| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said quietly [quietly]" |
| | dialogueSentences | 60 | | tagDensity | 0.317 | | leniency | 0.633 | | rawRatio | 0.053 | | effectiveRatio | 0.033 | |
| 90.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1514 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "carefully" | | 1 | "deliberately" |
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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) | |
| 50.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1514 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "chill" | | 1 | "velvet" | | 2 | "clandestine" | | 3 | "silence" | | 4 | "scanning" | | 5 | "standard" | | 6 | "tracing" | | 7 | "shattered" | | 8 | "amidst" | | 9 | "traced" | | 10 | "maw" | | 11 | "footsteps" | | 12 | "could feel" |
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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 | 1 | | narrationSentences | 104 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | 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 | 1511 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 905 | | uniqueNames | 10 | | maxNameDensity | 2.43 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 22 | | Quinn | 1 | | Camden | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Eva | 19 | | Silas | 3 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Silas" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.285 | | windowScore | 0.167 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "as if watching for trains that would never come" | | 1 | "as if clutching something that wasn’t there" |
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| 67.64% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.324 | | wordCount | 1511 | | matches | | 0 | "not in fists, but as if clutching something" | | 1 | "Not by running footsteps, but by something being dragged" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 23.61 | | std | 16.96 | | cv | 0.718 | | sampleLengths | | 0 | 60 | | 1 | 52 | | 2 | 34 | | 3 | 4 | | 4 | 30 | | 5 | 37 | | 6 | 12 | | 7 | 4 | | 8 | 17 | | 9 | 21 | | 10 | 26 | | 11 | 2 | | 12 | 42 | | 13 | 10 | | 14 | 20 | | 15 | 5 | | 16 | 21 | | 17 | 59 | | 18 | 40 | | 19 | 10 | | 20 | 10 | | 21 | 39 | | 22 | 25 | | 23 | 3 | | 24 | 6 | | 25 | 20 | | 26 | 33 | | 27 | 4 | | 28 | 31 | | 29 | 2 | | 30 | 6 | | 31 | 37 | | 32 | 5 | | 33 | 3 | | 34 | 40 | | 35 | 3 | | 36 | 33 | | 37 | 24 | | 38 | 50 | | 39 | 41 | | 40 | 27 | | 41 | 27 | | 42 | 5 | | 43 | 44 | | 44 | 11 | | 45 | 20 | | 46 | 36 | | 47 | 18 | | 48 | 11 | | 49 | 3 |
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| 74.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 104 | | matches | | 0 | "were torn" | | 1 | "been replaced" | | 2 | "were curled" | | 3 | "was concentrated" | | 4 | "been shattered" | | 5 | "was screwed" | | 6 | "been used" | | 7 | "been found" | | 8 | "being dragged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 153 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 145 | | ratio | 0.007 | | matches | | 0 | "They had staged the robbery, but badly—the clues were there for anyone who knew to look." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 908 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Tiny, faint red pinpricks" |
| | adverbCount | 23 | | adverbRatio | 0.025330396475770924 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009911894273127754 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 10.42 | | std | 7.04 | | cv | 0.676 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 20 | | 3 | 14 | | 4 | 9 | | 5 | 11 | | 6 | 18 | | 7 | 16 | | 8 | 18 | | 9 | 4 | | 10 | 13 | | 11 | 17 | | 12 | 16 | | 13 | 3 | | 14 | 18 | | 15 | 4 | | 16 | 8 | | 17 | 4 | | 18 | 17 | | 19 | 5 | | 20 | 8 | | 21 | 8 | | 22 | 10 | | 23 | 16 | | 24 | 2 | | 25 | 5 | | 26 | 37 | | 27 | 7 | | 28 | 3 | | 29 | 20 | | 30 | 5 | | 31 | 21 | | 32 | 5 | | 33 | 15 | | 34 | 12 | | 35 | 16 | | 36 | 3 | | 37 | 8 | | 38 | 6 | | 39 | 7 | | 40 | 19 | | 41 | 4 | | 42 | 4 | | 43 | 10 | | 44 | 10 | | 45 | 14 | | 46 | 16 | | 47 | 3 | | 48 | 6 | | 49 | 7 |
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| 43.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.31724137931034485 | | totalSentences | 145 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 97 | | matches | | 0 | "She stood on the grimy" | | 1 | "She tucked a curl of" | | 2 | "His eyes were open, fixed" | | 3 | "She looked nervous, but her" | | 4 | "He made a circle with" | | 5 | "She crouched by the body," | | 6 | "She looked at the overturned" | | 7 | "She stood and walked to" | | 8 | "It had been used recently." | | 9 | "She had ducked under the" | | 10 | "She didn’t touch it, just" | | 11 | "It had a faint, iridescent" | | 12 | "She gestured to the shards" | | 13 | "She leaned in close, ignoring" | | 14 | "She looked at his eyes" | | 15 | "She had never believed it." | | 16 | "She stood up, her sharp" | | 17 | "It was a targeted, precise" | | 18 | "They had convinced Silas to" | | 19 | "They had staged the robbery," |
| | ratio | 0.216 | |
| 27.01% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 97 | | matches | | 0 | "The damp chill of the" | | 1 | "She stood on the grimy" | | 2 | "The place was a tomb" | | 3 | "The stall’s velvet drapes were" | | 4 | "Shards of glass and strange," | | 5 | "This was the Veil Market," | | 6 | "Eva Kowalski stood just outside" | | 7 | "She tucked a curl of" | | 8 | "Harlow didn’t look at her," | | 9 | "The body lay half-under the" | | 10 | "His eyes were open, fixed" | | 11 | "Harlow finally glanced at her." | | 12 | "Eva’s freckles stood out against" | | 13 | "She looked nervous, but her" | | 14 | "A uniformed sergeant approached, his" | | 15 | "The sergeant consulted his notebook." | | 16 | "He made a circle with" | | 17 | "Harlow’s gaze drifted back to" | | 18 | "Harlow stepped under the tape." | | 19 | "The sergeant moved to stop" |
| | ratio | 0.866 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 46.05% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 3 | | matches | | 0 | "Harlow said, more to herself" | | 1 | "Eva corrected, her green eyes serious behind her glasses" | | 2 | "Harlow said, her tone leaving no room for debate" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "Eva corrected (correct)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 60 | | tagDensity | 0.183 | | leniency | 0.367 | | rawRatio | 0.182 | | effectiveRatio | 0.067 | |