| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 926 | | 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) | |
| 13.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 926 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "churning" | | 1 | "pulse" | | 2 | "loomed" | | 3 | "jaw clenched" | | 4 | "lurched" | | 5 | "weight" | | 6 | "echoes" | | 7 | "footsteps" | | 8 | "echoed" | | 9 | "gloom" | | 10 | "pulsed" | | 11 | "thundered" | | 12 | "depths" | | 13 | "flickered" | | 14 | "echo" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 903 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.63% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 876 | | uniqueNames | 22 | | maxNameDensity | 1.03 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Pistol" | | discoveredNames | | Camden | 3 | | Harlow | 9 | | Quinn | 1 | | Tomás | 8 | | Herrera | 3 | | Raven | 2 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Morse | 1 | | Inverness | 1 | | Street | 2 | | Veil | 2 | | Market | 1 | | Tube | 1 | | Blitz | 1 | | Morris | 2 | | English | 1 | | Rain | 4 | | Brown | 3 | | One | 4 | | Pistol | 5 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Rain" | | 10 | "Brown" | | 11 | "One" | | 12 | "Pistol" |
| | places | | 0 | "Inverness" | | 1 | "Street" | | 2 | "Veil" |
| | globalScore | 0.986 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 903 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 16.72 | | std | 14.39 | | cv | 0.861 | | sampleLengths | | 0 | 62 | | 1 | 51 | | 2 | 33 | | 3 | 3 | | 4 | 32 | | 5 | 36 | | 6 | 28 | | 7 | 40 | | 8 | 22 | | 9 | 4 | | 10 | 34 | | 11 | 27 | | 12 | 20 | | 13 | 3 | | 14 | 31 | | 15 | 32 | | 16 | 27 | | 17 | 29 | | 18 | 28 | | 19 | 39 | | 20 | 33 | | 21 | 9 | | 22 | 25 | | 23 | 22 | | 24 | 11 | | 25 | 20 | | 26 | 27 | | 27 | 14 | | 28 | 3 | | 29 | 6 | | 30 | 11 | | 31 | 12 | | 32 | 3 | | 33 | 24 | | 34 | 2 | | 35 | 10 | | 36 | 6 | | 37 | 7 | | 38 | 4 | | 39 | 4 | | 40 | 5 | | 41 | 3 | | 42 | 8 | | 43 | 2 | | 44 | 17 | | 45 | 7 | | 46 | 4 | | 47 | 5 | | 48 | 3 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 210 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 6 | | flaggedSentences | 14 | | totalSentences | 138 | | ratio | 0.101 | | matches | | 0 | "Suspicious glance toward the bookshelf-hidden room inside—clique business, she bet." | | 1 | "Soho's pulse faded behind; Camden's grit closed in." | | 2 | "Crowd thinned at this hour—drunks huddled in doorways, hoods up." | | 3 | "Harlow shouldered him aside; he spun into a lamppost with a meaty thud." | | 4 | "Fire lanced; she grunted, stumbled back two steps." | | 5 | "18 years on the force; chases never got easier at 41." | | 6 | "Pistol stayed holstered—backup none in this storm, radio fritzed wet." | | 7 | "Gloves shredded on barbs; blood welled hot." | | 8 | "Bone token dangled from his fist—ivory shard carved rune-twisted." | | 9 | "He flashed it at a hidden seam; stone ground aside, revealing stairwell mouth." | | 10 | "Herrera's treatments—off-books for freaks." | | 11 | "A voice below sharpened—Tomás?" | | 12 | "A figure flickered at the landing—cloaked, eyes compound-reflecting light." | | 13 | "Token's rune echo lingered in her mind—Herrera's scar, his medallion, the Raven's hidden room." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 899 | | adjectiveStacks | 1 | | stackExamples | | 0 | "copper-fresh, alchemical tang" |
| | adverbCount | 9 | | adverbRatio | 0.010011123470522803 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0033370411568409346 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 6.54 | | std | 3.7 | | cv | 0.566 | | sampleLengths | | 0 | 11 | | 1 | 19 | | 2 | 13 | | 3 | 19 | | 4 | 13 | | 5 | 17 | | 6 | 10 | | 7 | 9 | | 8 | 2 | | 9 | 10 | | 10 | 7 | | 11 | 6 | | 12 | 10 | | 13 | 3 | | 14 | 4 | | 15 | 13 | | 16 | 8 | | 17 | 7 | | 18 | 12 | | 19 | 14 | | 20 | 10 | | 21 | 10 | | 22 | 5 | | 23 | 13 | | 24 | 14 | | 25 | 7 | | 26 | 9 | | 27 | 10 | | 28 | 2 | | 29 | 5 | | 30 | 7 | | 31 | 8 | | 32 | 4 | | 33 | 9 | | 34 | 5 | | 35 | 5 | | 36 | 4 | | 37 | 11 | | 38 | 5 | | 39 | 7 | | 40 | 10 | | 41 | 5 | | 42 | 6 | | 43 | 4 | | 44 | 10 | | 45 | 3 | | 46 | 8 | | 47 | 7 | | 48 | 7 | | 49 | 9 |
| |
| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6159420289855072 | | totalSentences | 138 | | uniqueOpeners | 85 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 119 | | matches | | 0 | "She had picked up his" | | 1 | "Her stakeout pint sat abandoned" | | 2 | "He didn't flinch, darted past" | | 3 | "She cleared it cleaner, sharp" | | 4 | "He caught balance on a" | | 5 | "He whipped around, elbow cracking" | | 6 | "He bolted straighter, toward the" | | 7 | "They hit Inverness Street's flank." | | 8 | "Her holster dug hip." | | 9 | "He vaulted a stall counter," | | 10 | "She hurdled after, wood cracking" | | 11 | "He gripped, hauled up with" | | 12 | "She climbed, muscles corded, boots" | | 13 | "He sprinted across cracked concrete," | | 14 | "He flashed it at a" | | 15 | "He descended, token vanishing into" | | 16 | "Her pulse thundered ears." | | 17 | "She edged forward, boot on" | | 18 | "She inhaled deep, ozone thick." | | 19 | "Her boot froze mid-air." |
| | ratio | 0.176 | |
| 77.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 119 | | matches | | 0 | "Rain lashed Camden's crooked streets," | | 1 | "Detective Harlow Quinn pounded pavement," | | 2 | "Neon from shuttered kebab shops" | | 3 | "She had picked up his" | | 4 | "Her stakeout pint sat abandoned" | | 5 | "Harlow's legs burned from the" | | 6 | "Salt-and-pepper hair clung cropped to" | | 7 | "Brown eyes locked on his" | | 8 | "Wind swallowed her bark." | | 9 | "He didn't flinch, darted past" | | 10 | "Soho's pulse faded behind; Camden's" | | 11 | "Tomás vaulted a low wall," | | 12 | "She cleared it cleaner, sharp" | | 13 | "Crowd thinned at this hour—drunks" | | 14 | "Harlow shouldered him aside; he" | | 15 | "Street narrowed to alleys, walls" | | 16 | "Rain drummed bin lids like" | | 17 | "Tomás slipped on cobblestones glazed" | | 18 | "He caught balance on a" | | 19 | "Fingers brushed his coat hem." |
| | ratio | 0.765 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn pounded pavement, coat heavy with water, boots churning spray that soaked her trousers to the knees." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |