| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 31 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.564 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1479 | | 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) | |
| 45.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1479 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "electric" | | 1 | "rhythmic" | | 2 | "weight" | | 3 | "scanned" | | 4 | "perfect" | | 5 | "chaotic" | | 6 | "vibrated" | | 7 | "silence" | | 8 | "echoed" | | 9 | "etched" | | 10 | "racing" | | 11 | "tangible" |
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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 | 2 | | narrationSentences | 105 | | matches | | 0 | "g in panic" | | 1 | "g with determination" |
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| 74.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | 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 | 1477 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1019 | | uniqueNames | 15 | | maxNameDensity | 2.36 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 24 | | Tube | 1 | | London | 2 | | Romans | 1 | | Miller | 10 | | Victorian-era | 1 | | Morris | 1 | | Kowalski | 1 | | Eva | 12 | | Veil | 1 | | Compass | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Romans" | | 3 | "Miller" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.322 | | windowScore | 0.167 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 2 | | matches | | 0 | "It was as if the man had materialized out of thin air and collapsed" | | 1 | "looked like obsidian glass" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.677 | | wordCount | 1477 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 30.14 | | std | 18.49 | | cv | 0.614 | | sampleLengths | | 0 | 84 | | 1 | 43 | | 2 | 26 | | 3 | 21 | | 4 | 77 | | 5 | 29 | | 6 | 36 | | 7 | 49 | | 8 | 20 | | 9 | 25 | | 10 | 70 | | 11 | 11 | | 12 | 6 | | 13 | 53 | | 14 | 41 | | 15 | 10 | | 16 | 52 | | 17 | 12 | | 18 | 21 | | 19 | 31 | | 20 | 4 | | 21 | 60 | | 22 | 9 | | 23 | 36 | | 24 | 21 | | 25 | 47 | | 26 | 3 | | 27 | 16 | | 28 | 38 | | 29 | 23 | | 30 | 38 | | 31 | 56 | | 32 | 23 | | 33 | 36 | | 34 | 18 | | 35 | 7 | | 36 | 24 | | 37 | 39 | | 38 | 29 | | 39 | 4 | | 40 | 24 | | 41 | 21 | | 42 | 20 | | 43 | 33 | | 44 | 34 | | 45 | 31 | | 46 | 33 | | 47 | 16 | | 48 | 17 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 105 | | matches | | 0 | "been sucked" | | 1 | "been moved" | | 2 | "was caked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 181 | | matches | (empty) | |
| 54.26% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 4 | | flaggedSentences | 4 | | totalSentences | 129 | | ratio | 0.031 | | matches | | 0 | "She didn't look at the dead man; she looked at the ground." | | 1 | "Under the lens, the skin wasn't just dry; it was cracked like old parchment." | | 2 | "The man hadn't walked into the alley; he had dropped out of the sky, or rather, out of a rift that had sealed itself the moment he landed." | | 3 | "The blistered paint on the wall wasn't chemical damage; it was radiation from the crossing." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 702 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavier, pressing against her" |
| | adverbCount | 15 | | adverbRatio | 0.021367521367521368 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007122507122507123 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 11.45 | | std | 6.67 | | cv | 0.582 | | sampleLengths | | 0 | 21 | | 1 | 28 | | 2 | 9 | | 3 | 1 | | 4 | 25 | | 5 | 5 | | 6 | 19 | | 7 | 19 | | 8 | 17 | | 9 | 9 | | 10 | 7 | | 11 | 14 | | 12 | 8 | | 13 | 15 | | 14 | 12 | | 15 | 11 | | 16 | 5 | | 17 | 5 | | 18 | 21 | | 19 | 9 | | 20 | 20 | | 21 | 15 | | 22 | 21 | | 23 | 4 | | 24 | 14 | | 25 | 8 | | 26 | 9 | | 27 | 14 | | 28 | 3 | | 29 | 7 | | 30 | 10 | | 31 | 16 | | 32 | 9 | | 33 | 13 | | 34 | 10 | | 35 | 6 | | 36 | 15 | | 37 | 26 | | 38 | 11 | | 39 | 4 | | 40 | 2 | | 41 | 31 | | 42 | 9 | | 43 | 13 | | 44 | 4 | | 45 | 3 | | 46 | 5 | | 47 | 24 | | 48 | 5 | | 49 | 10 |
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| 62.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4251968503937008 | | totalSentences | 127 | | uniqueOpeners | 54 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Too early for the morning" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 95 | | matches | | 0 | "She checked the time on" | | 1 | "She stopped at the mouth" | | 2 | "His eyes stared blankly at" | | 3 | "She kept her hands in" | | 4 | "It was as if the" | | 5 | "She leaned closer, her sharp" | | 6 | "She stood up and brushed" | | 7 | "Her gaze swept the alley" | | 8 | "She pointed a gloved finger" | | 9 | "She remembered DS Morris." | | 10 | "Her curly red hair frizzed" | | 11 | "She didn't look at the" | | 12 | "She opened her satchel and" | | 13 | "She watched the compass" | | 14 | "It vibrated, humming in sync" | | 15 | "She knelt, careful not to" | | 16 | "She grabbed a magnifying glass" | | 17 | "It was violet sand, fine" | | 18 | "She looked at Eva." | | 19 | "She turned back to Eva." |
| | ratio | 0.211 | |
| 23.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 95 | | matches | | 0 | "Rain slicked the cobblestones of" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "She checked the time on" | | 3 | "The scent hit her first." | | 4 | "This smelled of ozone, burnt" | | 5 | "PC Miller stepped into her" | | 6 | "Quinn pushed past him without" | | 7 | "She stopped at the mouth" | | 8 | "The crime scene lay ten" | | 9 | "A man in a tattered" | | 10 | "His eyes stared blankly at" | | 11 | "Quinn asked, crouching beside the" | | 12 | "She kept her hands in" | | 13 | "Miller replied, shifting his weight" | | 14 | "Quinn scanned the ground." | | 15 | "The mud near the body" | | 16 | "It was as if the" | | 17 | "She leaned closer, her sharp" | | 18 | "Miller offered, voicing the theory" | | 19 | "She stood up and brushed" |
| | ratio | 0.874 | |
| 52.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 95 | | matches | | 0 | "Yet, the body looked drained," |
| | ratio | 0.011 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "It was violet sand, fine and glittering, mixed with shards of something that looked like obsidian glass." | | 1 | "Quinn scanned the alley again, her detective's eye filtering out the impossible and focusing on the tangible." | | 2 | "Quinn drew her torch, the beam cutting through the sudden fog that rolled into the alley, thick and smelling of ozone." |
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| 28.23% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 31 | | uselessAdditionCount | 6 | | matches | | 0 | "PC Miller stepped, his face pale under the strobe of the forensic lights" | | 1 | "Eva whispered, her voice trembling slightly" | | 2 | "Eva said, her tone gaining confidence" | | 3 | "Miller started, his voice rising in panic," | | 4 | "Quinn said, her voice flat" | | 5 | "Miller asked, his hand shaking as he reached for his baton" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 10 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "Quinn snapped (snap)" | | 2 | "Miller barked (bark)" | | 3 | "Quinn lied (lie)" | | 4 | "Eva whispered (whisper)" | | 5 | "Miller demanded (demand)" | | 6 | "Eva corrected (correct)" | | 7 | "Eva explained (explain)" | | 8 | "Quinn ordered (order)" | | 9 | "Eva warned (warn)" |
| | dialogueSentences | 55 | | tagDensity | 0.309 | | leniency | 0.618 | | rawRatio | 0.588 | | effectiveRatio | 0.364 | |