| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1113 | | totalAiIsmAdverbs | 2 | | 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) | |
| 46.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1113 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulse" | | 2 | "chill" | | 3 | "gloom" | | 4 | "silence" | | 5 | "navigated" | | 6 | "rhythmic" | | 7 | "churned" | | 8 | "ensuring" | | 9 | "silk" | | 10 | "resolve" | | 11 | "cacophony" |
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| 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 | 91 | | matches | (empty) | |
| 80.06% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1113 | | uniqueNames | 14 | | maxNameDensity | 0.81 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 2 | | Quinn | 2 | | Metropolitan | 2 | | Police | 2 | | Camden | 2 | | Lock | 1 | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Tube | 2 | | Harlow | 9 | | Morris | 4 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Raven" | | 3 | "Harlow" | | 4 | "Morris" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "Soho" | | 3 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "hum that seemed to vibrate in her teeth" | | 1 | "felt like warm silk" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.898 | | wordCount | 1113 | | matches | | 0 | "Not of men, but of something older" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 91 | | matches | | 0 | "watching that establishment" |
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| 75.58% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 79.5 | | std | 32.96 | | cv | 0.415 | | sampleLengths | | 0 | 84 | | 1 | 131 | | 2 | 97 | | 3 | 93 | | 4 | 18 | | 5 | 37 | | 6 | 127 | | 7 | 34 | | 8 | 81 | | 9 | 83 | | 10 | 88 | | 11 | 69 | | 12 | 55 | | 13 | 116 |
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| 97.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 91 | | matches | | 0 | "was saturated" | | 1 | "been boarded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 187 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 91 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1117 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.03491495076096687 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008057296329453895 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 12.23 | | std | 7.53 | | cv | 0.615 | | sampleLengths | | 0 | 18 | | 1 | 24 | | 2 | 20 | | 3 | 17 | | 4 | 3 | | 5 | 2 | | 6 | 22 | | 7 | 30 | | 8 | 13 | | 9 | 16 | | 10 | 26 | | 11 | 3 | | 12 | 21 | | 13 | 24 | | 14 | 4 | | 15 | 4 | | 16 | 13 | | 17 | 19 | | 18 | 7 | | 19 | 26 | | 20 | 16 | | 21 | 2 | | 22 | 23 | | 23 | 8 | | 24 | 32 | | 25 | 12 | | 26 | 9 | | 27 | 2 | | 28 | 5 | | 29 | 2 | | 30 | 14 | | 31 | 15 | | 32 | 8 | | 33 | 15 | | 34 | 20 | | 35 | 17 | | 36 | 13 | | 37 | 22 | | 38 | 4 | | 39 | 15 | | 40 | 13 | | 41 | 1 | | 42 | 7 | | 43 | 6 | | 44 | 13 | | 45 | 3 | | 46 | 2 | | 47 | 10 | | 48 | 3 | | 49 | 18 |
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| 46.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.31868131868131866 | | totalSentences | 91 | | uniqueOpeners | 29 | |
| 78.43% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 85 | | matches | | 0 | "Just a void that smelled" | | 1 | "Instead, her fingers met a" |
| | ratio | 0.024 | |
| 83.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 85 | | matches | | 0 | "She checked the time without" | | 1 | "She wiped water from her" | | 2 | "She kept her shoulders square," | | 3 | "She had been trailing him" | | 4 | "She had spent weeks watching" | | 5 | "She navigated around a group" | | 6 | "Her brown eyes locked onto" | | 7 | "He turned sharply off the" | | 8 | "She peeked around the edge" | | 9 | "He reached into his pocket" | | 10 | "He pressed it against the" | | 11 | "Her heart hammered against her" | | 12 | "He had chased a lead" | | 13 | "He never came back." | | 14 | "She had seen the look" | | 15 | "She looked down at the" | | 16 | "She had heard the name" | | 17 | "It felt heavy, useless." | | 18 | "She might find whatever took" | | 19 | "She glanced at her watch" |
| | ratio | 0.341 | |
| 83.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 85 | | matches | | 0 | "Harlow Quinn pushed through the" | | 1 | "She checked the time without" | | 2 | "The worn leather of her" | | 3 | "She wiped water from her" | | 4 | "She kept her shoulders square," | | 5 | "The suspect was ahead, a" | | 6 | "She had been trailing him" | | 7 | "The Raven's Nest." | | 8 | "She had spent weeks watching" | | 9 | "The suspect wore a hooded" | | 10 | "Harlow didn't call out." | | 11 | "The rain would swallow her" | | 12 | "She navigated around a group" | | 13 | "Her brown eyes locked onto" | | 14 | "He turned sharply off the" | | 15 | "Harlow slowed her pace as" | | 16 | "The city noise faded here," | | 17 | "She peeked around the edge" | | 18 | "The suspect stood before a" | | 19 | "He reached into his pocket" |
| | ratio | 0.753 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 85 | | matches | | 0 | "Even from twenty yards away," | | 1 | "If the rumors were true," | | 2 | "If she stepped through, she" | | 3 | "If this market was the" |
| | ratio | 0.047 | |
| 35.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 8 | | matches | | 0 | "Harlow Quinn pushed through the downpour, her breath coming in sharp, controlled bursts that matched the rhythm of her boots striking the wet concrete." | | 1 | "She wiped water from her eyes, her fingers brushing against the closely cropped salt-and-pepper hair that offered no protection from the storm." | | 2 | "At forty-one, her body felt every mile of the chase, but eighteen years on the Metropolitan Police force had drilled a military precision into her movements tha…" | | 3 | "She navigated around a group of late-night revelers huddled under an awning, their laughter dying as she shoved past." | | 4 | "He turned sharply off the main road, heading toward the skeletal remains of an old Tube station entrance that had been boarded up since the nineties." | | 5 | "She had heard the name whispered in interviews with informants who looked over their shoulders while they spoke." | | 6 | "The rain intensified, drumming harder against her jacket, urging her to seek cover." | | 7 | "The street behind her sealed up like a healing wound, leaving her standing in the dim light of an abandoned Tube station that was no longer abandoned." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |