| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "she replied curtly [curtly]" |
| | dialogueSentences | 20 | | tagDensity | 0.55 | | leniency | 1 | | rawRatio | 0.091 | | effectiveRatio | 0.091 | |
| 83.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1216 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "nervously" | | 1 | "cautiously" | | 2 | "slowly" | | 3 | "tightly" |
| |
| 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 | 1216 | | totalAiIsms | 30 | | found | | 0 | | | 1 | | | 2 | | word | "practiced ease" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | | | 24 | |
| | highlights | | 0 | "treacherous" | | 1 | "cacophony" | | 2 | "practiced ease" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "etched" | | 6 | "raced" | | 7 | "glint" | | 8 | "shimmered" | | 9 | "depths" | | 10 | "tracing" | | 11 | "glistening" | | 12 | "unspoken" | | 13 | "palpable" | | 14 | "tension" | | 15 | "scanned" | | 16 | "flickered" | | 17 | "beacon" | | 18 | "intricate" | | 19 | "pounding" | | 20 | "echoed" | | 21 | "racing" | | 22 | "sense of" | | 23 | "pulsed" | | 24 | "determined" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "eyes widened" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 88 | | matches | | 0 | "e with fear" | | 1 | "d with determination" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1213 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 45.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1105 | | uniqueNames | 12 | | maxNameDensity | 2.08 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 23 | | Soho | 1 | | Morris | 5 | | Veil | 4 | | Market | 6 | | Herrera | 3 | | Spanish | 1 | | Tube | 2 | | Camden | 2 | | Raven | 1 | | Nest | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Nest" |
| | places | | | globalScore | 0.459 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.824 | | wordCount | 1213 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 97 | | matches | | |
| 90.68% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 35.68 | | std | 16.67 | | cv | 0.467 | | sampleLengths | | 0 | 69 | | 1 | 51 | | 2 | 59 | | 3 | 56 | | 4 | 17 | | 5 | 41 | | 6 | 65 | | 7 | 36 | | 8 | 17 | | 9 | 13 | | 10 | 16 | | 11 | 16 | | 12 | 37 | | 13 | 35 | | 14 | 51 | | 15 | 44 | | 16 | 33 | | 17 | 22 | | 18 | 45 | | 19 | 47 | | 20 | 39 | | 21 | 31 | | 22 | 12 | | 23 | 27 | | 24 | 14 | | 25 | 22 | | 26 | 6 | | 27 | 45 | | 28 | 12 | | 29 | 48 | | 30 | 45 | | 31 | 47 | | 32 | 53 | | 33 | 42 |
| |
| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 88 | | matches | | 0 | "were lined" | | 1 | "was determined" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 197 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 97 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1108 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.021660649819494584 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.010830324909747292 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 12.51 | | std | 5.01 | | cv | 0.4 | | sampleLengths | | 0 | 22 | | 1 | 17 | | 2 | 11 | | 3 | 19 | | 4 | 17 | | 5 | 18 | | 6 | 16 | | 7 | 16 | | 8 | 13 | | 9 | 6 | | 10 | 24 | | 11 | 10 | | 12 | 14 | | 13 | 13 | | 14 | 19 | | 15 | 10 | | 16 | 7 | | 17 | 11 | | 18 | 12 | | 19 | 9 | | 20 | 9 | | 21 | 5 | | 22 | 20 | | 23 | 17 | | 24 | 13 | | 25 | 10 | | 26 | 16 | | 27 | 20 | | 28 | 17 | | 29 | 6 | | 30 | 7 | | 31 | 3 | | 32 | 13 | | 33 | 9 | | 34 | 7 | | 35 | 8 | | 36 | 16 | | 37 | 13 | | 38 | 7 | | 39 | 16 | | 40 | 12 | | 41 | 18 | | 42 | 15 | | 43 | 18 | | 44 | 9 | | 45 | 19 | | 46 | 16 | | 47 | 20 | | 48 | 13 | | 49 | 7 |
| |
| 39.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.31958762886597936 | | totalSentences | 97 | | uniqueOpeners | 31 | |
| 38.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 86 | | matches | | 0 | "Then, with a defiant glare," |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 86 | | matches | | 0 | "she shouted, her voice barely" | | 1 | "She couldn't let him slip" | | 2 | "He turned, desperation etched on" | | 3 | "she warned, her voice low" | | 4 | "He slipped through, and the" | | 5 | "She had never ventured into" | | 6 | "She approached the wall, her" | | 7 | "he asked, his Spanish accent" | | 8 | "she replied curtly" | | 9 | "she snapped, frustration bubbling to" | | 10 | "He reached into his pocket" | | 11 | "She pressed the token against" | | 12 | "She stepped through, the air" | | 13 | "She pushed through the throng" | | 14 | "Her focus remained on the" | | 15 | "She spotted him near a" | | 16 | "He bolted, knocking over a" | | 17 | "He held a small, ornate" | | 18 | "she ordered, her voice steady" | | 19 | "He placed the box on" |
| | ratio | 0.267 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 86 | | matches | | 0 | "Detective Harlow Quinn's boots pounded" | | 1 | "The suspect darted through the" | | 2 | "Rain lashed down, turning the" | | 3 | "Quinn's worn leather watch, a" | | 4 | "she shouted, her voice barely" | | 5 | "The figure glanced back, eyes" | | 6 | "Quinn followed, her military precision" | | 7 | "The courtyard was a maze" | | 8 | "The suspect weaved through the" | | 9 | "Quinn's sharp jawline tightened with" | | 10 | "She couldn't let him slip" | | 11 | "A flash of movement to" | | 12 | "The suspect had reached a" | | 13 | "He turned, desperation etched on" | | 14 | "Quinn's heart raced as she" | | 15 | "she warned, her voice low" | | 16 | "The suspect hesitated, his eyes" | | 17 | "The bricks shimmered and shifted," | | 18 | "He slipped through, and the" | | 19 | "Quinn cursed under her breath." |
| | ratio | 0.942 | |
| 58.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 86 | | matches | | 0 | "Before the suspect could answer," |
| | ratio | 0.012 | |
| 94.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 4 | | matches | | 0 | "A vendor with a face covered in intricate tattoos tried to catch her attention, waving a vial of glowing liquid." | | 1 | "She spotted him near a stall selling enchanted weapons, his eyes darting nervously." | | 2 | "The creature's eyes glowed with an unnatural light, its form shifting and writhing as it advanced." | | 3 | "The creature was unlike anything she had ever encountered, a manifestation of the supernatural forces that had taken Morris from her." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 4 | | matches | | 0 | "she warned, her voice low and steady" | | 1 | "he asked, his Spanish accent softening the edges of his words" | | 2 | "she snapped, frustration bubbling to the surface" | | 3 | "she ordered, her voice steady" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 9 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she warned (warn)" | | 2 | "she snapped (snap)" | | 3 | "Quinn muttered (mutter)" | | 4 | "she ordered (order)" | | 5 | "Quinn demanded (demand)" | | 6 | "he stammered (stammer)" | | 7 | "Quinn pressed (press)" | | 8 | "the suspect screamed (scream)" |
| | dialogueSentences | 20 | | tagDensity | 0.55 | | leniency | 1 | | rawRatio | 0.818 | | effectiveRatio | 0.818 | |