| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 3 | | adverbTags | | 0 | "Harlow said immediately [immediately]" | | 1 | "Each repetition grew more [more]" | | 2 | "she said tightly [tightly]" |
| | dialogueSentences | 72 | | tagDensity | 0.361 | | leniency | 0.722 | | rawRatio | 0.115 | | effectiveRatio | 0.083 | |
| 86.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2908 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "sharply" | | 1 | "slowly" | | 2 | "perfectly" | | 3 | "softly" | | 4 | "quickly" | | 5 | "really" | | 6 | "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) | |
| 79.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2908 | | totalAiIsms | 12 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "the last thing" | | count | 1 |
| | 11 | |
| | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "flicked" | | 3 | "pulsed" | | 4 | "jaw clenched" | | 5 | "quickened" | | 6 | "racing" | | 7 | "flickered" | | 8 | "pounding" | | 9 | "echo" | | 10 | "the last thing" | | 11 | "echoes" |
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| 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 | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 295 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 5 | | narrationSentences | 295 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 338 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 1 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0.003 | | matches | | 0 | "\"London Bridge is falling down, falling down, falling down. London Bridge is falling down, my fair lady.\" Each repetition grew more distorted." |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2908 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 2140 | | uniqueNames | 17 | | maxNameDensity | 0.89 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Soho | 1 | | Raven | 1 | | Nest | 2 | | Morris | 10 | | Tube | 1 | | Tomás | 15 | | Herrera | 1 | | Spanish-accented | 1 | | Saint | 2 | | Christopher | 2 | | Water | 1 | | London | 1 | | Harlow | 19 | | Spanish | 2 | | Stalls | 3 | | One | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Water" | | 9 | "Harlow" | | 10 | "Stalls" | | 11 | "One" |
| | places | | | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 170 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like tiny thunderstorms crackled s" | | 1 | "exits that seemed to shrink as they approached" | | 2 | "looked like after three years in whatever" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.344 | | wordCount | 2908 | | matches | | 0 | "not skin, but layers of overlapping memories" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 338 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 90 | | mean | 32.31 | | std | 16.66 | | cv | 0.516 | | sampleLengths | | 0 | 61 | | 1 | 37 | | 2 | 61 | | 3 | 62 | | 4 | 37 | | 5 | 30 | | 6 | 49 | | 7 | 50 | | 8 | 30 | | 9 | 52 | | 10 | 55 | | 11 | 7 | | 12 | 9 | | 13 | 32 | | 14 | 19 | | 15 | 27 | | 16 | 38 | | 17 | 46 | | 18 | 44 | | 19 | 35 | | 20 | 27 | | 21 | 56 | | 22 | 37 | | 23 | 13 | | 24 | 26 | | 25 | 58 | | 26 | 24 | | 27 | 25 | | 28 | 26 | | 29 | 48 | | 30 | 70 | | 31 | 42 | | 32 | 46 | | 33 | 57 | | 34 | 14 | | 35 | 24 | | 36 | 55 | | 37 | 28 | | 38 | 47 | | 39 | 16 | | 40 | 50 | | 41 | 27 | | 42 | 17 | | 43 | 45 | | 44 | 50 | | 45 | 39 | | 46 | 23 | | 47 | 16 | | 48 | 53 | | 49 | 41 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 295 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 393 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 338 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2148 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short curly dark hair." |
