| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.409 | | leniency | 0.818 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1524 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 34.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1524 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "fractured" | | 1 | "gleaming" | | 2 | "calculating" | | 3 | "familiar" | | 4 | "gloom" | | 5 | "echoed" | | 6 | "velvet" | | 7 | "etched" | | 8 | "intricate" | | 9 | "flicked" | | 10 | "weight" | | 11 | "could feel" | | 12 | "tension" | | 13 | "glinting" | | 14 | "whisper" | | 15 | "unreadable" | | 16 | "racing" | | 17 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1516 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1299 | | uniqueNames | 16 | | maxNameDensity | 0.77 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 10 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | Transport | 1 | | London | 1 | | Tube | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Elena | 1 | | Herrera | 6 | | Rookery | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Elena" | | 6 | "Herrera" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Camden" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 3 | | matches | | 0 | "blackness that seemed to eat the weak light from the street" | | 1 | "looked like suspended insects that moved" | | 2 | "looked like a clean, deep knife cut" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1516 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 130 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 39.89 | | std | 24.24 | | cv | 0.608 | | sampleLengths | | 0 | 57 | | 1 | 19 | | 2 | 88 | | 3 | 38 | | 4 | 78 | | 5 | 6 | | 6 | 59 | | 7 | 87 | | 8 | 14 | | 9 | 55 | | 10 | 40 | | 11 | 57 | | 12 | 90 | | 13 | 51 | | 14 | 56 | | 15 | 43 | | 16 | 13 | | 17 | 18 | | 18 | 32 | | 19 | 20 | | 20 | 30 | | 21 | 4 | | 22 | 77 | | 23 | 13 | | 24 | 9 | | 25 | 57 | | 26 | 15 | | 27 | 40 | | 28 | 12 | | 29 | 37 | | 30 | 49 | | 31 | 52 | | 32 | 8 | | 33 | 47 | | 34 | 54 | | 35 | 46 | | 36 | 17 | | 37 | 28 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 120 | | matches | | 0 | "was lost" | | 1 | "was covered" | | 2 | "was lost" | | 3 | "was holstered" | | 4 | "were rewritten" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 205 | | matches | | 0 | "was heading" | | 1 | "was still running" | | 2 | "were screaming" |
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| 32.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 130 | | ratio | 0.038 | | matches | | 0 | "Twenty feet ahead, the suspect—a wiry silhouette in a grey hoodie—slipped and skidded on the gleaming wet asphalt." | | 1 | "She’d been on a quiet surveillance detail, sipping a lukewarm coffee in her unmarked car, when the face from the photo matches—a low-level fence she’d been tracking for weeks—had walked right past her window." | | 2 | "The sound of his flight—scraping boots, a dislodged stone—faded downward." | | 3 | "The smell of rain-washed asphalt gave way to something older—damp concrete, mineral dust, and a faint, cloying sweetness like rotting flowers mixed with ozone." | | 4 | "The rain, the chase, the impossible market—it all receded to the edges of a cold, sharp point: the name." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1314 | | adjectiveStacks | 2 | | stackExamples | | 0 | "narrow, brick-lined service" | | 1 | "short curly dark hair" |
| | adverbCount | 27 | | adverbRatio | 0.02054794520547945 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0045662100456621 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 11.66 | | std | 7.52 | | cv | 0.645 | | sampleLengths | | 0 | 15 | | 1 | 24 | | 2 | 18 | | 3 | 15 | | 4 | 4 | | 5 | 22 | | 6 | 34 | | 7 | 7 | | 8 | 25 | | 9 | 4 | | 10 | 4 | | 11 | 11 | | 12 | 6 | | 13 | 3 | | 14 | 10 | | 15 | 12 | | 16 | 8 | | 17 | 10 | | 18 | 15 | | 19 | 24 | | 20 | 9 | | 21 | 6 | | 22 | 29 | | 23 | 13 | | 24 | 5 | | 25 | 2 | | 26 | 10 | | 27 | 12 | | 28 | 3 | | 29 | 2 | | 30 | 3 | | 31 | 9 | | 32 | 9 | | 33 | 16 | | 34 | 22 | | 35 | 11 | | 36 | 14 | | 37 | 3 | | 38 | 24 | | 39 | 9 | | 40 | 19 | | 41 | 7 | | 42 | 4 | | 43 | 9 | | 44 | 3 | | 45 | 3 | | 46 | 4 | | 47 | 9 | | 48 | 1 | | 49 | 8 |
