| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 48 | | adverbTagCount | 8 | | adverbTags | | 0 | "Eva reached slowly [slowly]" | | 1 | "Harlow corrected smoothly [smoothly]" | | 2 | "Eva's eyes flicked quickly [quickly]" | | 3 | "Harlow's gaze drifted back [back]" | | 4 | "Eva's fingers tightened around [around]" | | 5 | "It points further [further]" | | 6 | "Eva corrected smoothly [smoothly]" | | 7 | "Eva nodded once [once]" |
| | dialogueSentences | 80 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 76.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1691 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "slowly" | | 1 | "perfectly" | | 2 | "carefully" | | 3 | "very" | | 4 | "quickly" | | 5 | "nervously" | | 6 | "sharply" |
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| 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) | |
| 37.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1691 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "shattered" | | 1 | "standard" | | 2 | "imposing" | | 3 | "oppressive" | | 4 | "gloom" | | 5 | "glint" | | 6 | "weight" | | 7 | "navigating" | | 8 | "calculated" | | 9 | "flicked" | | 10 | "etched" | | 11 | "silence" | | 12 | "measured" | | 13 | "clandestine" | | 14 | "aligned" | | 15 | "unwavering" | | 16 | "echoing" | | 17 | "vibrated" | | 18 | "comforting" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 109 | | matches | | 0 | "h with confusion" | | 1 | "felt a prickle" |
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| 77.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 109 | | filterMatches | | 0 | "see" | | 1 | "think" | | 2 | "know" | | 3 | "watch" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1691 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1222 | | uniqueNames | 19 | | maxNameDensity | 1.88 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Kowalski | 3 | | Davies | 8 | | Harlow | 23 | | Camden | 2 | | Eva | 14 | | Veil | 4 | | Blacklisted | 1 | | Morris | 2 | | Metropolitan | 1 | | Police | 1 | | Researching | 1 | | Market | 1 | | Detective | 1 | | London | 3 | | Kept | 1 | | You | 6 | | Prove | 1 | | Tell | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Kowalski" | | 2 | "Davies" | | 3 | "Harlow" | | 4 | "Eva" | | 5 | "Veil" | | 6 | "Morris" | | 7 | "Police" | | 8 | "You" |
| | places | | | globalScore | 0.559 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1691 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 28.18 | | std | 17.26 | | cv | 0.612 | | sampleLengths | | 0 | 5 | | 1 | 34 | | 2 | 26 | | 3 | 7 | | 4 | 41 | | 5 | 29 | | 6 | 2 | | 7 | 45 | | 8 | 14 | | 9 | 32 | | 10 | 20 | | 11 | 48 | | 12 | 39 | | 13 | 2 | | 14 | 26 | | 15 | 11 | | 16 | 37 | | 17 | 49 | | 18 | 31 | | 19 | 31 | | 20 | 27 | | 21 | 6 | | 22 | 35 | | 23 | 11 | | 24 | 57 | | 25 | 25 | | 26 | 53 | | 27 | 10 | | 28 | 30 | | 29 | 7 | | 30 | 37 | | 31 | 48 | | 32 | 26 | | 33 | 18 | | 34 | 52 | | 35 | 22 | | 36 | 43 | | 37 | 53 | | 38 | 35 | | 39 | 50 | | 40 | 34 | | 41 | 35 | | 42 | 11 | | 43 | 38 | | 44 | 23 | | 45 | 2 | | 46 | 57 | | 47 | 56 | | 48 | 38 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 109 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 211 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 123 | | ratio | 0.008 | | matches | | 0 | "Harlow's military-trained mind instantly catalogued the threat level. A clandestine network, operating supernatural contraband, actively executing informants, and now possessing an active portal in the heart of the abandoned Camden station. The pieces aligned with terrifying clarity. The clique wasn't just a dark rumour; they were an organised crime syndicate with resources that defied conventional policing." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 736 | | adjectiveStacks | 2 | | stackExamples | | 0 | "controlled controlled targeted clique" | | 1 | "same rigid, calculated precision" |
| | adverbCount | 15 | | adverbRatio | 0.020380434782608696 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008152173913043478 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 13.75 | | std | 11.72 | | cv | 0.852 | | sampleLengths | | 0 | 5 | | 1 | 8 | | 2 | 13 | | 3 | 13 | | 4 | 4 | | 5 | 22 | | 6 | 7 | | 7 | 37 | | 8 | 4 | | 9 | 17 | | 10 | 4 | | 11 | 8 | | 12 | 2 | | 13 | 45 | | 14 | 4 | | 15 | 5 | | 16 | 5 | | 17 | 25 | | 18 | 7 | | 19 | 16 | | 20 | 4 | | 21 | 35 | | 22 | 13 | | 23 | 39 | | 24 | 2 | | 25 | 15 | | 26 | 5 | | 27 | 3 | | 28 | 3 | | 29 | 11 | | 30 | 28 | | 31 | 6 | | 32 | 3 | | 33 | 21 | | 34 | 12 | | 35 | 11 | | 36 | 5 | | 37 | 31 | | 38 | 8 | | 39 | 10 | | 40 | 13 | | 41 | 27 | | 42 | 6 | | 43 | 15 | | 44 | 4 | | 45 | 6 | | 46 | 10 | | 47 | 11 | | 48 | 3 | | 49 | 36 |
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| 60.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4146341463414634 | | totalSentences | 123 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 105 | | matches | | 0 | "Just a dead junkie." | | 1 | "Probably bled out from a" | | 2 | "Then they made an" | | 3 | "Just like they cleaned up" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 105 | | matches | | 0 | "Her boots made a sharp" | | 1 | "She adjusted her round glasses" | | 2 | "I see a cleanup" | | 3 | "They don't butcher men in" | | 4 | "You lost your partner three" | | 5 | "I lost him to a" | | 6 | "She stood up, her movement" | | 7 | "It was a key." | | 8 | "It's a crossroads for forbidden" | | 9 | "I think this is a" | | 10 | "it's etched with binding runes." | | 11 | "He tried to leave." | | 12 | "They hunted him" | | 13 | "You moved to London two" | | 14 | "You know these symbols." | | 15 | "You've been watching" | | 16 | "It kept my grandmother alive," | | 17 | "He found a dead end" | | 18 | "It points further down the" | | 19 | "it was the price of" |
| | ratio | 0.238 | |
| 64.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 105 | | matches | | 0 | "Harlow Quinn stepped past the" | | 1 | "Her boots made a sharp" | | 2 | "The stagnant air exhaled a" | | 3 | "Eva Kowalski didn't flinch." | | 4 | "She adjusted her round glasses" | | 5 | "Harlow's imposing secure the perimeter" | | 6 | "Nothing leaves this" | | 7 | "PC Davies lowered his torch," | | 8 | "Harlow walked to the centre" | | 9 | "Kowalski sees a sacrificial altar." | | 10 | "I see a cleanup" | | 11 | "Eva shifted the weight of" | | 12 | "A regular down in the" | | 13 | "Davies's voice pitched high with" | | 14 | "Eva reached slowly into the" | | 15 | "Harlow's sharp jaw tightened. She" | | 16 | "Harlow ran a gloved finger" | | 17 | "Eva's bright green eyes widened" | | 18 | "Harlow corrected smoothly, her brown" | | 19 | "They don't butcher men in" |
| | ratio | 0.79 | |
| 47.62% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 105 | | matches | | 0 | "If you value your continued" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow's military-trained mind instantly catalogued the threat level. A clandestine network, operating supernatural contraband, actively executing informants, a…" |
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| 59.21% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 38 | | uselessAdditionCount | 5 | | matches | | 0 | "Harlow corrected smoothly, her brown eyes narrowing in the dim light" | | 1 | "Eva whispered, her voice dropping to a hushed, terrified register" | | 2 | "Harlow stepped, her tall stature overwhelming in the confined space" | | 3 | "Eva corrected smoothly, her voice devoid of tremor" | | 4 | "Harlow ordered, her voice commanding absolute, unwavering loyalty" |
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| 62.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 7 | | fancyTags | | 0 | "Harlow corrected smoothly (correct)" | | 1 | "Davies laughed (laugh)" | | 2 | "Eva whispered (whisper)" | | 3 | "Eva corrected smoothly (correct)" | | 4 | "a raw terror she refused (refuse)" | | 5 | "Harlow ordered (order)" | | 6 | "Davies protested (protest)" |
| | dialogueSentences | 80 | | tagDensity | 0.088 | | leniency | 0.175 | | rawRatio | 1 | | effectiveRatio | 0.175 | |