| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 1 | | adverbTags | | 0 | "Tomás gestured around [around]" |
| | dialogueSentences | 51 | | tagDensity | 0.451 | | leniency | 0.902 | | rawRatio | 0.043 | | effectiveRatio | 0.039 | |
| 95.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1105 | | totalAiIsmAdverbs | 1 | | 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) | |
| 68.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1105 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "echoed" | | 1 | "silence" | | 2 | "shimmered" | | 3 | "charged" | | 4 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 143 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.08% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 807 | | uniqueNames | 15 | | maxNameDensity | 2.48 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | London | 2 | | Camden | 1 | | Harlow | 20 | | Tube | 1 | | Morris | 4 | | Silence | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Veil | 2 | | Market | 3 | | One | 2 | | Raven | 1 | | Nest | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Harlow" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Market" | | 7 | "One" | | 8 | "Raven" | | 9 | "Nest" |
| | places | | | globalScore | 0.261 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1109 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 143 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 24.11 | | std | 14.19 | | cv | 0.589 | | sampleLengths | | 0 | 42 | | 1 | 42 | | 2 | 10 | | 3 | 43 | | 4 | 50 | | 5 | 24 | | 6 | 10 | | 7 | 12 | | 8 | 15 | | 9 | 8 | | 10 | 36 | | 11 | 7 | | 12 | 19 | | 13 | 28 | | 14 | 20 | | 15 | 12 | | 16 | 44 | | 17 | 44 | | 18 | 16 | | 19 | 17 | | 20 | 8 | | 21 | 16 | | 22 | 20 | | 23 | 20 | | 24 | 25 | | 25 | 10 | | 26 | 44 | | 27 | 15 | | 28 | 38 | | 29 | 6 | | 30 | 52 | | 31 | 42 | | 32 | 49 | | 33 | 21 | | 34 | 21 | | 35 | 7 | | 36 | 13 | | 37 | 27 | | 38 | 4 | | 39 | 20 | | 40 | 46 | | 41 | 17 | | 42 | 15 | | 43 | 19 | | 44 | 41 | | 45 | 14 |
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| 99.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 115 | | matches | | 0 | "was outnumbered" | | 1 | "was committed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 155 | | matches | | |
| 62.94% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 143 | | ratio | 0.028 | | matches | | 0 | "Three blocks later, she spotted movement - a shadow slipping down a flight of stairs marked \"Staff Only.\"" | | 1 | "\"Welcome to the Veil Market,\" the figure said, now fully visible - a young man with olive skin and short curly dark brown hair." | | 2 | "Rain from the surface seeped through cracks in the ceiling, mixing with the smell of ozone and something else - blood." | | 3 | "Behind them, the entrance to the market shimmered - the only way out." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 806 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.028535980148883373 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009925558312655087 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 143 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 143 | | mean | 7.76 | | std | 4.31 | | cv | 0.556 | | sampleLengths | | 0 | 6 | | 1 | 12 | | 2 | 15 | | 3 | 2 | | 4 | 7 | | 5 | 5 | | 6 | 5 | | 7 | 1 | | 8 | 8 | | 9 | 13 | | 10 | 10 | | 11 | 8 | | 12 | 2 | | 13 | 9 | | 14 | 9 | | 15 | 7 | | 16 | 18 | | 17 | 2 | | 18 | 8 | | 19 | 4 | | 20 | 5 | | 21 | 14 | | 22 | 5 | | 23 | 5 | | 24 | 7 | | 25 | 9 | | 26 | 5 | | 27 | 10 | | 28 | 5 | | 29 | 5 | | 30 | 6 | | 31 | 6 | | 32 | 15 | | 33 | 1 | | 34 | 3 | | 35 | 4 | | 36 | 11 | | 37 | 6 | | 38 | 8 | | 39 | 11 | | 40 | 7 | | 41 | 13 | | 42 | 6 | | 43 | 9 | | 44 | 10 | | 45 | 1 | | 46 | 8 | | 47 | 14 | | 48 | 6 | | 49 | 12 |
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| 63.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.43356643356643354 | | totalSentences | 143 | | uniqueOpeners | 62 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 104 | | matches | | 0 | "Her worn leather watch clicked" | | 1 | "She kicked the door in." | | 2 | "she muttered to herself" | | 3 | "She moved with military precision," | | 4 | "Her partner Morris had told" | | 5 | "He'd known the hidden places." | | 6 | "It hummed with energy, smelling" | | 7 | "She recognized the scent from" | | 8 | "Their shadows elongated across the" | | 9 | "She fired a warning shot." | | 10 | "They were something else entirely." | | 11 | "She needed that bone token." | | 12 | "Her eyes darted around, searching" | | 13 | "She didn't belong here." | | 14 | "Their shadows merged, creating a" |
| | ratio | 0.144 | |
| 46.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 104 | | matches | | 0 | "The alley's rear door slammed" | | 1 | "Harlow Quinn pressed her shoulder" | | 2 | "Her worn leather watch clicked" | | 3 | "Nothing this side of London" | | 4 | "She kicked the door in." | | 5 | "Wood splintered across the floor." | | 6 | "The air hung thick with" | | 7 | "Water dripped from her sharp" | | 8 | "The suspect had vanished into" | | 9 | "she muttered to herself" | | 10 | "She moved with military precision," | | 11 | "The rain plastered her salt-and-pepper" | | 12 | "The city lights blurred through" | | 13 | "The stairs led to an" | | 14 | "Her partner Morris had told" | | 15 | "He'd known the hidden places." | | 16 | "The question surfaced like a" | | 17 | "The station smelled of damp" | | 18 | "The suspect stood at the" | | 19 | "Harlow called out" |
| | ratio | 0.827 | |
| 48.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 104 | | matches | | 0 | "Either way, she was committed" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "Stalls lined the cavernous space, displaying strange goods: glowing potions, exotic herbs, and artifacts that hummed with power." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow said, turning to face the creature" |
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| 12.75% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 7 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Harlow repeated (repeat)" | | 2 | "Tomás advised (advise)" | | 3 | "she warned (warn)" | | 4 | "a voice whispered (whisper)" | | 5 | "Harlow challenged (challenge)" | | 6 | "the lead enforcer hissed (hiss)" |
| | dialogueSentences | 51 | | tagDensity | 0.275 | | leniency | 0.549 | | rawRatio | 0.5 | | effectiveRatio | 0.275 | |