| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 2 | | adverbTags | | 0 | "the city absorbed almost [almost]" | | 1 | "Her voice echoed hollowly [hollowly]" |
| | dialogueSentences | 10 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |
| 89.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1469 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "truly" | | 2 | "suddenly" |
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| 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) | |
| 25.12% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1469 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "practiced ease" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "fractured" | | 1 | "chilled" | | 2 | "pounding" | | 3 | "practiced ease" | | 4 | "weight" | | 5 | "maw" | | 6 | "familiar" | | 7 | "echoed" | | 8 | "glint" | | 9 | "silence" | | 10 | "gloom" | | 11 | "flicker" | | 12 | "hulking" | | 13 | "sense of" | | 14 | "structure" | | 15 | "predator" | | 16 | "potential" | | 17 | "could feel" | | 18 | "symphony" |
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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 | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 9 | | totalWords | 1463 | | ratio | 0.006 | | matches | | 0 | "Saint Christopher." | | 1 | "CAMDEN TOWN – SERVICES ACCESS" | | 2 | "not" | | 3 | "other" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.68% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1391 | | uniqueNames | 13 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 14 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Morris | 2 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | | globalScore | 0.997 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 2 | | matches | | 0 | "liquid that seemed to contain miniature storms were being hawked" | | 1 | "looked like antique medical instruments s" | | 2 | "seemed thin and useless as parchment here" |
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| 63.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.367 | | wordCount | 1463 | | matches | | 0 | "Not the harsh white of a work lamp, but a softer, multicolored glow" | | 1 | "Not the shouts of fleeing suspects, but a low murmur, a current of conversation" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 56.27 | | std | 33.09 | | cv | 0.588 | | sampleLengths | | 0 | 112 | | 1 | 97 | | 2 | 87 | | 3 | 123 | | 4 | 77 | | 5 | 73 | | 6 | 87 | | 7 | 66 | | 8 | 14 | | 9 | 27 | | 10 | 109 | | 11 | 29 | | 12 | 18 | | 13 | 82 | | 14 | 7 | | 15 | 73 | | 16 | 49 | | 17 | 22 | | 18 | 24 | | 19 | 31 | | 20 | 38 | | 21 | 38 | | 22 | 55 | | 23 | 60 | | 24 | 12 | | 25 | 53 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 110 | | matches | | 0 | "been transformed" | | 1 | "was gone" |
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| 34.98% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 202 | | matches | | 0 | "was heading" | | 1 | "were being" | | 2 | "was standing" | | 3 | "was wading" | | 4 | "was already pulling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 114 | | ratio | 0.07 | | matches | | 0 | "She ran with purpose, her gaze locked on the figure fifty yards ahead—a silhouette in a dark hoodie, cutting through the sparse late-night crowd with the practiced ease of someone who knew the alleyways and shortcuts." | | 1 | "The narrow space amplified the sound of their flight—the scuff of his trainers, the authoritative stomp of her boots, the relentless drumming of rain on metal lids." | | 2 | "He was heading for a dark maw under a temporary walkway—a service entrance to something below." | | 3 | "A faded, partially scraped-away sign read: *CAMDEN TOWN – SERVICES ACCESS*." | | 4 | "She stood on a ledge overlooking a vast, cavernous space—the vaulted ceiling of a forgotten Tube station, its arched ribs lost in shadow." | | 5 | "It wasn't just a black market for drugs or stolen electronics; it was a black market for everything illegal, forbidden, and *other*." | | 6 | "Drawing it here would be like bringing a stick to a dragon’s den—a provocation and an insult." | | 7 | "The murmur of voices didn’t stop, but a hundred pairs of eyes—some with pupils slit vertically, some glowing faintly in the gloom—turned toward her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1406 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.02631578947368421 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.010668563300142247 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 12.83 | | std | 7.23 | | cv | 0.563 | | sampleLengths | | 0 | 24 | | 1 | 20 | | 2 | 21 | | 3 | 11 | | 4 | 36 | | 5 | 16 | | 6 | 3 | | 7 | 29 | | 8 | 24 | | 9 | 25 | | 10 | 25 | | 11 | 5 | | 12 | 20 | | 13 | 10 | | 14 | 27 | | 15 | 7 | | 16 | 25 | | 17 | 18 | | 18 | 3 | | 19 | 24 | | 20 | 21 | | 21 | 8 | | 22 | 17 | | 23 | 19 | | 24 | 10 | | 25 | 16 | | 26 | 16 | | 27 | 16 | | 28 | 12 | | 29 | 10 | | 30 | 12 | | 31 | 19 | | 32 | 2 | | 33 | 18 | | 34 | 7 | | 35 | 13 | | 36 | 10 | | 37 | 9 | | 38 | 5 | | 39 | 15 | | 40 | 11 | | 41 | 5 | | 42 | 12 | | 43 | 14 | | 44 | 17 | | 45 | 13 | | 46 | 3 | | 47 | 19 | | 48 | 4 | | 49 | 10 |
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| 35.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2631578947368421 | | totalSentences | 114 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 108 | | matches | (empty) | | ratio | 0 | |
| 90.37% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 108 | | matches | | 0 | "Her leather-soled boots struck the" | | 1 | "She didn’t run with the" | | 2 | "She ran with purpose, her" | | 3 | "She ignored it." | | 4 | "He’d been talking to Silas," | | 5 | "Her voice was a sharp" | | 6 | "He didn’t even glance back." | | 7 | "He swerved left, down a" | | 8 | "He was faster, but she" | | 9 | "He knew the maze of" | | 10 | "He burst out onto a" | | 11 | "He was heading for a" | | 12 | "Her voice echoed hollowly" | | 13 | "She descended after him, every" | | 14 | "It was a tunnel, a" | | 15 | "She moved forward, weapon raised," | | 16 | "She heard voices." | | 17 | "She stood on a ledge" | | 18 | "It wasn't just a black" | | 19 | "Her badge, her warrant card," |
| | ratio | 0.324 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 108 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s breath plumed" | | 2 | "Her leather-soled boots struck the" | | 3 | "She didn’t run with the" | | 4 | "She ran with purpose, her" | | 5 | "Quinn’s worn leather watch slapped" | | 6 | "She ignored it." | | 7 | "The only time that mattered" | | 8 | "He’d been talking to Silas," | | 9 | "The suspect had a bulge" | | 10 | "Her voice was a sharp" | | 11 | "He didn’t even glance back." | | 12 | "He swerved left, down a" | | 13 | "Quinn followed without hesitation, her" | | 14 | "The narrow space amplified the" | | 15 | "He was faster, but she" | | 16 | "He knew the maze of" | | 17 | "He burst out onto a" | | 18 | "A horn blared." | | 19 | "Quinn didn’t break stride, weaving" |
| | ratio | 0.889 | |
| 46.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 108 | | matches | | 0 | "To chase her suspect, she" |
| | ratio | 0.009 | |
| 89.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 5 | | matches | | 0 | "She ran with purpose, her gaze locked on the figure fifty yards ahead—a silhouette in a dark hoodie, cutting through the sparse late-night crowd with the practi…" | | 1 | "The suspect had a bulge in his jacket that matched the profile of a stolen alchemical reagent vial, and his reaction had been textbook guilty." | | 2 | "Figures moved between them, a motley collection of humanity that was decidedly *not* human." | | 3 | "A rack of what looked like antique medical instruments stood next to a table where vials of liquid that seemed to contain miniature storms were being hawked." | | 4 | "She wore a long, practical coat that did little to hide the lean, predatory strength beneath." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn demanded, her patience fraying" |
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| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn demanded (demand)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.25 | | effectiveRatio | 0.2 | |