| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1509 | | 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) | |
| 40.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1509 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "fractured" | | 1 | "perfect" | | 2 | "chill" | | 3 | "silence" | | 4 | "footsteps" | | 5 | "gloom" | | 6 | "flicker" | | 7 | "depths" | | 8 | "rhythmic" | | 9 | "velvet" | | 10 | "raced" | | 11 | "standard" | | 12 | "echoed" | | 13 | "predator" | | 14 | "marble" | | 15 | "weight" | | 16 | "whisper" | | 17 | "scanning" |
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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 | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1507 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 3 | | matches | | 0 | "Token, he said." | | 1 | "Metropolitan Police, she said." | | 2 | "The toll is memory, he said." |
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| 93.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1507 | | uniqueNames | 20 | | maxNameDensity | 1.13 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 17 | | Camden | 1 | | High | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Tube | 1 | | Underground | 1 | | Morris | 5 | | Veil | 2 | | Market | 1 | | Tomás | 1 | | Herrera | 2 | | Detective | 3 | | Saint | 1 | | Christopher | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Detective" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Raven" | | 5 | "Soho" | | 6 | "Veil" |
| | globalScore | 0.936 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like chanting, or perhaps a langua" | | 1 | "sounded like grinding stones" |
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| 67.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.327 | | wordCount | 1507 | | matches | | 0 | "Not of men, but of something older" | | 1 | "not for a token, but for her identification" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 48.61 | | std | 24.35 | | cv | 0.501 | | sampleLengths | | 0 | 85 | | 1 | 72 | | 2 | 75 | | 3 | 63 | | 4 | 62 | | 5 | 39 | | 6 | 113 | | 7 | 39 | | 8 | 42 | | 9 | 68 | | 10 | 59 | | 11 | 15 | | 12 | 77 | | 13 | 37 | | 14 | 79 | | 15 | 32 | | 16 | 38 | | 17 | 48 | | 18 | 47 | | 19 | 55 | | 20 | 9 | | 21 | 35 | | 22 | 22 | | 23 | 62 | | 24 | 33 | | 25 | 30 | | 26 | 4 | | 27 | 26 | | 28 | 24 | | 29 | 41 | | 30 | 76 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 152 | | matches | | 0 | "been closed" | | 1 | "was carved" |
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| 97.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 259 | | matches | | 0 | "wasn't already running" | | 1 | "was nearing" | | 2 | "was holding" | | 3 | "was entering" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 152 | | ratio | 0.007 | | matches | | 0 | "The city noise—the distant hum of traffic, the rattle of a late-night train—faded into a strange silence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1513 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.021810971579643092 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005287508261731659 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 9.91 | | std | 5.57 | | cv | 0.561 | | sampleLengths | | 0 | 17 | | 1 | 25 | | 2 | 16 | | 3 | 8 | | 4 | 5 | | 5 | 11 | | 6 | 3 | | 7 | 17 | | 8 | 17 | | 9 | 4 | | 10 | 11 | | 11 | 9 | | 12 | 14 | | 13 | 12 | | 14 | 24 | | 15 | 9 | | 16 | 4 | | 17 | 18 | | 18 | 4 | | 19 | 4 | | 20 | 8 | | 21 | 18 | | 22 | 9 | | 23 | 17 | | 24 | 7 | | 25 | 4 | | 26 | 18 | | 27 | 9 | | 28 | 19 | | 29 | 8 | | 30 | 8 | | 31 | 10 | | 32 | 17 | | 33 | 12 | | 34 | 11 | | 35 | 13 | | 36 | 6 | | 37 | 16 | | 38 | 13 | | 39 | 13 | | 40 | 10 | | 41 | 7 | | 42 | 3 | | 43 | 13 | | 44 | 1 | | 45 | 7 | | 46 | 9 | | 47 | 7 | | 48 | 6 | | 49 | 6 |
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| 43.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3355263157894737 | | totalSentences | 152 | | uniqueOpeners | 51 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 148 | | matches | | 0 | "Too late for decent people," | | 1 | "Then tell me what the" |
| | ratio | 0.014 | |
| 74.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 148 | | matches | | 0 | "She checked her left wrist," | | 1 | "She kept her center of" | | 2 | "She had picked up the" | | 3 | "She ignored the chill." | | 4 | "She pressed her back against" | | 5 | "She stepped around the corner" | | 6 | "It didn't smell of stale" | | 7 | "It smelled of ozone, dried" | | 8 | "She glanced at her watch" | | 9 | "They had found his badge" | | 10 | "She had seen the look" | | 11 | "She crept closer to the" | | 12 | "It wasn't a squat." | | 13 | "It was a market." | | 14 | "They presented it to a" | | 15 | "Her mind raced through the" | | 16 | "She had seen the name" | | 17 | "They must have already descended." | | 18 | "She stood at the threshold" | | 19 | "Her training screamed at her" |
| | ratio | 0.365 | |
| 68.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 148 | | matches | | 0 | "Detective Harlow Quinn pushed through" | | 1 | "She checked her left wrist," | | 2 | "The luminous hands glowed faintly" | | 3 | "The figure was slight, moving" | | 4 | "Quinn did not shout." | | 5 | "Shouting wasted breath and alerted" | | 6 | "She kept her center of" | | 7 | "She had picked up the" | | 8 | "The distinctive green neon sign" | | 9 | "The rain soaked through her" | | 10 | "She ignored the chill." | | 11 | "The suspect vanished behind a" | | 12 | "Quinn slowed as she approached" | | 13 | "She pressed her back against" | | 14 | "The city noise—the distant hum" | | 15 | "She stepped around the corner" | | 16 | "Graffiti scarred the tiled walls," | | 17 | "This station had been closed" | | 18 | "It didn't smell of stale" | | 19 | "It smelled of ozone, dried" |
| | ratio | 0.784 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 148 | | matches | | 0 | "Now, miles north, the chase" | | 1 | "If this was where Morris" | | 2 | "If she showed hesitation, they" | | 3 | "If she turned back now," | | 4 | "If she pushed forward, she" |
| | ratio | 0.034 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective 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 | "The rain soaked through her jacket, clinging to her closely cropped salt-and-pepper hair, matting it against her scalp." | | 2 | "Whispers of an underground economy dealing in things that shouldn't exist." | | 3 | "A slow smile spread across his face, revealing teeth that were too sharp." | | 4 | "She walked into the crowd, her eyes scanning the stalls, hunting for the suspect, hunting for the ghost of her partner, hunting for the end of the line." |
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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 | |