| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1286 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1286 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "down her spine" | | count | 1 |
| | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "silence" | | 3 | "chaotic" | | 4 | "echoed" | | 5 | "flicker" | | 6 | "pulsed" | | 7 | "shimmered" | | 8 | "stark" | | 9 | "down her spine" | | 10 | "could feel" | | 11 | "vibrated" | | 12 | "pulse" | | 13 | "rhythmic" | | 14 | "intensity" | | 15 | "cacophony" | | 16 | "gloom" | | 17 | "velvet" | | 18 | "scanned" | | 19 | "predator" | | 20 | "playfully" | | 21 | "depths" |
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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 | 80 | | matches | (empty) | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1278 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 1115 | | uniqueNames | 9 | | maxNameDensity | 0.63 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | London | 3 | | Victorian | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Harlow | 7 |
| | persons | | 0 | "Herrera" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Tomás" | | 4 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like pressed leather" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1278 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 37.59 | | std | 25.58 | | cv | 0.681 | | sampleLengths | | 0 | 75 | | 1 | 5 | | 2 | 22 | | 3 | 77 | | 4 | 8 | | 5 | 79 | | 6 | 17 | | 7 | 72 | | 8 | 72 | | 9 | 18 | | 10 | 32 | | 11 | 17 | | 12 | 39 | | 13 | 3 | | 14 | 39 | | 15 | 71 | | 16 | 1 | | 17 | 67 | | 18 | 3 | | 19 | 57 | | 20 | 59 | | 21 | 14 | | 22 | 65 | | 23 | 16 | | 24 | 54 | | 25 | 26 | | 26 | 66 | | 27 | 27 | | 28 | 25 | | 29 | 13 | | 30 | 7 | | 31 | 38 | | 32 | 58 | | 33 | 36 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 80 | | matches | | 0 | "being traded" | | 1 | "was fused" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 177 | | matches | | 0 | "was bleeding" | | 1 | "wasn't running" | | 2 | "was already moving" | | 3 | "was blinding" | | 4 | "was shrinking" | | 5 | "was leaving" | | 6 | "was already disappearing" | | 7 | "was lurking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 91 | | ratio | 0.066 | | matches | | 0 | "The suspect skidded near an alleyway—a black, jagged wound between two crumbling Victorian brick buildings—and dived into the shadows." | | 1 | "If she stayed, the man who knew the secrets of the market—and perhaps the truth behind the disappearance of her partner—would vanish forever." | | 2 | "Shapes moved within the mist—tall, spindly things that lacked faces, and men in suits who possessed eyes too sharp and predatory for any human." | | 3 | "The roar of the market hit her like a physical blow—a cacophony of hissing, rhythmic chanting and the clatter of wares being traded for things that made her skin crawl." | | 4 | "The crowd parted, but the silence didn't follow them; instead, a collective, hungry vibration hummed in the air." | | 5 | "A dozen pairs of eyes—golden, slit-pupilled, or entirely missing—fixed upon her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1129 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.024800708591674048 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.011514614703277236 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 14.04 | | std | 7.54 | | cv | 0.537 | | sampleLengths | | 0 | 21 | | 1 | 21 | | 2 | 33 | | 3 | 5 | | 4 | 6 | | 5 | 4 | | 6 | 12 | | 7 | 21 | | 8 | 13 | | 9 | 10 | | 10 | 14 | | 11 | 19 | | 12 | 3 | | 13 | 5 | | 14 | 16 | | 15 | 10 | | 16 | 15 | | 17 | 19 | | 18 | 19 | | 19 | 11 | | 20 | 6 | | 21 | 7 | | 22 | 20 | | 23 | 3 | | 24 | 21 | | 25 | 21 | | 26 | 10 | | 27 | 4 | | 28 | 11 | | 29 | 21 | | 30 | 23 | | 31 | 3 | | 32 | 10 | | 33 | 8 | | 34 | 14 | | 35 | 18 | | 36 | 17 | | 37 | 18 | | 38 | 21 | | 39 | 3 | | 40 | 39 | | 41 | 11 | | 42 | 26 | | 43 | 11 | | 44 | 9 | | 45 | 14 | | 46 | 1 | | 47 | 11 | | 48 | 16 | | 49 | 20 |
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| 37.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.31868131868131866 | | totalSentences | 91 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 57.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 79 | | matches | | 0 | "He was fast, favouring his" | | 1 | "He didn't even flinch." | | 2 | "He just pushed harder, his" | | 3 | "She checked the worn leather" | | 4 | "She didn't hesitate." | | 5 | "She plunged into the dark." | | 6 | "It was a suffocating cocktail" | | 7 | "She drew her baton, the" | | 8 | "She tracked the sound of" | | 9 | "she called out, her voice" | | 10 | "She knew the legends, the" | | 11 | "She reached the threshold of" | | 12 | "He wasn't running anymore." | | 13 | "He held the railing, his" | | 14 | "He looked back at her," | | 15 | "she breathed, her boots grinding" | | 16 | "He barked a jagged, hollow" | | 17 | "He stepped closer to the" | | 18 | "She could feel the weight" | | 19 | "She ignored the warning bells" |
| | ratio | 0.405 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 79 | | matches | | 0 | "The heavy tread of boots" | | 1 | "Harlow dipped her head, salt-and-pepper" | | 2 | "He was fast, favouring his" | | 3 | "The shout vanished into the" | | 4 | "He didn't even flinch." | | 5 | "He just pushed harder, his" | | 6 | "Harlow rounded the corner on" | | 7 | "She checked the worn leather" | | 8 | "The night was bleeding away," | | 9 | "The suspect skidded near an" | | 10 | "She didn't hesitate." | | 11 | "She plunged into the dark." | | 12 | "The stench hit her first:" | | 13 | "It was a suffocating cocktail" | | 14 | "She drew her baton, the" | | 15 | "Silence reigned here, heavy and" | | 16 | "She tracked the sound of" | | 17 | "she called out, her voice" | | 18 | "A flicker of movement caught" | | 19 | "A green neon light pulsed" |
| | ratio | 0.949 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 79 | | matches | | 0 | "If she went in, she" | | 1 | "If she stayed, the man" |
| | ratio | 0.025 | |
| 48.52% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 7 | | matches | | 0 | "He was fast, favouring his right side, slipping through the gap of a closing delivery van with the desperate grace of a man who knew exactly what waited for him…" | | 1 | "She tracked the sound of his ragged breathing, a wet, rattling noise that echoed off the damp tunnel walls." | | 2 | "The archway shuddered, the graffiti-covered concrete sliding back to reveal a glowing, chaotic atrium crowded with figures that shouldn't exist." | | 3 | "Shapes moved within the mist—tall, spindly things that lacked faces, and men in suits who possessed eyes too sharp and predatory for any human." | | 4 | "The transition was instantaneous, a sudden, jarring shift in gravity that threatened to tear her limbs from their sockets." | | 5 | "The roar of the market hit her like a physical blow—a cacophony of hissing, rhythmic chanting and the clatter of wares being traded for things that made her ski…" | | 6 | "A heavy, metallic clatter echoed from the far end of the tunnel, followed by a low, guttural growl that belonged to nothing that had ever walked the surface." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 4 | | matches | | 0 | "she called out, her voice low and tight" | | 1 | "she breathed, her boots grinding on the metal steps" | | 2 | "Tomás wiped, his voice trembling" | | 3 | "the woman purred, the sound scraping against Harlow’s nerves" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "she breathed (breathe)" | | 2 | "He barked (bark)" | | 3 | "she screamed (scream)" |
| | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |