 | | 33 | 10 | | 34 | 5 | | 35 | 6 | | 36 | 16 | | 37 | 15 | | 38 | 6 | | 39 | 4 | | 40 | 13 | | 41 | 4 | | 42 | 4 | | 43 | 2 | | 44 | 8 | | 45 | 7 | | 46 | 8 | | 47 | 15 | | 48 | 14 | | 49 | 17 |
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| 58.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.37735849056603776 | | totalSentences | 159 | | uniqueOpeners | 60 | |
| 23.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 143 | | matches | | 0 | "Bright brown eyes glowed under" |
| | ratio | 0.007 | |
| 66.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 143 | | matches | | 0 | "She kept her shoulders squared" | | 1 | "She scanned every shadow." | | 2 | "She pulled her collar tighter," | | 3 | "she barked, voice iron-honed from" | | 4 | "Her worn leather watch glinted" | | 5 | "She vaulted the curb, tires" | | 6 | "She landed in a spray" | | 7 | "She tasted it on her" | | 8 | "She’d lost track of her" | | 9 | "She glimpsed a tremor of" | | 10 | "She recognized the shape now:" | | 11 | "She yanked at the rotted" | | 12 | "She pressed her palms to" | | 13 | "She tightened her jaw." | | 14 | "She’d been here before—in the" | | 15 | "She had no backup." | | 16 | "She reached out, fingertips grazing" | | 17 | "She drew her service pistol—steel" | | 18 | "She stepped over the threshold." | | 19 | "Her skin prickled." |
| | ratio | 0.385 | |
| 43.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 143 | | matches | | 0 | "Rain slicked the pavement in" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "She kept her shoulders squared" | | 3 | "The streetlights overhead cast halos" | | 4 | "She scanned every shadow." | | 5 | "Quinn’s pulse jumped." | | 6 | "She pulled her collar tighter," | | 7 | "she barked, voice iron-honed from" | | 8 | "The suspect glanced back—heart—shaped profile," | | 9 | "Quinn didn’t hesitate." | | 10 | "Her worn leather watch glinted" | | 11 | "She vaulted the curb, tires" | | 12 | "She landed in a spray" | | 13 | "She tasted it on her" | | 14 | "Heart thudding, she wove through" | | 15 | "She’d lost track of her" | | 16 | "Nothing would blur her focus" | | 17 | "The suspect dove into a" | | 18 | "Quinn followed, lungs burning." | | 19 | "The alley was only wide" |
| | ratio | 0.832 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 143 | | matches | | 0 | "Now it was real." | | 1 | "Now she was standing at" | | 2 | "Even if it meant plunging" | | 3 | "Even if her next step" |
| | ratio | 0.028 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "She’d lost track of her partner, DS Morris, to this kind of darkness three years ago—shadows that moved like living things, unnatural forces she still didn’t un…" | | 1 | "She’d been here before—in the nightmares that followed Morris’s death—only in her mind." | | 2 | "Stalls lined the edges: jars brimming with powders that glowed like embers, dusty tomes bound in strange leathers, gleaming daggers with runic etchings." | | 3 | "People moved between them: cloaked figures, faces hidden beneath hoods, hands gloved; one I saw clutching a live raven that tilted its head and croaked." | | 4 | "Banshee charms, demon blood, elixirs that could bend a man’s will." | | 5 | "She thought of Morris, his laugh, the case that swallowed him." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn demanded, voice uneven" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "behind her hissed (hiss)" | | 2 | "Quinn demanded (demand)" | | 3 | "he whispered (whisper)" |
| | dialogueSentences | 19 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0.571 | | effectiveRatio | 0.421 | |