| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1220 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 30.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1220 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "calibrated" | | 1 | "measured" | | 2 | "pulse" | | 3 | "flickered" | | 4 | "wavering" | | 5 | "gloom" | | 6 | "pulsed" | | 7 | "rhythmic" | | 8 | "gleaming" | | 9 | "unreadable" | | 10 | "echoed" | | 11 | "familiar" | | 12 | "footsteps" | | 13 | "echoing" | | 14 | "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 | 144 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1220 | | uniqueNames | 18 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 12 | | Metropolitan | 1 | | Police | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Seville | 1 | | London | 1 | | Camden | 2 | | Morris | 4 | | Underground | 1 | | Town | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 | | Eighteen | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Seville" | | 2 | "London" | | 3 | "Camden" | | 4 | "Town" |
| | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 2 | | matches | | 0 | "something like burnt honey and crushed herbs" | | 1 | "sounded like a key turning in a lock" |
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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 | 1220 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 144 | | matches | | |
| 59.68% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 87.14 | | std | 31.27 | | cv | 0.359 | | sampleLengths | | 0 | 79 | | 1 | 89 | | 2 | 144 | | 3 | 114 | | 4 | 96 | | 5 | 103 | | 6 | 94 | | 7 | 95 | | 8 | 45 | | 9 | 86 | | 10 | 19 | | 11 | 114 | | 12 | 43 | | 13 | 99 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 144 | | matches | | 0 | "was pulled" | | 1 | "was pushed" | | 2 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 230 | | matches | | 0 | "was hollowing" | | 1 | "was starting" | | 2 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 144 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1229 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.023596419853539462 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004882017900732303 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 8.47 | | std | 6.41 | | cv | 0.757 | | sampleLengths | | 0 | 13 | | 1 | 15 | | 2 | 31 | | 3 | 14 | | 4 | 6 | | 5 | 16 | | 6 | 23 | | 7 | 3 | | 8 | 4 | | 9 | 19 | | 10 | 2 | | 11 | 2 | | 12 | 5 | | 13 | 15 | | 14 | 3 | | 15 | 10 | | 16 | 13 | | 17 | 21 | | 18 | 3 | | 19 | 9 | | 20 | 10 | | 21 | 28 | | 22 | 10 | | 23 | 24 | | 24 | 3 | | 25 | 2 | | 26 | 4 | | 27 | 4 | | 28 | 16 | | 29 | 10 | | 30 | 3 | | 31 | 10 | | 32 | 10 | | 33 | 21 | | 34 | 6 | | 35 | 6 | | 36 | 3 | | 37 | 8 | | 38 | 11 | | 39 | 5 | | 40 | 5 | | 41 | 12 | | 42 | 4 | | 43 | 7 | | 44 | 9 | | 45 | 13 | | 46 | 1 | | 47 | 3 | | 48 | 12 | | 49 | 9 |
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| 52.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3819444444444444 | | totalSentences | 144 | | uniqueOpeners | 55 | |
| 26.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 127 | | matches | | 0 | "Somewhere, a vendor muttered in" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 127 | | matches | | 0 | "He ducked past the distinctive" | | 1 | "She knew that gait." | | 2 | "She pushed harder." | | 3 | "He hit the alley running," | | 4 | "She didn’t draw." | | 5 | "She needed him conscious." | | 6 | "She needed him talking." | | 7 | "He cut left toward Camden," | | 8 | "She forced her breathing into" | | 9 | "She’d seen the scorch marks" | | 10 | "She’d heard the low, vibrating" | | 11 | "She didn’t understand it then." | | 12 | "She was starting to now." | | 13 | "Her pulse kicked." | | 14 | "She descended into the dark," | | 15 | "Her eyes adjusted." | | 16 | "He’d stopped running." | | 17 | "He turned, rain still gleaming" | | 18 | "His coat hung open." | | 19 | "His left sleeve was pushed" |
| | ratio | 0.291 | |
| 66.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 127 | | matches | | 0 | "Rain fell in hard, slanting" | | 1 | "Harlow Quinn’s boots struck the" | | 2 | "Tonight, the wire was pulled" | | 3 | "He ducked past the distinctive" | | 4 | "Quinn didn’t slow." | | 5 | "She knew that gait." | | 6 | "Ghost in her case files." | | 7 | "The man who kept the" | | 8 | "She pushed harder." | | 9 | "Rain slicked her closely cropped" | | 10 | "Water tracked down the sharp" | | 11 | "The worn leather watch on" | | 12 | "The city was hollowing out," | | 13 | "Herrera vaulted a low iron" | | 14 | "A paramedic’s grace, born in" | | 15 | "He hit the alley running," | | 16 | "Quinn followed, shoulder clipping damp" | | 17 | "She didn’t draw." | | 18 | "She needed him conscious." | | 19 | "She needed him talking." |
| | ratio | 0.787 | |
| 39.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 127 | | matches | | 0 | "If she walked away now," |
| | ratio | 0.008 | |
| 61.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 6 | | matches | | 0 | "A paramedic’s grace, born in Seville and refined on London’s ambulance routes before he threw his NHS license away treating patients who bled wrong and healed t…" | | 1 | "He cut left toward Camden, abandoning the main thoroughfare for a service road that sloped downward." | | 2 | "She’d heard the low, vibrating hum that made her teeth ache." | | 3 | "She’d heard the whispers in intercepted calls, in the ramblings of junkies who swore they’d bought dreams in glass vials and alchemical stimulants that stopped …" | | 4 | "Herrera turned and descended into the lower concourse, his footsteps echoing against tile that hadn’t seen a train in decades." | | 5 | "Quinn followed, water shedding from her coat, her bearing straight, her eyes scanning the stalls, the faces, the shadows that moved with purpose." |
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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 | |