| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1001 | | 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) | |
| 50.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1001 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "glistening" | | 1 | "flickered" | | 2 | "footsteps" | | 3 | "pulse" | | 4 | "navigated" | | 5 | "raced" | | 6 | "tracing" | | 7 | "weight" |
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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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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 23 | | totalWords | 994 | | ratio | 0.023 | | matches | | 0 | "The Raven’s Nest" | | 1 | "\"You said this would be safe.\"" | | 2 | "\"It is safe. For now.\"" | | 3 | "\"Detective Quinn. I was wondering when you’d find us.\"" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 974 | | uniqueNames | 10 | | maxNameDensity | 0.92 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Harlow | 9 | | Quinn | 1 | | Raven | 1 | | Elias | 1 | | Voss | 7 | | London | 1 | | Veil | 2 | | Market | 2 | | Silas | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Elias" | | 4 | "Voss" | | 5 | "Market" | | 6 | "Silas" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 994 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 79 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 38.23 | | std | 28.04 | | cv | 0.733 | | sampleLengths | | 0 | 88 | | 1 | 4 | | 2 | 82 | | 3 | 75 | | 4 | 5 | | 5 | 70 | | 6 | 44 | | 7 | 4 | | 8 | 50 | | 9 | 10 | | 10 | 84 | | 11 | 61 | | 12 | 68 | | 13 | 6 | | 14 | 45 | | 15 | 41 | | 16 | 33 | | 17 | 57 | | 18 | 15 | | 19 | 9 | | 20 | 20 | | 21 | 4 | | 22 | 57 | | 23 | 23 | | 24 | 27 | | 25 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 76 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 174 | | matches | | 0 | "was running" | | 1 | "was, standing" | | 2 | "was walking" | | 3 | "was walking" | | 4 | "was walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 79 | | ratio | 0.076 | | matches | | 0 | "She had been watching the bar for hours, waiting for the right moment—the one where the suspect, a man named Elias Voss, would slip out unnoticed." | | 1 | "One moment, she was running; the next, her boot caught on the edge, and she stumbled forward, arms windmilling as she teetered over the darkness." | | 2 | "She knew about the place—rumors had swirled through the force for years." | | 3 | "The air was thick with the scent of damp stone and something else—something metallic, like old blood." | | 4 | "She wasn’t here to fight—she was here to arrest." | | 5 | "She could hear the distant sound of footsteps, not hers, not Voss’s—something else." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 981 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.029561671763506627 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00815494393476045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 12.58 | | std | 6.17 | | cv | 0.49 | | sampleLengths | | 0 | 16 | | 1 | 26 | | 2 | 20 | | 3 | 26 | | 4 | 4 | | 5 | 21 | | 6 | 3 | | 7 | 17 | | 8 | 11 | | 9 | 12 | | 10 | 18 | | 11 | 23 | | 12 | 23 | | 13 | 18 | | 14 | 11 | | 15 | 5 | | 16 | 10 | | 17 | 25 | | 18 | 14 | | 19 | 18 | | 20 | 3 | | 21 | 3 | | 22 | 12 | | 23 | 14 | | 24 | 15 | | 25 | 4 | | 26 | 22 | | 27 | 3 | | 28 | 15 | | 29 | 10 | | 30 | 10 | | 31 | 17 | | 32 | 13 | | 33 | 17 | | 34 | 20 | | 35 | 17 | | 36 | 7 | | 37 | 12 | | 38 | 16 | | 39 | 9 | | 40 | 17 | | 41 | 21 | | 42 | 5 | | 43 | 17 | | 44 | 13 | | 45 | 12 | | 46 | 6 | | 47 | 21 | | 48 | 13 | | 49 | 11 |
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| 41.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.25316455696202533 | | totalSentences | 79 | | uniqueOpeners | 20 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 75 | | matches | | 0 | "Then, the pavement dropped away." | | 1 | "Then, she saw the hidden" | | 2 | "Then, the lantern flickered." |
| | ratio | 0.04 | |
| 54.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 75 | | matches | | 0 | "She had been watching the" | | 1 | "She had him now." | | 2 | "She followed, her leather watch" | | 3 | "She could hear him moving" | | 4 | "She had spent eighteen years" | | 5 | "She didn’t need to see" | | 6 | "She didn’t see the grate" | | 7 | "She barely caught herself, fingers" | | 8 | "Her pulse spiked." | | 9 | "She knew about the place—rumors" | | 10 | "She could see his silhouette" | | 11 | "She had no idea what" | | 12 | "She took a deep breath," | | 13 | "She landed in a crouch," | | 14 | "She saw Voss ahead, weaving" | | 15 | "She followed, her fingers brushing" | | 16 | "She wasn’t here to fight—she" | | 17 | "His eyes were sharp, assessing." | | 18 | "She met his gaze for" | | 19 | "She could hear the clink" |
| | ratio | 0.413 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 75 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The neon glow of *The" | | 3 | "She had been watching the" | | 4 | "She had him now." | | 5 | "A figure in a long" | | 6 | "Harlow didn’t hesitate." | | 7 | "She followed, her leather watch" | | 8 | "The alley was narrow, the" | | 9 | "She could hear him moving" | | 10 | "The streets of London at" | | 11 | "She had spent eighteen years" | | 12 | "The rain hammered against her" | | 13 | "She didn’t need to see" | | 14 | "She didn’t see the grate" | | 15 | "She barely caught herself, fingers" | | 16 | "The Veil Market." | | 17 | "Her pulse spiked." | | 18 | "She knew about the place—rumors" | | 19 | "A hidden market beneath the" |
| | ratio | 0.84 | |
| 66.67% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 75 | | matches | | 0 | "If she didn’t follow him" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "She could see his silhouette moving through the dim glow of flickering lanterns, his coat flaring as he navigated the underground tunnels." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |