| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 775 | | 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) | |
| 48.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 775 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "comforting" | | 1 | "efficient" | | 2 | "depths" | | 3 | "profound" | | 4 | "weight" | | 5 | "echoed" | | 6 | "familiar" | | 7 | "echo" |
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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 | 46 | | matches | (empty) | |
| 80.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 770 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.05% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 770 | | uniqueNames | 10 | | maxNameDensity | 1.04 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Harlow | 8 | | Quinn | 1 | | London | 2 | | Morris | 3 | | Tomás | 1 | | Herrera | 4 | | Underground | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" |
| | places | | | globalScore | 0.981 | | windowScore | 1 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed carved from the same gray stone as the London facades" |
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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 | 770 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 75.30% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 48.13 | | std | 19.9 | | cv | 0.414 | | sampleLengths | | 0 | 100 | | 1 | 63 | | 2 | 58 | | 3 | 55 | | 4 | 53 | | 5 | 34 | | 6 | 55 | | 7 | 18 | | 8 | 63 | | 9 | 28 | | 10 | 64 | | 11 | 31 | | 12 | 30 | | 13 | 50 | | 14 | 24 | | 15 | 44 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 46 | | matches | | 0 | "was plastered" | | 1 | "was unlocked" |
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| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 118 | | matches | | 0 | "was leading" | | 1 | "was still moving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 46 | | ratio | 0.109 | | matches | | 0 | "The former paramedic had been on her radar for a week—a phantom who stitched up gunshot wounds for people who never went to hospitals, a ghost moving through the city's dark underbelly." | | 1 | "Just a corrugated metal fence and the crumbling brick exterior of an abandoned London Underground station—a relic choked by ivy and decades of soot." | | 2 | "A draft billowed up from the subterranean depths below—air that smelled sickeningly sweet, like crushed lavender mixed with oxidized copper and old ozone." | | 3 | "She knew the intelligence reports—scraps of rumors gathered from desperate informants and informants who had stopped breathing shortly after talking." | | 4 | "The memory of Morris flashed behind her eyes—the sudden, unnatural cold, the empty space where her partner had been standing just a heartbeat before." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 781 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.023047375160051217 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012804097311139564 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 16.74 | | std | 7.76 | | cv | 0.464 | | sampleLengths | | 0 | 24 | | 1 | 8 | | 2 | 28 | | 3 | 20 | | 4 | 20 | | 5 | 9 | | 6 | 18 | | 7 | 36 | | 8 | 22 | | 9 | 4 | | 10 | 32 | | 11 | 15 | | 12 | 16 | | 13 | 24 | | 14 | 23 | | 15 | 3 | | 16 | 27 | | 17 | 10 | | 18 | 24 | | 19 | 14 | | 20 | 8 | | 21 | 16 | | 22 | 17 | | 23 | 18 | | 24 | 12 | | 25 | 23 | | 26 | 12 | | 27 | 16 | | 28 | 14 | | 29 | 14 | | 30 | 5 | | 31 | 20 | | 32 | 19 | | 33 | 20 | | 34 | 12 | | 35 | 2 | | 36 | 2 | | 37 | 15 | | 38 | 21 | | 39 | 9 | | 40 | 7 | | 41 | 24 | | 42 | 19 | | 43 | 24 | | 44 | 23 | | 45 | 21 |
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| 55.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.41304347826086957 | | totalSentences | 46 | | uniqueOpeners | 19 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 44 | | matches | | 0 | "Just a corrugated metal fence" | | 1 | "Just a heavy steel maintenance" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 44 | | matches | | 0 | "Her focus was absolute, honed" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "It was Tomás Herrera." | | 3 | "She leaped over a rusted" | | 4 | "She drew her service weapon," | | 5 | "She swept her flashlight beam" | | 6 | "She dropped to one knee," | | 7 | "It was a smell that" | | 8 | "It was the exact same" | | 9 | "She knew the intelligence reports—scraps" | | 10 | "She had spent three years" | | 11 | "She stood up, checked the" |
| | ratio | 0.273 | |
| 96.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 44 | | matches | | 0 | "The rain fell in relentless" | | 1 | "Detective Harlow Quinn did not" | | 2 | "Her focus was absolute, honed" | | 3 | "Her closely cropped salt-and-pepper hair" | | 4 | "It was Tomás Herrera." | | 5 | "The former paramedic had been" | | 6 | "Harlow closed the distance, her" | | 7 | "She leaped over a rusted" | | 8 | "Herrera was leading her north," | | 9 | "The runner suddenly swerved sharply" | | 10 | "Harlow didn't hesitate." | | 11 | "She drew her service weapon," | | 12 | "Harlow reached the dead-end wall," | | 13 | "She swept her flashlight beam" | | 14 | "She dropped to one knee," | | 15 | "The hatch was unlocked, its" | | 16 | "A draft billowed up from" | | 17 | "It was a smell that" | | 18 | "It was the exact same" | | 19 | "Harlow gripped her flashlight tighter," |
| | ratio | 0.727 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 44 | | matches | | 0 | "If she went down those" |
| | ratio | 0.023 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 8 | | matches | | 0 | "Her focus was absolute, honed by eighteen years on the force and a sharp, unforgiving jaw that seemed carved from the same gray stone as the London facades." | | 1 | "But since the night she lost DS Morris to things that defied police reports and medical examiners alike, Harlow trusted only her own boots on the pavement and t…" | | 2 | "The former paramedic had been on her radar for a week—a phantom who stitched up gunshot wounds for people who never went to hospitals, a ghost moving through th…" | | 3 | "She leaped over a rusted iron grating, her breath pluming white in the freezing midnight air." | | 4 | "Harlow reached the dead-end wall, her chest heaving, boots sliding on the wet stone." | | 5 | "A draft billowed up from the subterranean depths below—air that smelled sickeningly sweet, like crushed lavender mixed with oxidized copper and old ozone." | | 6 | "She knew the intelligence reports—scraps of rumors gathered from desperate informants and informants who had stopped breathing shortly after talking." | | 7 | "An underground black market trading in banned alchemical goods and supernatural contraband that shifted locations with the lunar cycle." |
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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 | |