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| 43.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.33076923076923076 | | totalSentences | 130 | | uniqueOpeners | 43 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 116 | | matches | | 0 | "Then a new voice cut" | | 1 | "Then she gave a single," |
| | ratio | 0.017 | |
| 78.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 116 | | matches | | 0 | "Her voice was a ragged" | | 1 | "He didn’t look back." | | 2 | "He’d darted out of the" | | 3 | "She’d been on a quiet" | | 4 | "He was younger, faster." | | 5 | "She paced her breathing, her" | | 6 | "He was heading north, towards" | | 7 | "He cut left, diving down" | | 8 | "Her suspect sprinted past a" | | 9 | "she hissed under her breath" | | 10 | "He yanked a rusted grate" | | 11 | "She didn’t draw it yet." | | 12 | "Her breath fogged in the" | | 13 | "Her partner, DS Morris, had" | | 14 | "She drew her sidearm, the" | | 15 | "It stretched into the gloom," | | 16 | "He was still running." | | 17 | "She moved forward, her back" | | 18 | "She strained for sound." | | 19 | "She followed it into a" |
| | ratio | 0.353 | |
| 41.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 116 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Harlow Quinn’s worn leather watch" | | 2 | "Her voice was a ragged" | | 3 | "He didn’t look back." | | 4 | "He’d darted out of the" | | 5 | "She’d been on a quiet" | | 6 | "The chase was instinct, brutal" | | 7 | "He was younger, faster." | | 8 | "She paced her breathing, her" | | 9 | "He was heading north, towards" | | 10 | "A stupid move." | | 11 | "He cut left, diving down" | | 12 | "Quinn followed, her footfalls slapping" | | 13 | "The alley spat them out" | | 14 | "The market stalls were skeletal" | | 15 | "Her suspect sprinted past a" | | 16 | "A maintenance access point for" | | 17 | "she hissed under her breath" | | 18 | "He yanked a rusted grate" | | 19 | "Quinn reached the opening, her" |
| | ratio | 0.836 | |
| 43.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 116 | | matches | | 0 | "Now the city blurred around" |
| | ratio | 0.009 | |
| 46.70% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 7 | | matches | | 0 | "He yanked a rusted grate aside and disappeared down a steep flight of concrete steps, swallowed by a blackness that seemed to eat the weak light from the street…" | | 1 | "It stretched into the gloom, lit at erratic intervals by failing utility bulbs that cast more shadow than light." | | 2 | "Stalls crafted from old railway sleepers and draped in velvet held glowing vials, jars of what looked like suspended insects that moved with their own inner lig…" | | 3 | "The suspect vaulted over a chain blocking the defunct platform edge and dropped onto the tracks, landing with a splash in the shallow, stagnant water that colle…" | | 4 | "He scrambled up the far side and headed for a section of the tunnel wall that was covered in a thick, black tarp." | | 5 | "A man detached himself from the shadows near one of the larger stalls, which sold what appeared to be anatomical diagrams that moved." | | 6 | "He was in his late twenties, with short curly dark hair and warm brown eyes that assessed her with an unnerving stillness." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 3 | | matches | | 0 | "the woman said, her voice a gravelly rumble" | | 1 | "she began, her voice barely a whisper" | | 2 | "Herrera said, a ghost of a smile touching his lips" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 22 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0.2 | | effectiveRatio | 0.091 | |