| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 984 | | totalAiIsmAdverbs | 1 | | 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) | |
| 74.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 984 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "etched" | | 3 | "dancing" | | 4 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 75 | | matches | | |
| 9.52% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 5 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "seemed to" | | 2 | "try to" | | 3 | "begin to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 986 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 985 | | uniqueNames | 18 | | maxNameDensity | 1.22 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 4 | | Harlow | 2 | | Quinn | 12 | | Tomás | 2 | | Herrera | 8 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 5 | | Soho | 1 | | Old | 1 | | Compton | 1 | | Street | 1 | | Underground | 1 | | Tube | 1 | | Transport | 1 | | Victorian | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Transport" | | 9 | "Victorian" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Street" | | 5 | "Underground" |
| | globalScore | 0.891 | | windowScore | 1 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like the night she might finally g" | | 1 | "skin that seemed to shift between scales and flesh" |
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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 | 986 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 77.70% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 44.82 | | std | 18.91 | | cv | 0.422 | | sampleLengths | | 0 | 68 | | 1 | 66 | | 2 | 60 | | 3 | 48 | | 4 | 56 | | 5 | 45 | | 6 | 51 | | 7 | 35 | | 8 | 53 | | 9 | 12 | | 10 | 9 | | 11 | 61 | | 12 | 45 | | 13 | 11 | | 14 | 79 | | 15 | 45 | | 16 | 44 | | 17 | 40 | | 18 | 49 | | 19 | 51 | | 20 | 49 | | 21 | 9 |
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| 81.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 75 | | matches | | 0 | "was connected" | | 1 | "were etched" | | 2 | "was hidden" | | 3 | "was connected" | | 4 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 162 | | matches | | 0 | "was speaking" | | 1 | "was seeing" |
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| 66.67% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 75 | | ratio | 0.027 | | matches | | 0 | "One of them belonged to Herrera – she was certain of it." | | 1 | "People moved between the vendors – people who didn't look entirely human." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 698 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.025787965616045846 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0057306590257879654 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 13.15 | | std | 6.4 | | cv | 0.486 | | sampleLengths | | 0 | 19 | | 1 | 21 | | 2 | 28 | | 3 | 8 | | 4 | 30 | | 5 | 7 | | 6 | 10 | | 7 | 11 | | 8 | 12 | | 9 | 16 | | 10 | 13 | | 11 | 19 | | 12 | 5 | | 13 | 10 | | 14 | 17 | | 15 | 16 | | 16 | 25 | | 17 | 18 | | 18 | 6 | | 19 | 7 | | 20 | 11 | | 21 | 20 | | 22 | 1 | | 23 | 13 | | 24 | 14 | | 25 | 29 | | 26 | 8 | | 27 | 15 | | 28 | 8 | | 29 | 12 | | 30 | 4 | | 31 | 13 | | 32 | 7 | | 33 | 15 | | 34 | 14 | | 35 | 5 | | 36 | 4 | | 37 | 3 | | 38 | 9 | | 39 | 12 | | 40 | 12 | | 41 | 20 | | 42 | 17 | | 43 | 11 | | 44 | 19 | | 45 | 15 | | 46 | 11 | | 47 | 21 | | 48 | 17 | | 49 | 12 |
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| 72.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4533333333333333 | | totalSentences | 75 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 74 | | matches | | 0 | "She'd been following the former" | | 1 | "She'd learned patience during eighteen" | | 2 | "He carried a small leather" | | 3 | "She knew every inch of" | | 4 | "She moved deeper into the" | | 5 | "She pressed her ear to" | | 6 | "It swung open on well-oiled" | | 7 | "She should call for backup." | | 8 | "She caught fragments now: discussions" | | 9 | "Her fifteen years of detective" | | 10 | "She pressed against the door" | | 11 | "He was speaking with a" | | 12 | "She could retreat now, follow" | | 13 | "She thought of Morris, of" | | 14 | "She turned the handle and" |
| | ratio | 0.203 | |
| 95.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 74 | | matches | | 0 | "The rain hammered London's streets" | | 1 | "Detective Harlow Quinn pressed herself" | | 2 | "Quinn's worn leather watch showed" | | 3 | "She'd been following the former" | | 4 | "Bodies found with wounds that" | | 5 | "Victims who recovered from injuries" | | 6 | "The kind of cases that" | | 7 | "Herrera pushed through the bar's" | | 8 | "Quinn waited, counting seconds, her" | | 9 | "She'd learned patience during eighteen" | | 10 | "Tonight felt like the night" | | 11 | "The bar's door opened again." | | 12 | "Herrera emerged, moving with purpose" | | 13 | "He carried a small leather" | | 14 | "Quinn pushed off from the" | | 15 | "Herrera turned left onto Old" | | 16 | "The detective frowned, quickening her" | | 17 | "She knew every inch of" | | 18 | "Quinn drew her flashlight, the" | | 19 | "She moved deeper into the" |
| | ratio | 0.73 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 74 | | matches | | 0 | "By the time she reached" | | 1 | "Now she stood at the" |
| | ratio | 0.027 | |
| 30.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 8 | | matches | | 0 | "The rain hammered London's streets like bullets, turning the asphalt into a mirror that reflected the city's neon veins." | | 1 | "But the door stood slightly ajar, and warm air drifted up from below, carrying scents that made no sense: cinnamon, old parchment, something metallic that might…" | | 2 | "It swung open on well-oiled hinges, revealing a staircase that descended into darkness." | | 3 | "She caught fragments now: discussions of prices, of rare items, of services that couldn't be found elsewhere in the city." | | 4 | "He was speaking with a vendor whose face was hidden beneath a hood, exchanging his leather satchel for a small vial filled with liquid that glowed with its own …" | | 5 | "The official report would read like the ravings of a detective who'd finally cracked under the pressure of too many unsolved cases." | | 6 | "This market, these people, this world that existed beneath London's streets." | | 7 | "And somehow, it was connected to the cases that had been haunting her, the unexplained deaths and miraculous recoveries." |
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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 | |