| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0.091 | | effectiveRatio | 0.08 | |
| 96.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1341 | | totalAiIsmAdverbs | 1 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1341 | | totalAiIsms | 30 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "scanning" | | 1 | "flickered" | | 2 | "whisper" | | 3 | "etched" | | 4 | "pounding" | | 5 | "glinting" | | 6 | "echoed" | | 7 | "weight" | | 8 | "tapestry" | | 9 | "reminder" | | 10 | "pulse" | | 11 | "echo" | | 12 | "fleeting" | | 13 | "raced" | | 14 | "predator" | | 15 | "pulsed" | | 16 | "could feel" | | 17 | "unravel" | | 18 | "glint" | | 19 | "flicked" | | 20 | "echoing" | | 21 | "potential" |
| |
| 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 | 77 | | matches | (empty) | |
| 50.09% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1331 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.37% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1045 | | uniqueNames | 11 | | maxNameDensity | 1.05 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 11 | | Quinn | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 | | Tomás | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" |
| | places | (empty) | | globalScore | 0.974 | | windowScore | 1 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 3 | | matches | | 0 | "silhouettes that seemed to watch her pass" | | 1 | "seemed deeper than any alley" | | 2 | "sound that seemed to vibrate the lanterns" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1331 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 99.03% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 38.03 | | std | 18.89 | | cv | 0.497 | | sampleLengths | | 0 | 88 | | 1 | 68 | | 2 | 43 | | 3 | 48 | | 4 | 58 | | 5 | 67 | | 6 | 57 | | 7 | 32 | | 8 | 35 | | 9 | 7 | | 10 | 48 | | 11 | 30 | | 12 | 9 | | 13 | 37 | | 14 | 21 | | 15 | 24 | | 16 | 57 | | 17 | 14 | | 18 | 33 | | 19 | 69 | | 20 | 17 | | 21 | 35 | | 22 | 36 | | 23 | 16 | | 24 | 39 | | 25 | 31 | | 26 | 18 | | 27 | 26 | | 28 | 29 | | 29 | 38 | | 30 | 35 | | 31 | 26 | | 32 | 70 | | 33 | 43 | | 34 | 27 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 77 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 181 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 92 | | ratio | 0.087 | | matches | | 0 | "A flash of green caught her peripheral—The Raven’s Nest’s neon sign flickered, sputtering against the downpour." | | 1 | "Her mind slipped back to the case file on her desk: DS Morris’s death, a shadowed whisper of something beyond the ordinary, the same symbol etched on the suspect’s coat—a stylized raven clawed into the hem." | | 2 | "She heard a hollow bang—metal on metal—and the distant clang of a door sliding shut." | | 3 | "The world above—cars, neon, rain—fell away." | | 4 | "She could see the suspect’s silhouette leaning against a table heavy with trinkets—brass compasses that spun without direction, vials labeled in languages she didn’t recognize." | | 5 | "The Veil Market pulsed like a living organism—its stalls breathing, vendors whispering funds in exchange for secrets." | | 6 | "The suspect lifted a slender object from the table—a silver locket etched with the same raven claw motif as his coat." | | 7 | "A faint metallic clang rang—a digital lock clicking somewhere deeper." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 902 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.019955654101995565 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0022172949002217295 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 14.47 | | std | 6.37 | | cv | 0.44 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 17 | | 3 | 30 | | 4 | 16 | | 5 | 13 | | 6 | 3 | | 7 | 36 | | 8 | 19 | | 9 | 11 | | 10 | 13 | | 11 | 9 | | 12 | 4 | | 13 | 19 | | 14 | 16 | | 15 | 24 | | 16 | 18 | | 17 | 16 | | 18 | 14 | | 19 | 15 | | 20 | 6 | | 21 | 12 | | 22 | 20 | | 23 | 15 | | 24 | 25 | | 25 | 17 | | 26 | 20 | | 27 | 12 | | 28 | 21 | | 29 | 14 | | 30 | 2 | | 31 | 5 | | 32 | 6 | | 33 | 11 | | 34 | 16 | | 35 | 15 | | 36 | 15 | | 37 | 15 | | 38 | 4 | | 39 | 5 | | 40 | 11 | | 41 | 26 | | 42 | 9 | | 43 | 12 | | 44 | 14 | | 45 | 10 | | 46 | 9 | | 47 | 13 | | 48 | 21 | | 49 | 14 |
| |
| 38.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.29347826086956524 | | totalSentences | 92 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 75 | | matches | | 0 | "She moved with military precision," | | 1 | "She didn’t pause." | | 2 | "Her mind slipped back to" | | 3 | "She ducked, the edge of" | | 4 | "she barked, voice cutting through" | | 5 | "He slipped past a rusted" | | 6 | "She caught a glimpse of" | | 7 | "She heard a hollow bang—metal" | | 8 | "She emerged into an underground" | | 9 | "She could see the suspect’s" | | 10 | "He turned slowly, eyes hidden" | | 11 | "Her hand hovered near her" | | 12 | "His medallion clinked against his" | | 13 | "She had spent years tracking" | | 14 | "She could feel the weight" | | 15 | "she asked, eyes on the" | | 16 | "He shrugged, his hands tightening" | | 17 | "He held it up, the" | | 18 | "he offered, voice smooth as" | | 19 | "he said, voice low, almost" |
| | ratio | 0.333 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 75 | | matches | | 0 | "Detective Harlow Quinn pressed her" | | 1 | "The scent of ozone mingled" | | 2 | "She moved with military precision," | | 3 | "A flash of green caught" | | 4 | "The bar’s windows glimmered, reflecting" | | 5 | "She didn’t pause." | | 6 | "Her mind slipped back to" | | 7 | "A soft whine rose from" | | 8 | "She ducked, the edge of" | | 9 | "A figure darted into a" | | 10 | "she barked, voice cutting through" | | 11 | "The suspect didn’t answer." | | 12 | "He slipped past a rusted" | | 13 | "Harlow sprinted after, her breath" | | 14 | "She caught a glimpse of" | | 15 | "The suspect pushed through a" | | 16 | "Harlow followed, feeling the wet" | | 17 | "A low, humming thrum rose" | | 18 | "She heard a hollow bang—metal" | | 19 | "The world above—cars, neon, rain—fell" |
| | ratio | 0.987 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 50.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 7 | | matches | | 0 | "She moved with military precision, boots splashing through shallow pools, eyes scanning the gray blur for any sign of the figure that had slipped through the al…" | | 1 | "The bar’s windows glimmered, reflecting rain-soaked silhouettes that seemed to watch her pass." | | 2 | "The suspect pushed through a gap between two dumpsters, disappearing into darkness that seemed deeper than any alley." | | 3 | "Lanterns hung from arches, casting long shadows across stalls lined with curios and glass jars that held misty, unidentifiable substances." | | 4 | "She could see the suspect’s silhouette leaning against a table heavy with trinkets—brass compasses that spun without direction, vials labeled in languages she d…" | | 5 | "The suspect stepped back, his coat brushing the rim of a table, sending a cascade of luminous powders into the air." | | 6 | "She had spent years tracking a single thread, the phantom footprints of a predator that devoured her partner." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 4 | | matches | | 0 | "she barked, voice cutting through the drumming rain" | | 1 | "Tomás said, voice low, eyes flicking to the suspect’s face" | | 2 | "he offered, voice smooth as oil" | | 3 | "he said, voice low, almost pleading" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 4 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "he whispered (whisper)" | | 2 | "Tomás shouted (shout)" | | 3 | "Tomás muttered (mutter)" |
| | dialogueSentences | 25 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.5 | | effectiveRatio | 0.32 | |