| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.387 | | leniency | 0.774 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1417 | | totalAiIsmAdverbs | 3 | | 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) | |
| 64.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1417 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "complex" | | 1 | "weight" | | 2 | "reminder" | | 3 | "scanned" | | 4 | "rhythmic" | | 5 | "pulsed" | | 6 | "echoed" | | 7 | "whisper" | | 8 | "silence" | | 9 | "firmly" |
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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 | 130 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 130 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 11 | | totalWords | 1411 | | ratio | 0.008 | | matches | | 0 | "The Raven's Nest" | | 1 | "Click, clack, scrape." | | 2 | "Staff Only" | | 3 | "The Raven's Nest" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.56% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1194 | | uniqueNames | 14 | | maxNameDensity | 1.09 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Raven | 2 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Morris | 1 | | Tommy | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Tommy" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" |
| | places | | | globalScore | 0.956 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | 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 | 1411 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 23.52 | | std | 16.61 | | cv | 0.707 | | sampleLengths | | 0 | 34 | | 1 | 34 | | 2 | 11 | | 3 | 20 | | 4 | 45 | | 5 | 47 | | 6 | 4 | | 7 | 14 | | 8 | 1 | | 9 | 39 | | 10 | 19 | | 11 | 37 | | 12 | 13 | | 13 | 32 | | 14 | 4 | | 15 | 30 | | 16 | 1 | | 17 | 14 | | 18 | 21 | | 19 | 7 | | 20 | 27 | | 21 | 33 | | 22 | 25 | | 23 | 36 | | 24 | 64 | | 25 | 13 | | 26 | 33 | | 27 | 41 | | 28 | 31 | | 29 | 26 | | 30 | 42 | | 31 | 6 | | 32 | 54 | | 33 | 20 | | 34 | 19 | | 35 | 66 | | 36 | 5 | | 37 | 1 | | 38 | 33 | | 39 | 20 | | 40 | 11 | | 41 | 21 | | 42 | 2 | | 43 | 49 | | 44 | 14 | | 45 | 4 | | 46 | 13 | | 47 | 15 | | 48 | 4 | | 49 | 15 |
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| 97.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 130 | | matches | | 0 | "was padlocked" | | 1 | "was gone" | | 2 | "been swallowed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 204 | | matches | | 0 | "was flickering" | | 1 | "was waving" | | 2 | "was running" |
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| 85.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 149 | | ratio | 0.02 | | matches | | 0 | "She gripped his forearm—muscular, tense." | | 1 | "Legend had it people sold things down there—enchantments, poisons, favors for the right price." | | 2 | "Hundreds of figures—predominantly human, but with the occasional hooded cloak—huddled under flickering gas lamps." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1200 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009166666666666667 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 9.47 | | std | 6.08 | | cv | 0.642 | | sampleLengths | | 0 | 13 | | 1 | 5 | | 2 | 16 | | 3 | 18 | | 4 | 4 | | 5 | 12 | | 6 | 11 | | 7 | 4 | | 8 | 5 | | 9 | 11 | | 10 | 2 | | 11 | 9 | | 12 | 18 | | 13 | 4 | | 14 | 2 | | 15 | 10 | | 16 | 12 | | 17 | 13 | | 18 | 7 | | 19 | 5 | | 20 | 10 | | 21 | 4 | | 22 | 3 | | 23 | 11 | | 24 | 1 | | 25 | 6 | | 26 | 2 | | 27 | 13 | | 28 | 9 | | 29 | 9 | | 30 | 11 | | 31 | 8 | | 32 | 5 | | 33 | 2 | | 34 | 4 | | 35 | 8 | | 36 | 18 | | 37 | 8 | | 38 | 5 | | 39 | 8 | | 40 | 13 | | 41 | 11 | | 42 | 1 | | 43 | 3 | | 44 | 7 | | 45 | 12 | | 46 | 11 | | 47 | 1 | | 48 | 10 | | 49 | 4 |
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| 56.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3783783783783784 | | totalSentences | 148 | | uniqueOpeners | 56 | |
| 28.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 118 | | matches | | 0 | "Just the rhythmic drum of" |
| | ratio | 0.008 | |
| 50.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 118 | | matches | | 0 | "Her boots splashed over a" | | 1 | "She didn’t look up." | | 2 | "Her focus was the figure" | | 3 | "she yelled, her voice cracking" | | 4 | "He didn't even glance back." | | 5 | "He pivoted left, cutting toward" | | 6 | "Her lungs burned, a cold" | | 7 | "She checked her wrist." | | 8 | "She grabbed the lapel of" | | 9 | "She gripped his forearm—muscular, tense." | | 10 | "He pulled away, his boot" | | 11 | "She lunged again." | | 12 | "They crashed into a chain-link" | | 13 | "She grunted, the air leaving" | | 14 | "Her back hit the concrete" | | 15 | "He scrambled up, hauling himself" | | 16 | "He had the advantage." | | 17 | "He reached the end, disappearing" | | 18 | "She peered around the bend." | | 19 | "He was gone." |
| | ratio | 0.424 | |
| 19.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 104 | | totalSentences | 118 | | matches | | 0 | "Rain slicked the asphalt, turning" | | 1 | "Detective Harlow Quinn didn’t care." | | 2 | "Her boots splashed over a" | | 3 | "Water sluiced off the green" | | 4 | "She didn’t look up." | | 5 | "Her focus was the figure" | | 6 | "she yelled, her voice cracking" | | 7 | "The suspect didn't pause." | | 8 | "He didn't even glance back." | | 9 | "He pivoted left, cutting toward" | | 10 | "Her lungs burned, a cold" | | 11 | "The weight of her service" | | 12 | "She checked her wrist." | | 13 | "She grabbed the lapel of" | | 14 | "The man stiffened, his arm" | | 15 | "Quinn sidestepped, falling into a" | | 16 | "She gripped his forearm—muscular, tense." | | 17 | "He pulled away, his boot" | | 18 | "She lunged again." | | 19 | "This time her fingers hooked" |
| | ratio | 0.881 | |
| 84.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 118 | | matches | | 0 | "If she went down there," | | 1 | "If she didn't go in" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "Cobwebs draped the shelves that lined the damp walls, filled with jars of glowing liquid, vials of crushed herbs, and jagged stones that pulsed with faint light…" |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "she yelled, her voice cracking slightly before snapping into command" | | 1 | "he countered, his voice dropping an octave" |
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| 20.97% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 4 | | fancyTags | | 0 | "she yelled (yell)" | | 1 | "She snapped (snap)" | | 2 | "Tomás explained (explain)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 31 | | tagDensity | 0.258 | | leniency | 0.516 | | rawRatio | 0.5 | | effectiveRatio | 0.258 | |