| | adverbCount | 80 | | adverbRatio | 0.037243947858473 | | lyAdverbCount | 27 | | lyAdverbRatio | 0.012569832402234637 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 338 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 338 | | mean | 8.6 | | std | 6.53 | | cv | 0.759 | | sampleLengths | | 0 | 18 | | 1 | 20 | | 2 | 23 | | 3 | 7 | | 4 | 6 | | 5 | 24 | | 6 | 9 | | 7 | 17 | | 8 | 10 | | 9 | 7 | | 10 | 18 | | 11 | 3 | | 12 | 5 | | 13 | 12 | | 14 | 14 | | 15 | 12 | | 16 | 11 | | 17 | 3 | | 18 | 2 | | 19 | 10 | | 20 | 11 | | 21 | 4 | | 22 | 2 | | 23 | 1 | | 24 | 9 | | 25 | 12 | | 26 | 1 | | 27 | 11 | | 28 | 5 | | 29 | 1 | | 30 | 5 | | 31 | 6 | | 32 | 19 | | 33 | 12 | | 34 | 1 | | 35 | 1 | | 36 | 1 | | 37 | 4 | | 38 | 5 | | 39 | 10 | | 40 | 9 | | 41 | 13 | | 42 | 3 | | 43 | 7 | | 44 | 3 | | 45 | 2 | | 46 | 13 | | 47 | 4 | | 48 | 1 | | 49 | 1 |
| |
| 59.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3875739644970414 | | totalSentences | 338 | | uniqueOpeners | 131 | |
| 92.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 251 | | matches | | 0 | "Then he turned toward the" | | 1 | "Only the woman's voice remained." | | 2 | "Much taller than she'd appeared." | | 3 | "No longer pretending to be" | | 4 | "Just teeth and hunger and" | | 5 | "Then she looked at the" | | 6 | "Then darkness swallowed them whole," |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 251 | | matches | | 0 | "she shouted, voice raw" | | 1 | "Her words dissolved into the" | | 2 | "She followed without hesitation, coat" | | 3 | "She vaulted after him, metal" | | 4 | "she muttered, closing the gap" | | 5 | "Her quarry ducked into a" | | 6 | "She slowed, hand instinctively reaching" | | 7 | "She'd left it charging back" | | 8 | "She clicked on her torch." | | 9 | "She blinked hard." | | 10 | "She approached slowly, boots scraping" | | 11 | "She'd caught a glimpse of" | | 12 | "She spun, baton raised, heart" | | 13 | "She kept the baton between" | | 14 | "He offered a slight smile" | | 15 | "She ignored it." | | 16 | "His gaze flicked to her" | | 17 | "She laughed once, sharp." | | 18 | "His jaw tightened." | | 19 | "She stepped closer, torch steady" |
| | ratio | 0.243 | |
| 61.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 200 | | totalSentences | 251 | | matches | | 0 | "Harlow Quinn's lungs burned as" | | 1 | "The suspect had bolted from" | | 2 | "she shouted, voice raw" | | 3 | "Her words dissolved into the" | | 4 | "The figure glanced back once," | | 5 | "She followed without hesitation, coat" | | 6 | "The leather watch on her" | | 7 | "The alley narrowed." | | 8 | "Puddles exploded under her strides." | | 9 | "She vaulted after him, metal" | | 10 | "Camden sprawled before her, its" | | 11 | "The green neon of the" | | 12 | "This felt wrong." | | 13 | "she muttered, closing the gap" | | 14 | "Her quarry ducked into a" | | 15 | "An old Tube station." | | 16 | "The sign above read CAMDEN" | | 17 | "She slowed, hand instinctively reaching" | | 18 | "She'd left it charging back" | | 19 | "The suspect's footsteps echoed downward." |
| | ratio | 0.797 | |
| 19.92% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 251 | | matches | | | ratio | 0.004 | |
| 76.19% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 7 | | matches | | 0 | "The beam cut through dust motes that danced like warnings." | | 1 | "The off-books medic who'd patched up too many wrong people in too many wrong places." | | 2 | "Lanterns hung along a subterranean arcade that stretched impossibly far." | | 3 | "Something that wore the shape of her dead partner but stretched too far in every direction." | | 4 | "Blood and fear and the sickening sweetness of something that should never have crossed over." | | 5 | "The former paramedic who'd lost everything to help people who weren't supposed to exist." | | 6 | "The man who'd carried her partner's final secret like a wound." |
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| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "she shouted, voice raw" | | 1 | "Tomás said, voice dropping" | | 2 | "she asked, voice low" |
| |
| 52.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 7 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she muttered (mutter)" | | 2 | "the woman croaked (croak)" | | 3 | "The old woman cackled (cackle)" | | 4 | "she gasped (gasp)" | | 5 | "she demanded (demand)" | | 6 | "it whispered (whisper)" |
| | dialogueSentences | 72 | | tagDensity | 0.153 | | leniency | 0.306 | | rawRatio | 0.636 | | effectiveRatio | 0.194 | |