| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "she motioned back [back]" |
| | dialogueSentences | 27 | | tagDensity | 0.37 | | leniency | 0.741 | | rawRatio | 0.1 | | effectiveRatio | 0.074 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 858 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 41.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 858 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "chill" | | 1 | "amidst" | | 2 | "echoing" | | 3 | "footsteps" | | 4 | "clenching" | | 5 | "measured" | | 6 | "tension" | | 7 | "etched" | | 8 | "eyebrow" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 85.32% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 42 | | matches | | 0 | "d with excitement" | | 1 | "d in confusion" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 42 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 850 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 513 | | uniqueNames | 10 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Thames | 1 | | Harlow | 2 | | Quinn | 10 | | Constable | 1 | | Eva | 1 | | Kowalski | 1 | | Met | 1 | | Formica | 1 | | Evie | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Constable" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Evie" |
| | places | | 0 | "London" | | 1 | "Thames" | | 2 | "Formica" |
| | globalScore | 0.525 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 850 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 30.36 | | std | 22.54 | | cv | 0.743 | | sampleLengths | | 0 | 58 | | 1 | 71 | | 2 | 20 | | 3 | 59 | | 4 | 9 | | 5 | 45 | | 6 | 69 | | 7 | 73 | | 8 | 9 | | 9 | 57 | | 10 | 12 | | 11 | 46 | | 12 | 11 | | 13 | 41 | | 14 | 31 | | 15 | 6 | | 16 | 5 | | 17 | 8 | | 18 | 30 | | 19 | 1 | | 20 | 7 | | 21 | 28 | | 22 | 38 | | 23 | 51 | | 24 | 18 | | 25 | 28 | | 26 | 8 | | 27 | 11 |
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| 96.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 42 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 89 | | matches | (empty) | |
| 46.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 59 | | ratio | 0.034 | | matches | | 0 | "She carried a worn leather satchel over her shoulder, no doubt packed with the tools of her trade—cameras, journals, and measuring tape for documenting the scene." | | 1 | "\"It's not my area of expertise. I'm just here to assist with the\"—she motioned back to the crates—\"supernatural side of things.\" She made the last word sound unnatural." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 536 | | adjectiveStacks | 1 | | stackExamples | | 0 | "hugging large wooden crates." |
| | adverbCount | 9 | | adverbRatio | 0.016791044776119403 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.009328358208955223 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.41 | | std | 10.27 | | cv | 0.713 | | sampleLengths | | 0 | 20 | | 1 | 18 | | 2 | 20 | | 3 | 11 | | 4 | 11 | | 5 | 23 | | 6 | 26 | | 7 | 20 | | 8 | 20 | | 9 | 17 | | 10 | 14 | | 11 | 8 | | 12 | 6 | | 13 | 3 | | 14 | 45 | | 15 | 17 | | 16 | 28 | | 17 | 12 | | 18 | 12 | | 19 | 20 | | 20 | 12 | | 21 | 15 | | 22 | 8 | | 23 | 18 | | 24 | 9 | | 25 | 2 | | 26 | 55 | | 27 | 12 | | 28 | 34 | | 29 | 12 | | 30 | 3 | | 31 | 8 | | 32 | 22 | | 33 | 2 | | 34 | 17 | | 35 | 31 | | 36 | 6 | | 37 | 5 | | 38 | 8 | | 39 | 14 | | 40 | 16 | | 41 | 1 | | 42 | 7 | | 43 | 14 | | 44 | 5 | | 45 | 9 | | 46 | 4 | | 47 | 28 | | 48 | 6 | | 49 | 13 |
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| 92.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.576271186440678 | | totalSentences | 59 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 40 | | matches | | 0 | "she called out, her voice" | | 1 | "She carried a worn leather" | | 2 | "She pointed with a nod," | | 3 | "Her body lay sprawled on" | | 4 | "Her legs were splayed out," | | 5 | "She took in the details," | | 6 | "She crouched, running a gloved" | | 7 | "She kept the officer calm" | | 8 | "She turned back to Evie." | | 9 | "she motioned back to the" | | 10 | "She held it out, and" | | 11 | "It was Evie's turn to" |
| | ratio | 0.3 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 40 | | matches | | 0 | "Shadows danced across the grimy" | | 1 | "The air was thick with" | | 2 | "Detective Harlow Quinn stood amidst" | | 3 | "she called out, her voice" | | 4 | "The sound of footsteps approached," | | 5 | "Detective Constable Eva Kowalski appeared," | | 6 | "She carried a worn leather" | | 7 | "Evie's eyes sparkled with excitement" | | 8 | "Harlow glanced around the site" | | 9 | "Foot traffic had trampled and" | | 10 | "This was no ordinary crime" | | 11 | "She pointed with a nod," | | 12 | "The detectives picked their way" | | 13 | "Her body lay sprawled on" | | 14 | "Her legs were splayed out," | | 15 | "A boney woman, none of" | | 16 | "The drawers swung wide, the" | | 17 | "An obvious robbery gone wrong" | | 18 | "She took in the details," | | 19 | "Quinn asked, her tone measured" |
| | ratio | 0.9 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 40 | | matches | | 0 | "While Quinn could sense some" |
| | ratio | 0.025 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 2 | | matches | | 0 | "Her body lay sprawled on the concrete, hands clenching at her throat, the defining feature of strangulation, as though she had tried to claw the killer's hands …" | | 1 | "A uniformed officer at the corner caught their attention, attempting to play cool after snatching a peek at the gory events unfolding." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "she called out, her voice echoing down the dark tunnels" | | 1 | "Quinn asked, her tone measured" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.333 | | effectiveRatio | 0.074 | |